From 02e8f7abec8f69bdec0b66e805e1e569f320fccd Mon Sep 17 00:00:00 2001 From: Alexandros Ladas Date: Fri, 28 Nov 2025 21:34:05 +0100 Subject: [PATCH 1/4] Initial Snowflake integration --- .github/workflows/snowflake-test.yml | 56 ++ .gitignore | 7 +- integration/snowflake/bootstrap.py | 69 ++ integration/snowflake/data/__init__.py | 33 + integration/snowflake/data/_sql_loader.py | 34 + integration/snowflake/data/ingestion.py | 148 +++ integration/snowflake/data/preparation.py | 293 ++++++ .../data/sql/bootstrap/create_database.sql | 2 + .../data/sql/bootstrap/create_warehouse.sql | 7 + .../sql/ingestion/create_external_stage.sql | 9 + .../data/sql/ingestion/create_raw_schema.sql | 2 + .../data/sql/preparation/analyze_stores.sql | 13 + .../data/sql/preparation/count_snapshots.sql | 2 + .../data/sql/preparation/count_stores.sql | 2 + .../create_population_weekly_by_store.sql | 56 ++ .../create_population_with_target.sql | 23 + .../preparation/create_prepared_schema.sql | 2 + .../sql/preparation/data_quality_check.sql | 7 + .../drop_population_weekly_by_store.sql | 2 + .../drop_population_with_target.sql | 2 + .../sql/preparation/overall_statistics.sql | 8 + .../preparation/recent_snapshots_by_store.sql | 11 + .../sql/preparation/sample_data_by_store.sql | 11 + .../sql/preparation/snapshots_per_store.sql | 9 + .../data/sql/preparation/store_statistics.sql | 12 + .../preparation/zero_sales_investigation.sql | 6 + .../GETML_WEEKLY_SALES_DATA_PREPARATION.md | 116 +++ .../snowflake/docs/python-testing-guide.md | 456 +++++++++ integration/snowflake/mise.toml | 9 + integration/snowflake/pyproject.toml | 130 +++ integration/snowflake/settings.py | 61 ++ integration/snowflake/snowflake_session.py | 85 ++ integration/snowflake/tests/__init__.py | 1 + .../snowflake/tests/cleanup_test_schemas.py | 86 ++ integration/snowflake/tests/conftest.py | 53 ++ .../snowflake/tests/integration/__init__.py | 1 + .../snowflake/tests/integration/conftest.py | 126 +++ .../tests/integration/test_pipeline.py | 177 ++++ integration/snowflake/tests/test_data_init.py | 53 ++ integration/snowflake/tests/test_ingestion.py | 135 +++ .../snowflake/tests/test_preparation.py | 57 ++ integration/snowflake/uv.lock | 900 ++++++++++++++++++ 42 files changed, 3271 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/snowflake-test.yml create mode 100644 integration/snowflake/bootstrap.py create mode 100644 integration/snowflake/data/__init__.py create mode 100644 integration/snowflake/data/_sql_loader.py create mode 100644 integration/snowflake/data/ingestion.py create mode 100644 integration/snowflake/data/preparation.py create mode 100644 integration/snowflake/data/sql/bootstrap/create_database.sql create mode 100644 integration/snowflake/data/sql/bootstrap/create_warehouse.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_external_stage.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_raw_schema.sql create mode 100644 integration/snowflake/data/sql/preparation/analyze_stores.sql create mode 100644 integration/snowflake/data/sql/preparation/count_snapshots.sql create mode 100644 integration/snowflake/data/sql/preparation/count_stores.sql create mode 100644 integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql create mode 100644 integration/snowflake/data/sql/preparation/create_population_with_target.sql create mode 100644 integration/snowflake/data/sql/preparation/create_prepared_schema.sql create mode 100644 integration/snowflake/data/sql/preparation/data_quality_check.sql create mode 100644 integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql create mode 100644 integration/snowflake/data/sql/preparation/drop_population_with_target.sql create mode 100644 integration/snowflake/data/sql/preparation/overall_statistics.sql create mode 100644 integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql create mode 100644 integration/snowflake/data/sql/preparation/sample_data_by_store.sql create mode 100644 integration/snowflake/data/sql/preparation/snapshots_per_store.sql create mode 100644 integration/snowflake/data/sql/preparation/store_statistics.sql create mode 100644 integration/snowflake/data/sql/preparation/zero_sales_investigation.sql create mode 100644 integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md create mode 100644 integration/snowflake/docs/python-testing-guide.md create mode 100644 integration/snowflake/mise.toml create mode 100644 integration/snowflake/pyproject.toml create mode 100644 integration/snowflake/settings.py create mode 100644 integration/snowflake/snowflake_session.py create mode 100644 integration/snowflake/tests/__init__.py create mode 100644 integration/snowflake/tests/cleanup_test_schemas.py create mode 100644 integration/snowflake/tests/conftest.py create mode 100644 integration/snowflake/tests/integration/__init__.py create mode 100644 integration/snowflake/tests/integration/conftest.py create mode 100644 integration/snowflake/tests/integration/test_pipeline.py create mode 100644 integration/snowflake/tests/test_data_init.py create mode 100644 integration/snowflake/tests/test_ingestion.py create mode 100644 integration/snowflake/tests/test_preparation.py create mode 100644 integration/snowflake/uv.lock diff --git a/.github/workflows/snowflake-test.yml b/.github/workflows/snowflake-test.yml new file mode 100644 index 0000000..51a7541 --- /dev/null +++ b/.github/workflows/snowflake-test.yml @@ -0,0 +1,56 @@ +name: Python Linting, Formatting, Testing, Coverage +on: + workflow_dispatch: + pull_request: + branches: [master, 42-build-data-preparation-infrastructure-for-feature-store-notebooks] + paths: + - "integration/snowflake/**" +jobs: + test: + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./integration/snowflake + strategy: + matrix: + python-version: ["3.12", "3.13"] + fail-fast: false + steps: + - uses: actions/checkout@v4 + with: + lfs: true + - name: Set up Python ${{ matrix.python-version }} and uv + uses: astral-sh/setup-uv@v5 + with: + python-version: ${{ matrix.python-version }} + enable-cache: true + cache-dependency-glob: | + **/pyproject.toml + - name: Install dependencies + run: | + uv sync \ + --python ${{ matrix.python-version }} \ + --group dev + - name: Check linting and formatting + run: | + uv run --active ruff check . + uv run --active ruff format --check . + - name: Check types + run: | + uv run --active basedpyright + - name: Show TODOs + run: | + uv run --active ruff check --select FIX . || true + - name: Run tests and coverage + env: + SNOWFLAKE_ACCOUNT: ${{ secrets.SNOWFLAKE_ACCOUNT }} + SNOWFLAKE_USER: ${{ secrets.SNOWFLAKE_USER }} + SNOWFLAKE_PASSWORD: ${{ secrets.SNOWFLAKE_PASSWORD }} + SNOWFLAKE_ROLE: ${{ secrets.SNOWFLAKE_ROLE }} + SNOWFLAKE_WAREHOUSE: ${{ secrets.SNOWFLAKE_WAREHOUSE }} + SNOWFLAKE_DATABASE: ${{ secrets.SNOWFLAKE_DATABASE }} + run: |- + uv run --active pytest \ + --cov=. \ + --cov-report=term \ + -v diff --git a/.gitignore b/.gitignore index 0fda688..b75efb2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,7 @@ **/.DS_Store # Temp -preparation/ +/preparation/ # Binder .bash_logout @@ -152,3 +152,8 @@ dmypy.json *_spark/ *_pipeline/ .vscode/ + +# LLM instructions + +copilot-instructions.md +CLAUDE.md \ No newline at end of file diff --git a/integration/snowflake/bootstrap.py b/integration/snowflake/bootstrap.py new file mode 100644 index 0000000..0960dba --- /dev/null +++ b/integration/snowflake/bootstrap.py @@ -0,0 +1,69 @@ +"""Bootstrap Snowflake infrastructure for the Jaffle Shop dataset. + +Creates the required warehouse and database if they don't exist. +All operations are idempotent using CREATE ... IF NOT EXISTS. + +Usage example: + from settings import SnowflakeAdminSettings, SnowflakeSettings + from snowflake_session import create_admin_session + from bootstrap import bootstrap_snowflake_infrastructure + + admin_settings = SnowflakeAdminSettings.from_env() + full_settings = SnowflakeSettings.from_env() + + with create_admin_session(admin_settings) as session: + bootstrap_snowflake_infrastructure(session, full_settings) +""" + +# ruff: noqa: G004 + +import logging + +from snowflake.snowpark import Session + +from data import load_sql +from settings import SnowflakeSettings + +logger: logging.Logger = logging.getLogger(__name__) + +WAREHOUSE_SIZE = "X-SMALL" +AUTO_SUSPEND_SECONDS = 60 + + +def bootstrap_snowflake_infrastructure( + session: Session, + settings: SnowflakeSettings, +) -> None: + """Create Snowflake warehouse and database if they don't exist. + + Raises: + Exception: If warehouse or database creation fails. + """ + _create_warehouse(session, settings.warehouse) + _create_database(session, settings.database) + logger.info("Snowflake infrastructure bootstrap complete") + + +def _create_warehouse(session: Session, warehouse_name: str) -> None: + """Create warehouse with X-SMALL size and auto-suspend enabled.""" + logger.info(f"Creating warehouse '{warehouse_name}' if not exists...") + + sql = load_sql( + "bootstrap/create_warehouse.sql", + warehouse_name=warehouse_name, + warehouse_size=WAREHOUSE_SIZE, + auto_suspend_seconds=str(AUTO_SUSPEND_SECONDS), + ) + _ = session.sql(sql).collect() + + logger.info(f"✓ Warehouse '{warehouse_name}' ready") + + +def _create_database(session: Session, database_name: str) -> None: + """Create database if it doesn't exist.""" + logger.info(f"Creating database '{database_name}' if not exists...") + + sql = load_sql("bootstrap/create_database.sql", database_name=database_name) + _ = session.sql(sql).collect() + + logger.info(f"✓ Database '{database_name}' ready") diff --git a/integration/snowflake/data/__init__.py b/integration/snowflake/data/__init__.py new file mode 100644 index 0000000..0d64c40 --- /dev/null +++ b/integration/snowflake/data/__init__.py @@ -0,0 +1,33 @@ +"""Data operations for getML Feature Store integration with Snowflake. + +Usage example: + from settings import SnowflakeSettings + from snowflake_session import create_session + from data import load_jaffle_shop_data, prepare_weekly_sales_by_store + + # Settings auto-load from SNOWFLAKE_* environment variables + settings = SnowflakeSettings.from_env() + + with create_session(settings) as session: + load_jaffle_shop_data(session) + prepare_weekly_sales_by_store(session) +""" + +from ._sql_loader import load_sql +from .ingestion import ( + DataIngestionError, + load_jaffle_shop_data, + load_single_table, + rollback_transaction, +) +from .preparation import prepare_weekly_sales_by_store, validate_raw_schema + +__all__ = [ + "DataIngestionError", + "load_jaffle_shop_data", + "load_single_table", + "load_sql", + "prepare_weekly_sales_by_store", + "rollback_transaction", + "validate_raw_schema", +] diff --git a/integration/snowflake/data/_sql_loader.py b/integration/snowflake/data/_sql_loader.py new file mode 100644 index 0000000..3ef67eb --- /dev/null +++ b/integration/snowflake/data/_sql_loader.py @@ -0,0 +1,34 @@ +"""Internal SQL file loading utilities. + +This module provides utilities for loading and formatting SQL queries +from the sql/ directory. It is intended for internal use within the +data package only. +""" + +from pathlib import Path + +_SQL_DIR = Path(__file__).parent / "sql" + + +def load_sql(path: str, **kwargs: str) -> str: + """Load SQL query from file with optional parameter substitution. + + Loads SQL content from data/sql/{path} and applies string formatting + if keyword arguments are provided. + + Args: + path: Relative path to SQL file within data/sql/ directory + (e.g., "preparation/analyze_stores.sql"). + **kwargs: Named parameters to substitute in the SQL using .format(). + + Raises: + FileNotFoundError: If the SQL file does not exist. + KeyError: If a placeholder in the SQL file is not provided in kwargs. + """ + sql_file: Path = _SQL_DIR / path + content: str = sql_file.read_text() + + if kwargs: + return content.format(**kwargs) + + return content diff --git a/integration/snowflake/data/ingestion.py b/integration/snowflake/data/ingestion.py new file mode 100644 index 0000000..8e2667b --- /dev/null +++ b/integration/snowflake/data/ingestion.py @@ -0,0 +1,148 @@ +"""Data ingestion from S3 to Snowflake for the Jaffle Shop dataset. + +This module loads CSV data from a public S3 bucket into Snowflake's RAW schema +using native COPY INTO commands with external staging. + +Usage example: + from settings import SnowflakeSettings + from snowflake_session import create_session + from data import load_jaffle_shop_data + + with create_session(SnowflakeSettings.from_env()) as session: + results = load_jaffle_shop_data(session) +""" + +# ruff: noqa: G004, S608 + +import logging + +from snowflake.snowpark import Row, Session + +from ._sql_loader import load_sql + +logger: logging.Logger = logging.getLogger(__name__) + +JAFFLE_SHOP_TABLES: dict[str, str] = { + "raw_customers": "(id TEXT, name TEXT)", + "raw_orders": ( + "(id TEXT, customer TEXT, ordered_at TIMESTAMP, store_id TEXT, " + "subtotal INTEGER, tax_paid INTEGER, order_total INTEGER)" + ), + "raw_order_items": "(id TEXT, order_id TEXT, sku TEXT)", + "raw_products": "(sku TEXT, name TEXT, type TEXT, price INTEGER, description TEXT)", + "raw_stores": "(id TEXT, name TEXT, opened_at TIMESTAMP, tax_rate FLOAT)", + "raw_supplies": "(id TEXT, name TEXT, cost INTEGER, perishable BOOLEAN, sku TEXT)", +} + +S3_BUCKET_URL = "s3://dbt-tutorial-public/long_term_dataset" +STAGE_NAME = "JAFFLE_SHOP_S3_STAGE" + + +def load_jaffle_shop_data(session: Session) -> dict[str, int]: + """Load Jaffle Shop data from S3 into Snowflake RAW schema. + + Creates the RAW schema if needed, sets up an external stage pointing to + the public S3 bucket, and loads all tables using COPY INTO commands. + All operations are wrapped in a transaction that rolls back on failure. + + Raises: + DataIngestionError: If schema creation, stage setup, or data loading fails. + """ + try: + _ = session.sql("BEGIN").collect() + _create_raw_schema(session) + _create_external_stage(session) + results = _load_all_tables(session) + _ = session.sql("COMMIT").collect() + except Exception as e: + rollback_transaction(session) + raise DataIngestionError( + f"Failed to load Jaffle Shop data into Snowflake. Error: {e}" + ) from e + + _log_completion_summary(results) + return results + + +class DataIngestionError(Exception): + """Raised when data ingestion fails.""" + + +def _create_raw_schema(session: Session) -> None: + """Create RAW schema if it doesn't exist.""" + logger.info("Creating RAW schema if not exists...") + _ = session.sql(load_sql("ingestion/create_raw_schema.sql")).collect() + logger.info("✓ RAW schema ready") + + +def _create_external_stage(session: Session) -> None: + """Create external stage pointing to S3 bucket.""" + logger.info(f"Creating external stage {STAGE_NAME}...") + sql = load_sql( + "ingestion/create_external_stage.sql", + stage_name=STAGE_NAME, + bucket_url=S3_BUCKET_URL, + ) + _ = session.sql(sql).collect() + logger.info(f"✓ External stage {STAGE_NAME} created") + + +def _load_all_tables(session: Session) -> dict[str, int]: + """Load all Jaffle Shop tables from S3.""" + logger.info("Loading tables from S3...") + results: dict[str, int] = {} + + for table_name, schema_def in JAFFLE_SHOP_TABLES.items(): + row_count = load_single_table(session, table_name, schema_def) + results[table_name] = row_count + + return results + + +def load_single_table(session: Session, table_name: str, schema_def: str) -> int: + """Load a single table from S3 and return row count.""" + logger.info(f"Loading {table_name}...") + + _ = session.sql(f""" + CREATE OR REPLACE TABLE RAW.{table_name} {schema_def} + """).collect() + + copy_result: list[Row] = session.sql(f""" + COPY INTO RAW.{table_name} + FROM @RAW.{STAGE_NAME}/{table_name}.csv + FORCE = FALSE + """).collect() + + count_result: list[Row] = session.sql(f""" + SELECT COUNT(*) as count FROM RAW.{table_name} + """).collect() + + # TODO: Research proper typing for Snowpark Row objects + row_count = int(count_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] + rows_copied = int(copy_result[0]["rows_parsed"]) if copy_result else 0 # pyright: ignore[reportArgumentType] + + logger.info(f" ✓ {rows_copied:,} rows copied, {row_count:,} total rows in table") + + if row_count == 0: + raise DataIngestionError(f"Table {table_name} has 0 rows after loading") + + return row_count + + +def _log_completion_summary(results: dict[str, int]) -> None: + """Log summary of loaded tables.""" + logger.info("=" * 80) + logger.info("DATA LOADING COMPLETE!") + logger.info("=" * 80) + logger.info(f"Successfully loaded {len(results)} tables into RAW schema:") + for table_name, count in results.items(): + logger.info(f" • {table_name}: {count:,} rows") + + +def rollback_transaction(session: Session) -> None: + """Attempt to rollback the current transaction.""" + try: + _ = session.sql("ROLLBACK").collect() + logger.warning("Transaction rolled back due to error") + except Exception: # noqa: BLE001 + logger.debug("Rollback failed - transaction may not have been active") diff --git a/integration/snowflake/data/preparation.py b/integration/snowflake/data/preparation.py new file mode 100644 index 0000000..8a14bba --- /dev/null +++ b/integration/snowflake/data/preparation.py @@ -0,0 +1,293 @@ +"""Prepare weekly sales forecasting data for getML - BY STORE. + +This script creates a population table with weekly snapshots (Sunday nights) +per store and calculates the target: total sales for the following week. + +Only includes full weeks where the store was open for the entire week. + +SQL queries are externalized in the sql/ directory for better maintainability. +""" + +# ruff: noqa: G004, G003, S608, TRY301, E501 + +import logging +from typing import cast + +from snowflake.snowpark import Row, Session + +from ._sql_loader import load_sql + +logger: logging.Logger = logging.getLogger(__name__) + + +def validate_raw_schema(session: Session) -> None: + """Validate that RAW schema exists and contains required tables.""" + required_tables: list[str] = ["raw_stores", "raw_orders"] + logger.info("Validating RAW schema and required tables...") + + try: + for table_name in required_tables: + result: list[Row] = session.sql( + f"SELECT COUNT(*) as count FROM RAW.{table_name} LIMIT 1" + ).collect() + if not result: + msg: str = ( + f"Table RAW.{table_name} does not exist or is not accessible. " + "Please run ingestion.load_jaffle_shop_data() first." + ) + raise ValueError(msg) + except Exception as e: + msg = f"Failed to validate RAW schema. Ensure data has been loaded. Error: {e}" + raise ValueError(msg) from e + + logger.info("✓ RAW schema validated") + + +def _ensure_prepared_schema(session: Session) -> None: + """Create PREPARED schema if it doesn't exist.""" + logger.info("Creating PREPARED schema if not exists...") + _ = session.sql(load_sql("preparation/create_prepared_schema.sql")).collect() + logger.info("✓ PREPARED schema ready") + + +def _analyze_and_display_stores(session: Session) -> None: + """Analyze and display store activity.""" + logger.info("\n1. Analyzing store data...") + stores: list[Row] = session.sql( + load_sql("preparation/analyze_stores.sql") + ).collect() + + logger.info(f"\n{'Store':<30} {'Opened':<20} {'Orders':<12} {'Total Sales':<15}") + logger.info("-" * 80) + for store in stores: + logger.info( + f"{store[1]:<30} {store[2]!s:<20} {store[5]:<12,} ${store[6]:<14,.2f}" + ) + + +def _create_weekly_population_table(session: Session) -> list[Row]: + """Create population table with Sunday night snapshots per store. + + Returns: + List of store information rows. + """ + logger.info( + "\n2. Creating weekly population table per store (Sunday night snapshots)..." + ) + logger.info(" Only including FULL weeks (store open entire week)...") + + _ = session.sql( + load_sql("preparation/drop_population_weekly_by_store.sql") + ).collect() + _ = session.sql( + load_sql("preparation/create_population_weekly_by_store.sql") + ).collect() + + num_snapshots: int = cast( + "int", session.sql(load_sql("preparation/count_snapshots.sql")).collect()[0][0] + ) + num_stores: int = cast( + "int", session.sql(load_sql("preparation/count_stores.sql")).collect()[0][0] + ) + logger.info( + f" Created {num_snapshots:,} weekly snapshots across {num_stores} stores" + ) + + # Show snapshots per store + per_store: list[Row] = session.sql( + load_sql("preparation/snapshots_per_store.sql") + ).collect() + + logger.info(f"\n {'Store':<30} {'Snapshots':<12} {'First':<20} {'Last':<20}") + logger.info(" " + "-" * 80) + for row in per_store: + logger.info(f" {row[0]:<30} {row[1]:<12,} {row[2]!s:<20} {row[3]!s:<20}") + + return per_store + + +def _add_target_column(session: Session) -> None: + """Calculate target - total sales for the following week per store.""" + logger.info("\n3. Calculating target: next week's total sales per store...") + _ = session.sql(load_sql("preparation/drop_population_with_target.sql")).collect() + _ = session.sql(load_sql("preparation/create_population_with_target.sql")).collect() + + +def _display_sample_data(session: Session, per_store: list[Row]) -> None: + """Show sample data for first few stores.""" + logger.info("\n4. Sample data - first 5 snapshots per store:") + logger.info("-" * 120) + + for store_info in per_store[:3]: # Show first 3 stores + store_name: str = cast("str", store_info[0]) + logger.info(f"\n{store_name}:") + sample_query = load_sql( + "preparation/sample_data_by_store.sql", store_name=store_name + ) + samples: list[Row] = session.sql(sample_query).collect() + + logger.info( + f"{'ID':<8} {'Snapshot (Sunday)':<20} {'Predict Week':<20} " + f"{'Sales $':<15} {'Orders':<10}" + ) + logger.info("-" * 80) + for row in samples: + logger.info( + f"{row[0]:<8} {row[1]!s:<20} {row[2]!s:<20} " + f"${row[3]:<14,.2f} {row[4]:<10,}" + ) + + +def _display_store_statistics(session: Session) -> None: + """Display target statistics per store.""" + logger.info("\n5. Target statistics per store:") + logger.info("-" * 120) + + store_stats: list[Row] = session.sql( + load_sql("preparation/store_statistics.sql") + ).collect() + + logger.info( + f"{'Store':<30} {'Snapshots':<12} {'Avg Week $':<15} " + f"{'Min $':<15} {'Max $':<15} {'Std Dev $':<15}" + ) + logger.info("-" * 120) + for row in store_stats: + logger.info( + f"{row[0]:<30} {row[1]:<12,} ${row[2]:<14,.2f} " + f"${row[3]:<14,.2f} ${row[4]:<14,.2f} ${row[5]:<14,.2f}" + ) + + +def _display_overall_statistics(session: Session) -> None: + """Display overall dataset statistics.""" + logger.info("\n6. Overall statistics:") + logger.info("-" * 80) + + overall: Row = session.sql( + load_sql("preparation/overall_statistics.sql") + ).collect()[0] + + logger.info(f" Total snapshots: {overall[0]:,}") + logger.info(f" Number of stores: {overall[1]:,}") + logger.info(f" Average weekly sales per store: ${overall[2]:,.2f}") + logger.info(f" Total sales in dataset: ${overall[3]:,.2f}") + logger.info(f" Total orders in dataset: {overall[4]:,}") + + +def _perform_data_quality_check(session: Session) -> None: + """Perform and display data quality checks.""" + logger.info("\n7. Data quality check:") + logger.info("-" * 80) + + quality: Row = session.sql( + load_sql("preparation/data_quality_check.sql") + ).collect()[0] + + logger.info(f" Total snapshots: {quality[0]:,}") + logger.info(f" Weeks with zero sales: {quality[1]:,}") + logger.info(f" Weeks with zero orders: {quality[2]:,}") + logger.info(f" Invalid records (sales but no orders): {quality[3]:,}") + + zero_sales_count: int = cast("int", quality[1]) + if zero_sales_count > 0: + logger.warning( + f"\n WARNING: {zero_sales_count} snapshots have zero sales. Investigating..." + ) + zero_sales: list[Row] = session.sql( + load_sql("preparation/zero_sales_investigation.sql") + ).collect() + for row in zero_sales: + logger.warning(f" {row[0]}: {row[1]} weeks with zero sales") + + +def _display_recent_snapshots(session: Session, per_store: list[Row]) -> None: + """Show most recent snapshots for first few stores.""" + logger.info("\n8. Most recent snapshots (last 3 per store):") + logger.info("-" * 120) + + for store_info in per_store[:3]: # Show first 3 stores + store_name: str = cast("str", store_info[0]) + logger.info(f"\n{store_name}:") + recent_query = load_sql( + "preparation/recent_snapshots_by_store.sql", store_name=store_name + ) + recent: list[Row] = session.sql(recent_query).collect() + + logger.info( + f"{'ID':<8} {'Snapshot (Sunday)':<20} {'Predict Week':<20} " + f"{'Sales $':<15} {'Orders':<10}" + ) + logger.info("-" * 80) + for row in recent: + logger.info( + f"{row[0]:<8} {row[1]!s:<20} {row[2]!s:<20} " + f"${row[3]:<14,.2f} {row[4]:<10,}" + ) + + +def prepare_weekly_sales_by_store(session: Session) -> None: + """Prepare data for getML to predict next week's sales from Sunday night, grouped by store. + + Creates: + - population_weekly_by_store: Weekly snapshots per store at Sunday 23:59:59 + - population_weekly_by_store_with_target: Population table with target column + - Target: Sum of order_total for the following 7 days for that store + - Only includes complete weeks (store was open entire week) + + Args: + session: Active Snowflake Snowpark session + """ + # Setup and validation + validate_raw_schema(session) + _ensure_prepared_schema(session) + + logger.info("=" * 80) + logger.info("PREPARING WEEKLY SALES FORECASTING DATA BY STORE FOR GETML") + logger.info("=" * 80) + + # Data preparation pipeline + _analyze_and_display_stores(session) + per_store = _create_weekly_population_table(session) + _add_target_column(session) + + # Display analysis and quality checks + _display_sample_data(session, per_store) + _display_store_statistics(session) + _display_overall_statistics(session) + _perform_data_quality_check(session) + _display_recent_snapshots(session, per_store) + + logger.info("\n" + "=" * 80) + logger.info("DATA PREPARATION COMPLETE!") + logger.info("=" * 80) + logger.info("\nTables created in PREPARED schema:") + logger.info(" - population_weekly_by_store") + logger.info(" - population_weekly_by_store_with_target (USE THIS FOR GETML)") + logger.info( + "\nFor getML integration instructions, see: " + "docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md" + ) + logger.info("\n") + + +if __name__ == "__main__": + # Configure logging for standalone execution + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + ) + + # Example usage - requires connection parameters + logger.info("This module requires a Snowflake session to be passed.") + logger.info("Example usage:") + logger.info(""" + from settings import SnowflakeSettings + from snowflake_session import create_session + from data import prepare_weekly_sales_by_store + + settings = SnowflakeSettings.from_env() + + with create_session(settings) as session: + prepare_weekly_sales_by_store(session) + """) diff --git a/integration/snowflake/data/sql/bootstrap/create_database.sql b/integration/snowflake/data/sql/bootstrap/create_database.sql new file mode 100644 index 0000000..0972095 --- /dev/null +++ b/integration/snowflake/data/sql/bootstrap/create_database.sql @@ -0,0 +1,2 @@ +-- Create Snowflake database if it doesn't exist +CREATE DATABASE IF NOT EXISTS {database_name} diff --git a/integration/snowflake/data/sql/bootstrap/create_warehouse.sql b/integration/snowflake/data/sql/bootstrap/create_warehouse.sql new file mode 100644 index 0000000..7d1252d --- /dev/null +++ b/integration/snowflake/data/sql/bootstrap/create_warehouse.sql @@ -0,0 +1,7 @@ +-- Create Snowflake warehouse with specified configuration +CREATE WAREHOUSE IF NOT EXISTS {warehouse_name} +WITH + WAREHOUSE_SIZE = '{warehouse_size}' + AUTO_SUSPEND = {auto_suspend_seconds} + AUTO_RESUME = TRUE + INITIALLY_SUSPENDED = TRUE diff --git a/integration/snowflake/data/sql/ingestion/create_external_stage.sql b/integration/snowflake/data/sql/ingestion/create_external_stage.sql new file mode 100644 index 0000000..6022d8d --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_external_stage.sql @@ -0,0 +1,9 @@ +-- Create external stage pointing to S3 bucket for CSV data ingestion +CREATE OR REPLACE STAGE RAW.{stage_name} + URL = '{bucket_url}' + FILE_FORMAT = ( + TYPE = 'CSV' + FIELD_DELIMITER = ',' + SKIP_HEADER = 1 + FIELD_OPTIONALLY_ENCLOSED_BY = '"' + ) diff --git a/integration/snowflake/data/sql/ingestion/create_raw_schema.sql b/integration/snowflake/data/sql/ingestion/create_raw_schema.sql new file mode 100644 index 0000000..0a0b6a9 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_raw_schema.sql @@ -0,0 +1,2 @@ +-- Create RAW schema for raw data ingestion +CREATE SCHEMA IF NOT EXISTS RAW diff --git a/integration/snowflake/data/sql/preparation/analyze_stores.sql b/integration/snowflake/data/sql/preparation/analyze_stores.sql new file mode 100644 index 0000000..138f111 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/analyze_stores.sql @@ -0,0 +1,13 @@ +-- Analyze stores and their activity +SELECT + s.id, + s.name, + s.opened_at, + MIN(o.ordered_at) as first_order, + MAX(o.ordered_at) as last_order, + COUNT(o.id) as total_orders, + SUM(COALESCE(o.order_total, 0)) / 100.0 as total_sales +FROM RAW.raw_stores s +LEFT JOIN RAW.raw_orders o ON o.store_id = s.id +GROUP BY s.id, s.name, s.opened_at +ORDER BY s.opened_at diff --git a/integration/snowflake/data/sql/preparation/count_snapshots.sql b/integration/snowflake/data/sql/preparation/count_snapshots.sql new file mode 100644 index 0000000..c915aef --- /dev/null +++ b/integration/snowflake/data/sql/preparation/count_snapshots.sql @@ -0,0 +1,2 @@ +-- Count total snapshots +SELECT COUNT(*) FROM PREPARED.population_weekly_by_store diff --git a/integration/snowflake/data/sql/preparation/count_stores.sql b/integration/snowflake/data/sql/preparation/count_stores.sql new file mode 100644 index 0000000..59a20de --- /dev/null +++ b/integration/snowflake/data/sql/preparation/count_stores.sql @@ -0,0 +1,2 @@ +-- Count distinct stores +SELECT COUNT(DISTINCT store_id) FROM PREPARED.population_weekly_by_store diff --git a/integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql b/integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql new file mode 100644 index 0000000..a65d317 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql @@ -0,0 +1,56 @@ +-- Create weekly snapshots per store +-- Only include weeks where: +-- - Store opened BEFORE the week started (store was open at beginning of week) +-- - Store has orders throughout the entire prediction week (store was open entire week) +CREATE TABLE PREPARED.population_weekly_by_store AS +WITH store_weeks AS ( + -- Get all weeks from data + SELECT DISTINCT + DATE_TRUNC('week', ordered_at) as week_start + FROM RAW.raw_orders + WHERE ordered_at IS NOT NULL +), +store_activity AS ( + -- Get first full week for each store (week after opening) + SELECT + s.id as store_id, + s.name as store_name, + s.opened_at, + -- First full week is the Monday after opening week + DATE_TRUNC('week', s.opened_at) + INTERVAL '7 days' as first_full_week, + MAX(o.ordered_at) as last_order_date, + DATE_TRUNC('week', MAX(o.ordered_at)) as last_order_week + FROM RAW.raw_stores s + LEFT JOIN RAW.raw_orders o ON o.store_id = s.id + GROUP BY s.id, s.name, s.opened_at +), +valid_store_weeks AS ( + -- Cross join stores with weeks, filter to valid weeks only + SELECT + sa.store_id, + sa.store_name, + sw.week_start, + sw.week_start - INTERVAL '1 second' as snapshot_time, + sw.week_start as prediction_week_start, + (sw.week_start + INTERVAL '7 days' - INTERVAL '1 second') + as prediction_week_end + FROM store_activity sa + CROSS JOIN store_weeks sw + WHERE + -- Store was open before this week started + sw.week_start >= sa.first_full_week + -- Don't include the last week (might be incomplete) + AND sw.week_start < sa.last_order_week +) +SELECT + ROW_NUMBER() OVER (ORDER BY snapshot_time, store_id) as snapshot_id, + store_id, + store_name, + snapshot_time, + prediction_week_start, + prediction_week_end, + EXTRACT(year FROM snapshot_time) as year, + EXTRACT(month FROM snapshot_time) as month, + EXTRACT(week FROM snapshot_time) as week_number +FROM valid_store_weeks +ORDER BY snapshot_time, store_id diff --git a/integration/snowflake/data/sql/preparation/create_population_with_target.sql b/integration/snowflake/data/sql/preparation/create_population_with_target.sql new file mode 100644 index 0000000..9b01bcc --- /dev/null +++ b/integration/snowflake/data/sql/preparation/create_population_with_target.sql @@ -0,0 +1,23 @@ +-- Calculate target - total sales for the following week per store +CREATE TABLE PREPARED.population_weekly_by_store_with_target AS +SELECT + p.*, + COALESCE( + ( + SELECT SUM(order_total) / 100.0 + FROM RAW.raw_orders o + WHERE o.store_id = p.store_id + AND o.ordered_at >= p.prediction_week_start + AND o.ordered_at <= p.prediction_week_end + ), 0 + ) as next_week_sales, + COALESCE( + ( + SELECT COUNT(*) + FROM RAW.raw_orders o + WHERE o.store_id = p.store_id + AND o.ordered_at >= p.prediction_week_start + AND o.ordered_at <= p.prediction_week_end + ), 0 + ) as next_week_orders +FROM PREPARED.population_weekly_by_store p diff --git a/integration/snowflake/data/sql/preparation/create_prepared_schema.sql b/integration/snowflake/data/sql/preparation/create_prepared_schema.sql new file mode 100644 index 0000000..d959066 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/create_prepared_schema.sql @@ -0,0 +1,2 @@ +-- Create PREPARED schema for processed data ready for ML +CREATE SCHEMA IF NOT EXISTS PREPARED diff --git a/integration/snowflake/data/sql/preparation/data_quality_check.sql b/integration/snowflake/data/sql/preparation/data_quality_check.sql new file mode 100644 index 0000000..35b28ea --- /dev/null +++ b/integration/snowflake/data/sql/preparation/data_quality_check.sql @@ -0,0 +1,7 @@ +-- Data quality check +SELECT + COUNT(*) as total, + SUM(CASE WHEN next_week_sales = 0 THEN 1 ELSE 0 END) as zero_sales, + SUM(CASE WHEN next_week_orders = 0 THEN 1 ELSE 0 END) as zero_orders, + SUM(CASE WHEN next_week_sales > 0 AND next_week_orders = 0 THEN 1 ELSE 0 END) as invalid +FROM PREPARED.population_weekly_by_store_with_target diff --git a/integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql b/integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql new file mode 100644 index 0000000..7c8f0bb --- /dev/null +++ b/integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql @@ -0,0 +1,2 @@ +-- Drop the population table if it exists +DROP TABLE IF EXISTS PREPARED.population_weekly_by_store diff --git a/integration/snowflake/data/sql/preparation/drop_population_with_target.sql b/integration/snowflake/data/sql/preparation/drop_population_with_target.sql new file mode 100644 index 0000000..8c29d4d --- /dev/null +++ b/integration/snowflake/data/sql/preparation/drop_population_with_target.sql @@ -0,0 +1,2 @@ +-- Drop the population table with target if it exists +DROP TABLE IF EXISTS PREPARED.population_weekly_by_store_with_target diff --git a/integration/snowflake/data/sql/preparation/overall_statistics.sql b/integration/snowflake/data/sql/preparation/overall_statistics.sql new file mode 100644 index 0000000..8a53514 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/overall_statistics.sql @@ -0,0 +1,8 @@ +-- Overall statistics +SELECT + COUNT(*) as total_snapshots, + COUNT(DISTINCT store_id) as num_stores, + AVG(next_week_sales) as avg_weekly_sales, + SUM(next_week_sales) as total_sales, + SUM(next_week_orders) as total_orders +FROM PREPARED.population_weekly_by_store_with_target diff --git a/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql b/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql new file mode 100644 index 0000000..39a7809 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql @@ -0,0 +1,11 @@ +-- Most recent snapshots for a specific store +SELECT + snapshot_id, + snapshot_time, + prediction_week_start, + next_week_sales, + next_week_orders +FROM PREPARED.population_weekly_by_store_with_target +WHERE store_name = '{store_name}' +ORDER BY snapshot_time DESC +LIMIT 3 diff --git a/integration/snowflake/data/sql/preparation/sample_data_by_store.sql b/integration/snowflake/data/sql/preparation/sample_data_by_store.sql new file mode 100644 index 0000000..0ddffea --- /dev/null +++ b/integration/snowflake/data/sql/preparation/sample_data_by_store.sql @@ -0,0 +1,11 @@ +-- Sample data - first 5 snapshots for a specific store +SELECT + snapshot_id, + snapshot_time, + prediction_week_start, + next_week_sales, + next_week_orders +FROM PREPARED.population_weekly_by_store_with_target +WHERE store_name = '{store_name}' +ORDER BY snapshot_time +LIMIT 5 diff --git a/integration/snowflake/data/sql/preparation/snapshots_per_store.sql b/integration/snowflake/data/sql/preparation/snapshots_per_store.sql new file mode 100644 index 0000000..97f105c --- /dev/null +++ b/integration/snowflake/data/sql/preparation/snapshots_per_store.sql @@ -0,0 +1,9 @@ +-- Show snapshots per store +SELECT + store_name, + COUNT(*) as num_snapshots, + MIN(snapshot_time) as first_snapshot, + MAX(snapshot_time) as last_snapshot +FROM PREPARED.population_weekly_by_store +GROUP BY store_name +ORDER BY first_snapshot diff --git a/integration/snowflake/data/sql/preparation/store_statistics.sql b/integration/snowflake/data/sql/preparation/store_statistics.sql new file mode 100644 index 0000000..0776150 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/store_statistics.sql @@ -0,0 +1,12 @@ +-- Statistics per store +SELECT + store_name, + COUNT(*) as num_snapshots, + AVG(next_week_sales) as avg_weekly_sales, + MIN(next_week_sales) as min_weekly_sales, + MAX(next_week_sales) as max_weekly_sales, + STDDEV(next_week_sales) as stddev_weekly_sales, + SUM(next_week_orders) as total_orders +FROM PREPARED.population_weekly_by_store_with_target +GROUP BY store_name +ORDER BY avg_weekly_sales DESC diff --git a/integration/snowflake/data/sql/preparation/zero_sales_investigation.sql b/integration/snowflake/data/sql/preparation/zero_sales_investigation.sql new file mode 100644 index 0000000..83533ea --- /dev/null +++ b/integration/snowflake/data/sql/preparation/zero_sales_investigation.sql @@ -0,0 +1,6 @@ +-- Investigate stores with zero sales +SELECT store_name, COUNT(*) as count +FROM PREPARED.population_weekly_by_store_with_target +WHERE next_week_sales = 0 +GROUP BY store_name +ORDER BY count DESC diff --git a/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md b/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md new file mode 100644 index 0000000..fe79729 --- /dev/null +++ b/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md @@ -0,0 +1,116 @@ +# getML Weekly Sales Data Preparation Guide - By Store + +**This guide explains how to use the prepared weekly sales forecasting data with getML for store-level predictions.** + +The data preparation pipeline creates population tables with weekly snapshots (Sunday nights) per store, calculating the target as total sales for the following week. Only full weeks where the store was open for the entire week are included. + +--- + +## 1. Population Table + +**Table name:** `population_weekly_by_store_with_target` + +| Column | Description | +|--------|-------------| +| `snapshot_id` | Unique identifier for each prediction point | +| `store_id` | The store being predicted | +| `store_name` | Store name (for reference) | +| `snapshot_time` | The time of prediction (Sunday 23:59:59) | +| `prediction_week_start` | Start of the week being predicted | +| `prediction_week_end` | End of the week being predicted | + +--- + +## 2. Target Column + +**Column name:** `next_week_sales` + +- This is the sum of sales for the following 7 days **for that specific store** +- Unit: dollars (already divided by 100) + +--- + +## 3. Time Column + +**Column name:** `snapshot_time` + +- Mark this as `time_stamp` role in getML +- Ensures no data leakage (only past data used for prediction) + +--- + +## 4. Join Key + +**Column name:** `store_id` + +- Links population to store-specific data + +--- + +## 5. Join Tables + +| Table | Join Condition | +|-------|----------------| +| `raw_stores` | `store.id = population.store_id` | +| `raw_orders` | `order.store_id = population.store_id AND order.ordered_at <= snapshot_time` | +| `raw_customers` | Join through orders | +| `raw_items` | Join through orders | +| `raw_products` | Join through items | +| `raw_tweets` | `tweet.tweeted_at <= snapshot_time` (if relevant to stores) | + +--- + +## 6. Example getML Code + +```python +import getml + +# Load population (one row per store per week) +population = getml.DataFrame.from_db( + name="population", + table_name="population_weekly_by_store_with_target" +) + +# Set roles +population.set_role("snapshot_id", getml.data.roles.join_key) +population.set_role("store_id", getml.data.roles.join_key) +population.set_role("snapshot_time", getml.data.roles.time_stamp) +population.set_role("next_week_sales", getml.data.roles.target) +population.set_role("store_name", getml.data.roles.categorical) + +# Load peripheral tables +stores = getml.DataFrame.from_db(name="stores", table_name="raw_stores") +stores.set_role("id", getml.data.roles.join_key) +stores.set_role("opened_at", getml.data.roles.time_stamp) + +orders = getml.DataFrame.from_db(name="orders", table_name="raw_orders") +orders.set_role("store_id", getml.data.roles.join_key) +orders.set_role("ordered_at", getml.data.roles.time_stamp) + +# Define container with store-specific relationships +container = getml.data.Container(population=population) +container.add( + stores=stores, + orders=orders, + # ... other tables +) + +# Create propositionalization for store-level features +# getML will automatically aggregate order history per store + +# Train model +pipe = getml.Pipeline(...) +pipe.fit(container.train) + +# Predict next week's sales for each store +predictions = pipe.predict(container.test) +``` + +--- + +## 7. Key Differences from Overall Prediction + +- **Row granularity:** Each row represents ONE store's prediction for ONE week +- **Feature scope:** Features will be store-specific (that store's history) +- **Analysis capability:** Can compare store performance, identify store-specific patterns +- **Training data:** More training examples (6 stores × N weeks instead of just N weeks) diff --git a/integration/snowflake/docs/python-testing-guide.md b/integration/snowflake/docs/python-testing-guide.md new file mode 100644 index 0000000..3155ad2 --- /dev/null +++ b/integration/snowflake/docs/python-testing-guide.md @@ -0,0 +1,456 @@ +# Pytest Best Practices: A Comprehensive Guide to Python Testing Excellence + +**The path to reliable, maintainable tests starts with understanding what to test and how to test it effectively.** The Python testing community, led by experts like Brian Okken, Martin Fowler, Kent Beck, and Ned Batchelder, has established clear principles: write tests until fear transforms into confidence, mock only at boundaries, and measure success not by coverage numbers but by how rarely bugs escape to production. This guide synthesizes authoritative sources into actionable patterns for writing pytest tests that catch real bugs, survive refactoring, and make you move faster. + +Testing is fundamentally about confidence—the confidence to refactor without breaking things, to deploy without anxiety, and to evolve your codebase safely. The practices below represent years of community wisdom distilled into principles that work at scale, from small projects to enterprise codebases. + +## Fixtures and conftest.py: Building blocks for reusable test infrastructure + +**Fixtures are pytest's superpower**, providing a modular, composable way to manage test setup and teardown. They make test dependencies explicit, enable resource sharing, and handle cleanup automatically through Python's yield mechanism. + +### When to use fixtures versus regular functions + +Use fixtures when you need lifecycle management, resource cleanup, or state sharing across tests. The official pytest documentation emphasizes that fixtures have explicit names and are activated by declaring their use from test functions. This declarative approach makes dependencies visible and manageable. Fixtures excel at managing expensive resources like database connections, API clients, or browser instances where pytest should handle the lifecycle automatically. + +Avoid creating fixtures for trivial data. A common anti-pattern identified by pytest core developers is wrapping simple data in fixtures: `@pytest.fixture def my_list(): return ["a", "b", "c"]` adds unnecessary indirection when a plain function or inline data suffices. For test data generation without cleanup needs or shared state, a regular function is always simpler. + +### Understanding and applying fixture scopes + +Pytest offers five scope levels that control fixture lifetime and sharing. **Function scope** (the default) provides maximum isolation, executing once per test with teardown after each test. Use this when each test needs fresh, independent data. **Class scope** executes once per test class, shared among all test methods—useful when multiple test methods need the same resource but be cautious of side effects between methods. + +**Module scope** executes once per test module, shared across all tests in the module, ideal for expensive setup that's safe to share like SMTP connections. **Package scope** executes once per package, less commonly used but valuable when multiple modules in a package require shared resources. **Session scope** executes once for the entire test session, most efficient for expensive resources like database connections or WebDriver instances, but must be defined in conftest.py to be practical. + +The critical rule: fixtures can use broader-scoped fixtures but not narrower ones. A session-scoped fixture cannot meaningfully use a module-scoped one. Scope selection depends on three factors: isolation needs (higher isolation requires smaller scope), performance (expensive setups benefit from larger scopes), and side effects (shared state can cause test interdependence). + +### Factory fixtures for flexibility + +The factory-as-fixture pattern solves situations where you need multiple instances in one test: + +```python +@pytest.fixture +def make_customer(): + def _make_customer(name, email): + return Customer(name=name, email=email) + return _make_customer + +def test_multiple_customers(make_customer): + customer1 = make_customer("Alice", "[email protected]") + customer2 = make_customer("Bob", "[email protected]") + assert customer1.name != customer2.name +``` + +This pattern provides flexibility while maintaining fixture benefits like automatic discovery and conftest.py sharing. + +### Organizing conftest.py for large projects + +The conftest.py file serves as pytest's configuration and fixture hub, automatically discovered without imports. Its primary purposes include sharing fixtures across test files, implementing local per-directory plugins, defining hooks for test collection and execution, and configuring pytest behavior for specific directories. + +Successful projects use a hierarchical approach. The root conftest.py contains session fixtures and global configuration—the most general, widely-used fixtures. Subdirectory conftest.py files hold specialized fixtures for that test category. For example, separate integration/ and unit/ directories can each have conftest.py files with appropriate fixtures. **Nested conftest.py files add to parent fixtures rather than replacing them**, creating a fixture inheritance hierarchy. + +For very large test suites, the pytest_plugins approach provides better organization: + +```python +# tests/conftest.py +pytest_plugins = [ + "tests.fixtures.database", + "tests.fixtures.api", + "tests.fixtures.auth", +] +``` + +This modularization separates fixtures into logical files, making them easier to locate and maintain. The official documentation warns: pytest_plugins in non-root conftest.py files is deprecated—define it only in the tests root directory. + +### Critical fixture anti-patterns to avoid + +**Over-fixturing** creates fixtures for trivial data when simple functions suffice. **Implicit dependencies** abuse autouse when explicit fixture requests make tests clearer. **Testing through fixtures** uses API calls to set up fixtures for testing other API calls—if fixture setup fails, the test fails for the wrong reason; use ORM or direct database access instead. + +**Stateful fixtures without proper scope** share mutable state across tests unintentionally, breaking test isolation. **Complex parametrized fixtures** make tests harder to understand; consider factory functions instead. **Nested mocks** within fixtures indicate the unit under test knows too much about collaborators—mock direct collaborators only. + +## Parameterization: Testing multiple scenarios efficiently + +Parameterization through `@pytest.mark.parametrize` eliminates repetitive test code by running the same logic with different inputs. Each parameter set runs as a separate test with a unique ID, so failed parameters don't affect others. This approach reduces code duplication while improving test coverage. + +Use parameterization when testing the same logic with different inputs, validating edge cases and boundaries, testing multiple variations of similar scenarios, or when repetitive test code differs only in data. The pattern excels at testing input validation comprehensively: + +```python +TEST_CASES = [ + ("[email protected]", True), + ("invalid.email", False), + ("@example.com", False), +] + +@pytest.mark.parametrize("email,is_valid", TEST_CASES) +def test_email_validation(email, is_valid): + assert validate_email(email) == is_valid +``` + +### Structuring parametrized tests for clarity + +Use descriptive parameter names—`input_string,expected_output` instead of `x,y`. Separate test data from test logic by defining test cases as module-level constants or variables. Use `pytest.param` for complex scenarios with custom IDs: + +```python +@pytest.mark.parametrize("input,expected,error", [ + pytest.param(4, 2, None, id="normal_division"), + pytest.param(2, 0, ZeroDivisionError, id="division_by_zero"), +]) +def test_division(input, expected, error): + if error: + with pytest.raises(error): + divide(input, 2) + else: + assert divide(input, 2) == expected +``` + +Custom test IDs make failures easier to debug and allow running specific test cases with pytest's `-k` flag. + +### When parameterization becomes harmful + +Avoid parameterization when tests have significantly different logic requiring different setup or assertions—adding if/else complexity to accommodate parameters reduces clarity. Don't parametrize when test readability suffers; sometimes explicit separate tests communicate intent better than abstracted parametrization. + +Skip parameterization for one-off test cases or when debugging becomes difficult due to excessive abstraction. The expert consensus: balance DRY principles with clarity. Make sure you're not "parametrizing your test suite into incomprehensibility." + +### Indirect parameterization for expensive setup + +Use `indirect=True` to pass parameters through fixtures when expensive setup is needed for each parameter or when parameters need transformation before use: + +```python +@pytest.fixture +def database(request): + db = create_database(request.param) + yield db + db.close() + +@pytest.mark.parametrize("database", ["mysql", "postgres"], indirect=True) +def test_query(database): + assert database.query("SELECT 1") +``` + +This pattern defers expensive setup until test runtime rather than collection time, improving performance. + +## Unit tests versus integration tests: Choosing the right granularity + +**Unit tests** verify small units of code in isolation, running in milliseconds with external dependencies stubbed or mocked. They form the base of the testing pyramid—you should have lots of them. In functional languages, a unit is typically a single function; in object-oriented languages, it ranges from a single method to an entire class. The key is testing behavior, not implementation details. + +Martin Fowler distinguishes between **sociable unit tests** that allow talking to real collaborators when sensible, and **solitary unit tests** that stub all collaborators for perfect isolation. His advice: use both approaches as situations warrant. + +**Integration tests** verify that your application correctly works with external components like databases, APIs, or filesystems. They run much slower than unit tests—tens per second instead of hundreds. Integration tests focus on boundaries where your code meets the outside world. Write some, but fewer than unit tests. + +### The testing pyramid guides test distribution + +Mike Cohn's testing pyramid, popularized by Martin Fowler, structures tests by granularity. At the base: many fast unit tests. In the middle: some integration tests for component interactions. At the top: few end-to-end UI tests. The pyramid shape reflects that fast tests at the bottom catch bugs close to the root cause, while broad tests at the top are slower, harder to maintain, and more susceptible to flaky failures. + +Fowler's key principle: **write tests with different granularity, and the more high-level you get, the fewer tests you should have.** Write integration tests for all code that serializes or deserializes data—calls to REST APIs, reading/writing databases, calling other applications' APIs, reading/writing queues, and writing to the filesystem. + +### When to use each test type + +Use **unit tests** for business logic and algorithms, individual classes and functions, fast feedback loops, testing edge cases and input combinations, and documenting how specific code units work. The focus is internal correctness. + +Use **integration tests** for database interactions, API integrations with external services, file system operations, message queue interactions, and verifying serialization/deserialization works correctly. The focus is boundary correctness. + +Don't duplicate coverage across pyramid levels. Fowler's rule: if a higher-level test spots an error and there's no lower-level test failing, you need to write a lower-level test. Push your tests as far down the pyramid as you can. + +### Best practices for each type + +**Unit test structure** follows the Arrange-Act-Assert pattern (or Given-When-Then). Keep tests small and focused—test one thing at a time with one assertion or closely related assertions. Test behavior through public interfaces, not implementation. Focus on observable outcomes rather than internal state. Avoid testing private methods and trivial code like getters/setters. + +**Integration test practices** emphasize testing at the boundary—focus on the integration point itself without retesting logic already covered by unit tests. Use appropriate test doubles: for testing your database repository, use a real test database; for testing external APIs, use tools like Wiremock or Pact. Run external dependencies locally when possible. Keep integration tests narrow rather than reimplementing all unit tests at a higher level. + +## Test coverage philosophy: Focusing on meaningful tests + +Martin Fowler's definitive statement: **"Test coverage is a useful tool for finding untested parts of a codebase. Test coverage is of little use as a numeric statement of how good your tests are."** The fundamental problem with coverage targets is that people write tests to make coverage numbers happy without thinking about test quality. + +Fowler identifies the target problem: if you make a certain level of coverage a target, people will try to attain it, but high coverage numbers are too easy to reach with low-quality testing. Developers can write tests that execute code without actually testing anything meaningful—assertion-free tests that satisfy coverage tools while providing zero protection against bugs. + +### What coverage actually reveals + +Coverage tells you which parts of your codebase aren't being executed by tests, helping identify potential gaps deserving attention. Coverage doesn't tell you whether your tests check the right things, can catch meaningful bugs, have quality assertions, or distinguish business logic from trivial code. + +Fowler states that with thoughtful testing, coverage should naturally fall in the upper 80s or 90s. **Anything like 100% should raise suspicion**—it would smell of someone writing tests to make coverage numbers happy rather than testing effectively. + +### Martin Fowler's sufficiency test + +You are doing enough testing if: (1) you rarely get bugs that escape into production, and (2) you are rarely hesitant to change code for fear it will cause production bugs. This outcome-based measure beats any coverage percentage. + +Kent Beck's philosophy reinforces this: **"I get paid for code that works, not for tests, so my philosophy is to test as little as possible to reach a given level of confidence."** He adds: "Can you test too much? Sure you can. You are testing too much if you can remove tests while still having enough." + +### Why measuring coverage in TDD is counterproductive + +James Shore argues that measuring unit test coverage in test-driven development is worse than useless—it actively misleads. The definition of TDD is that you don't write code without a failing test, doing so in a tight loop covering one branch at a time. If you're doing TDD properly, any code you write is ipso facto covered. + +Instead of measuring coverage, Shore recommends focusing on root-cause analysis of escaped defects, teaching testing skills, refactoring and evolutionary design, pairing and mobbing for better discipline, and adding tests as you work on legacy code. + +### What not to test + +Skip testing trivial getters and setters, simple pass-through methods, framework code you didn't write, and code without conditional logic. Kent Beck explicitly gives permission: don't test trivial code. Use coverage tools periodically to find untested code, then review those areas asking "Does it worry me that this isn't tested?" rather than setting organizational mandates like "87% coverage required." + +## Testing external dependencies: Drawing the right boundaries + +**The central principle: don't test external libraries themselves; test YOUR integration with them.** Pydantic, requests, SQLAlchemy, and other well-maintained libraries already have comprehensive test suites. Your responsibility is testing how your code uses these libraries, not verifying the libraries work. + +### What not to test: Don't verify library behavior + +Never write tests that verify Pydantic's validation works: + +```python +# DON'T do this - testing Pydantic, not your code +def test_pydantic_validates(): # ❌ BAD + class User(BaseModel): + name: str + age: int + + user = User(name="John", age=30) + assert user.name == "John" # Testing the library +``` + +Don't test that the requests library makes HTTP calls correctly or that SQLAlchemy's ORM functions work. These libraries are tested by their maintainers. + +### What to test: Your integration and business logic + +Test YOUR validation logic built on top of libraries: + +```python +# DO test your business logic +def test_user_creation_logic(): # ✅ GOOD + try: + user = create_user_from_data({"name": "", "age": -5}) + assert False, "Should have raised validation error" + except InvalidUserError as e: + assert "name cannot be empty" in str(e) +``` + +Test YOUR error handling around library code: + +```python +# DO test your error handling +def test_api_client_handles_errors(mocker): # ✅ GOOD + mock_requests = mocker.patch('myapp.client.requests.post') + mock_requests.side_effect = requests.ConnectionError() + + client = ApiClient() + with pytest.raises(ApiUnavailableError): + client.send_data({"test": "data"}) +``` + +### Testing at boundaries with the adapter pattern + +Best practice wraps external dependencies in adapters. Create an abstract interface that your business logic depends on, then implement concrete adapters for specific technologies: + +```python +class StorageAdapter(ABC): + @abstractmethod + def save(self, key: str, data: bytes) -> None: + pass + + @abstractmethod + def load(self, key: str) -> bytes: + pass + +class S3StorageAdapter(StorageAdapter): + def __init__(self, client): + self.client = client + + def save(self, key: str, data: bytes) -> None: + self.client.put_object(Bucket='mybucket', Key=key, Body=data) +``` + +Your business logic depends on the interface, not the implementation. For testing, create fake implementations: + +```python +class FakeStorageAdapter(StorageAdapter): + def __init__(self): + self.data = {} + + def save(self, key: str, data: bytes) -> None: + self.data[key] = data + + def load(self, key: str) -> bytes: + return self.data[key] + +def test_document_service(): + fake_storage = FakeStorageAdapter() + service = DocumentService(fake_storage) + + service.save_document("doc1", "Hello World") + assert fake_storage.data["docs/doc1"] == b"Hello World" +``` + +This approach tests your code without mocking, making tests resilient to implementation changes. + +### Decision framework for testing dependencies + +Test your database schema (integration tests with real DB)—it's part of your contract. Use in-memory SQLite for SQLAlchemy ORM tests—fast with real database behavior. Mock external APIs at the adapter/client boundary using verified fakes or contract tests. Mock or inject datetime.now() since it's non-deterministic. Use temp directories or mocks for file I/O since I/O is slow. + +For your custom adapters, write integration tests quarterly against real implementations and unit tests daily with mocks. This balance ensures your abstractions match reality while maintaining fast test feedback. + +## Happy path versus edge cases: Balancing testing strategies + +**Happy path testing** verifies default scenarios with no exceptional or error conditions—the ideal user journey where everything goes right. Expected inputs produce successful operations and positive outcomes without errors or edge cases. + +**Edge cases** cover uncommon scenarios outside normal usage patterns, boundary conditions, unexpected or unusual inputs, and error conditions. However, Albert Gareev warns: until we sufficiently learn about the users, the product, and the environment, we have no idea what usage pattern is mainstream and what would be edge cases. What seems like an edge case might actually be common for your users. + +### Testing priority and strategy + +Start with happy path testing to ensure core functionality works, demonstrating value delivery and providing a foundation for further testing. But don't stop there—happy path alone is insufficient. As White Test Lab notes, "While sad path testing—where things go wrong—gets all the attention, happy path testing is what ensures your core value actually works." + +Then add edge case testing for boundary conditions, error handling, unexpected inputs, and resource constraints. Prioritize using a risk-based approach considering both likelihood and impact: high impact with low likelihood still deserves testing, low impact with low likelihood might be skipped. + +### What edge cases to test + +**Input validation boundaries** require testing empty strings, null/None values, maximum/minimum values, negative numbers where unexpected, special characters and unicode, and very long inputs. + +**State management scenarios** check what happens when actions are performed out of order, whether users can break workflows by going out of sequence, and what if the same action triggers multiple times. + +**Resource constraints** verify behavior with slow network connections, timeouts, database connection failures, and disk space limitations. + +**Concurrent operations** test multiple users acting simultaneously, race conditions, and locking and transaction issues. + +The Treehouse course wisely notes: "On the whole, edge cases are going to make up a lot more of your test cases than just the happy path. So it's good to understand them. But also understand that you simply can't think of everything." + +Balance the approach—don't get paralyzed trying to think of every edge case, but do think systematically about types of edge cases. Use production monitoring and bug reports to find missing tests. Add tests when bugs are found for regression protection. + +## Smoke tests and sanity tests: Quick validation strategies + +**Smoke testing** verifies that the most critical functionalities work after a new build—broad and shallow coverage of the entire system at a high level. The question it answers: "Is the build stable enough for further testing?" Also called Build Verification Testing (BVT), smoke testing is done early, immediately after a build. + +**Sanity testing** verifies that specific bug fixes or features work correctly after changes—narrow and deep focus on specific functionality. The question it answers: "Did the recent changes work without breaking related functionality?" Sanity testing is done after smoke testing passes and after minor changes. + +### Key differences in scope and application + +Smoke testing covers the entire system with wide end-to-end coverage but shallow depth. It's usually automated and scripted, performed by developers or testers, and serves as a subset of acceptance testing. Example smoke tests for a LinkedIn app verify: launch app and log in, check newsfeed loads, verify messaging works, test basic job search, and confirm profile is accessible. + +Sanity testing targets specific components with narrow coverage but deeper exploration. It can be manual or automated and is often unscripted, typically performed by testers, serving as a subset of regression testing. Example sanity test for a password validation bug: navigate to login, enter username, enter password below minimum length, verify validation error appears, then enter valid password and verify login works. + +### Integration with testing strategy + +Tests follow a sequential relationship: New Build → Smoke Test → Pass? → Sanity Test → Pass? → Full Testing. If smoke tests fail, reject the build. If sanity tests fail, return to development. + +Katalon recommends: "Execute smoke testing first to confirm fundamental build stability, followed by sanity testing to validate specific fixes or enhancements." + +In CI/CD pipelines, smoke tests run automatically on every build with fast execution (under 10 minutes), serving as a gate for proceeding to further stages. They can run in parallel across environments. Sanity tests run after smoke tests pass, focus on changed areas, may be triggered by specific conditions, and may run less frequently than smoke tests. + +### Best practices for implementation + +**For smoke tests**: keep them fast (under 10 minutes total), automate them, cover critical user journeys, test end-to-end when possible, include both UI and API levels, and run on real devices/environments when critical. + +**For sanity tests**: focus on changed areas, include related functionality checks, can be manual for complex scenarios, document what was tested, maintain quick execution without exhaustiveness, and record results for team awareness. + +## Mocking best practices: When and how to isolate dependencies + +**The core principle: mock roles, not objects. Mock behaviors at boundaries, not implementations.** From the seminal paper "Mock Roles, Not Objects" by Freeman and Price, focus on interactions between objects based on their roles, verify that objects collaborate correctly to fulfill their roles, and think about the protocol of communication rather than implementation. + +### What to mock and what not to mock + +**Mock unmanaged dependencies**—out-of-process dependencies you don't fully control: SMTP servers, message buses (RabbitMQ, Kafka), external APIs (third-party REST APIs, payment gateways), cloud services (AWS S3, Azure services when not testing the adapter itself), non-deterministic operations (random number generators, current time), and expensive operations (long-running computations, slow network calls). These produce side effects visible to external systems, forming part of your system's observable behavior and contract. + +**Don't mock managed dependencies**—components you fully control: application databases (databases only your application accesses), internal services (microservices you own and deploy together), value objects (simple data structures like dataclasses, Pydantic models), pure functions (functions without side effects), and cheap objects (simple objects that don't cross process boundaries). These aren't directly observable by external clients; you can change their implementation without breaking backward compatibility. + +### Distinguishing mocks from stubs + +**Mocks** verify outgoing commands (side effects): + +```python +def test_sends_email(mocker): + mock_gateway = mocker.patch('app.EmailGateway') + controller = Controller(mock_gateway) + + controller.greet_user("[email protected]") + + # Verify the interaction (command was sent) + mock_gateway.send_greetings_email.assert_called_once_with("[email protected]") +``` + +**Stubs** provide incoming data (queries): + +```python +def test_creates_report(mocker): + stub_db = mocker.patch('app.Database') + stub_db.get_number_of_users.return_value = 10 + + controller = Controller(stub_db) + report = controller.create_report() + + assert report.number_of_users == 10 + # NEVER assert stub interactions - that's an anti-pattern! +``` + +The critical rule: never assert interactions with stubs. Doing so creates brittle tests coupled to implementation details. + +### Essential mocking techniques in pytest + +Use **pytest-mock** with autospec for safety: + +```python +def test_function(mocker): + mock_service = mocker.patch('myapp.service.ExternalService', autospec=True) + mock_service.return_value.fetch_data.return_value = {"result": "success"} + + result = my_function() + + assert result == "success" + mock_service.return_value.fetch_data.assert_called_once() +``` + +**Always use autospec=True** to prevent signature violations. Without autospec, tests pass even with wrong arguments, causing production failures. With autospec, tests fail immediately with wrong signatures. + +**Patch where used, not where defined**. If module_a imports requests, patch 'module_a.requests.get', not 'requests.get'. This common mistake causes mocks not to take effect. + +### Critical anti-patterns with mocking + +**Mockery** (over-mocking) means too many mocks make tests test mocks rather than real behavior. Signs include more mock setup code than actual test code, deep mock chains like `mock.method().result.value.something()`, mocking every dependency including simple ones, and tests that mirror implementation exactly. + +**Mocking implementation details** couples tests to internal structure. Tests break when refactoring even though behavior is unchanged. Avoid mocking private methods or internal implementation details. Mock at the boundary (the database) rather than internal method calls. + +**Asserting interactions with stubs** creates brittle tests that fail when changing how you query data, even if behavior remains correct. Only assert outcomes, not stub interactions. + +**Mocking low-level architecture** tightly couples tests to implementation choices. If you mock SQLAlchemy's internals, switching to a different ORM breaks all tests. Instead, use the adapter pattern with fakes. + +### When mocking is appropriate + +Good use cases include non-determinism (time, randomness, external events), performance (slow operations like network or disk I/O), availability (services not available in test environment), cost (expensive API calls like SMS or payment processing), and safety (dangerous operations like email sending or data deletion). + +Ned Batchelder's insight: use fakes instead of mocks. Create authoritative, well-tested fake implementations—one fake per service shared across tests rather than N different mocks. Mock only expensive external services; everything else uses real code. + +## Additional critical best practices + +### Test organization and naming + +Structure tests to mirror your application or use the testing pyramid organization with separate unit/, integration/, and e2e/ directories. Keep test files focused, splitting at around 500 lines. Use consistent naming: test files as test_*.py, test functions starting with test_. + +Choose descriptive test names that explain what behavior is being tested: `test_empty_list_has_zero_length()` or `test_user_creation_with_valid_email()`. Include docstrings for complex tests explaining the scenario being validated. + +### Test isolation and independence + +Each test must run independently in any order. Avoid shared mutable state between tests. Use pytest fixtures with appropriate scopes for test data. The fixture scope determines sharing: function scope (default) for maximum isolation, module/class scope only when safe to share. + +Don't use autouse fixtures unless ALL tests in that scope need them—autouse makes dependencies implicit and harder to debug. Each test should get fresh data through fixtures with proper cleanup. + +### Avoiding flaky tests + +Flaky tests—those that fail intermittently—cause more harm than no tests. Kraken Technologies identifies primary causes: tight coupling to system clock (use freezegun or inject time as dependency), implicit query ordering (always use explicit ORDER BY), randomly generated test data (use deterministic data or fixed seeds), and test pollution (state leaking through caches or global objects). + +Fix test pollution with cleanup fixtures: + +```python +@pytest.fixture(autouse=True) +def clear_django_cache(): + yield # Run the test + cache.clear() # Clean up after +``` + +### Keeping tests maintainable + +Keep tests small and focused, testing one thing at a time. Use the Arrange-Act-Assert pattern consistently. Make tests self-documenting through clear naming and minimal setup code. Centralize fixture definitions in conftest.py rather than duplicating them. Keep test code as clean as production code—tests are documentation. + +Split overloaded tests that verify multiple things into focused tests. Don't create dependencies between tests. Use markers to categorize tests (slow, integration, unit) enabling selective test runs. + +### Leveraging pytest's ecosystem + +Use pytest-cov for coverage reporting, pytest-xdist for parallel execution, pytest-mock for simplified mocking, and pytest-django for Django integration. Explore the pytest plugin ecosystem—hundreds of plugins extend pytest's capabilities. + +Use pytest's rich command-line options: `-v` for verbose output, `--pdb` to drop into debugger on failure, `--lf` to re-run last failed tests, `-k` for keyword selection, and `-m` for marker-based selection. + +## The expert consensus + +Martin Fowler: Write tests with different granularity; the more high-level you get, the fewer tests you should have. Kent Beck: Test until fear is transformed into boredom. James Shore: Focus on preventing defects through TDD, not measuring tests. Ned Batchelder: Use fakes instead of mocks; test interfaces, not implementation. Brian Okken: Fixtures are why people switch to and stay with pytest—leverage their modularity. + +The Python testing community agrees: tests are a means to an end. The end is software that works, that you can change confidently, and that serves users well. Start with the basics, apply patterns incrementally, and remember that good tests make you move faster, not slower. Write tests that catch real bugs, survive refactoring, and maintain clarity. Test behavior through interfaces, mock only at boundaries, and measure success by how rarely bugs escape to production. diff --git a/integration/snowflake/mise.toml b/integration/snowflake/mise.toml new file mode 100644 index 0000000..69eac93 --- /dev/null +++ b/integration/snowflake/mise.toml @@ -0,0 +1,9 @@ +[env] +SNOWFLAKE_ACCOUNT = "{{ exec(command='op read --account getml.1password.eu \"op://svibqfali56qralxbzg6wanc5i/Snowflake/account\"', cache_key='snowflake_account', duration='1w') }}" +SNOWFLAKE_USER = "{{ exec(command='op read --account getml.1password.eu \"op://svibqfali56qralxbzg6wanc5i/Snowflake/username\"', cache_key='snowflake_user', duration='1w') }}" +SNOWFLAKE_PASSWORD = "{{ exec(command='op read --account getml.1password.eu \"op://svibqfali56qralxbzg6wanc5i/Snowflake/password\"', cache_key='snowflake_password', duration='1w') }}" + +SNOWFLAKE_ROLE = "ACCOUNTADMIN" +SNOWFLAKE_WAREHOUSE = "COMPUTE_WH" +SNOWFLAKE_DATABASE = "JAFFLE_SHOP" +SNOWFLAKE_SCHEMA = "RAW" diff --git a/integration/snowflake/pyproject.toml b/integration/snowflake/pyproject.toml new file mode 100644 index 0000000..a12e17b --- /dev/null +++ b/integration/snowflake/pyproject.toml @@ -0,0 +1,130 @@ +[project] +name = "getml-featurestore-integrations" +version = "0.1.0" +description = "Integrations and Data Preparation for getML Feature Stores" +authors = [ + { name = "Code17 GmbH", email = "hello@code17.io" }, + { name = "getML", email = "hello@getml.com" }, +] +maintainers = [ + { name = "Code17 GmbH", email = "hello@code17.io" }, + { name = "getML", email = "hello@getml.com" }, +] +license = { text = "Proprietary" } +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Operating System :: OS Independent", + "Private :: Do Not Upload", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development :: Libraries :: Python Modules", +] +readme = "README.md" +requires-python = ">=3.12" + +dependencies = [ + "pydantic-settings>=2.12.0", + "snowflake-connector-python>=4.1.0", + "snowflake-snowpark-python>=1.42.0", +] + +[dependency-groups] +dev = [ + "ruff~=0.12.2", + "basedpyright~=1.28.4", + "pytest~=8.0.0", + "pytest-cov>=6.2.1", +] + +[tool.uv] +package = false + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] +python_classes = ["Test*"] +python_functions = ["test_*"] +markers = [ + "integration: marks tests as integration tests (require Snowflake credentials)", +] + +[project.urls] +"Homepage" = "https://github.com/getml/getml-demo" +"Bug Tracker" = "https://github.com/getml/getml-demo/issues" +"getML" = "https://getml.com" +"Code17 GmbH" = "https://www.code17.io/" + +[tool.pyright] +venvPath = "." +venv = ".venv" +reportMissingTypeStubs = false +reportImplicitStringConcatenation = false + +[[tool.pyright.executionEnvironments]] +root = "tests" +extraPaths = ["."] +reportUnusedParameter = false + +[build-system] +requires = ["uv_build>=0.7.21,<0.8.0"] +build-backend = "uv_build" + +[tool.ruff] +line-length = 88 +target-version = "py312" + +[tool.ruff.format] +preview = false +quote-style = "double" +line-ending = "auto" +docstring-code-format = true + +[tool.ruff.lint] +select = ["ALL"] +ignore = [ + # Allow for string literals in exceptions + "EM", + # Allow missing copyright notice at top of files + "CPY001", + # Allow missing docstrings in public modules + "D100", + # Allow missing docstrings in public classes + "D101", + # Allow missing docstrings in public packages + "D104", + # Allow docstrings without blank line before class docstring + "D203", + # Allow multi-line docstring summary to start at second line + "D213", + # Allow first-party imports outside type-checking blocks + "TC001", + # Allow third-party imports outside type-checking blocks + "TC002", + # Allow standard library imports outside type-checking blocks + "TC003", + # Allow TODO comments + "FIX002", + # Allow TODO comments without author + "TD002", + # Allow TODO comments without link to issue + "TD003", + # Allow specifying long messages outside the exception class + "TRY003", + # Conflicts with formatter - trailing commas are handled by ruff format + "COM812", +] + +fixable = ["ALL"] + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.lint.per-file-ignores] +# S101: Allow for use of the assert keyword +# PLR2004: Allow "magic value" used in comparison +"test_*.py" = ["S101", "PLR2004"] diff --git a/integration/snowflake/settings.py b/integration/snowflake/settings.py new file mode 100644 index 0000000..cdf0e21 --- /dev/null +++ b/integration/snowflake/settings.py @@ -0,0 +1,61 @@ +"""Snowflake connection settings using pydantic-settings. + +Provides typed configuration classes that automatically load from environment +variables with the SNOWFLAKE_ prefix. + +Usage example: + from settings import SnowflakeAdminSettings, SnowflakeSettings + + # Auto-loads from SNOWFLAKE_* environment variables + admin_settings = SnowflakeAdminSettings.from_env() + full_settings = SnowflakeSettings.from_env() +""" + +from typing import ClassVar, Self + +from pydantic import Field, SecretStr +from pydantic_settings import BaseSettings, SettingsConfigDict + +DEFAULT_SNOWFLAKE_ROLE = "ACCOUNTADMIN" +DEFAULT_SNOWFLAKE_SCHEMA = "RAW" + + +class SnowflakeAdminSettings(BaseSettings): + """Settings for administrative Snowflake operations (bootstrap). + + These minimal settings allow connecting without specifying warehouse + or database, which is required for creating those resources. + """ + + model_config: ClassVar[SettingsConfigDict] = SettingsConfigDict( + env_prefix="SNOWFLAKE_", + case_sensitive=False, + ) + + account: str + user: str + password: SecretStr + role: str = DEFAULT_SNOWFLAKE_ROLE + + @classmethod + def from_env(cls) -> Self: + """Create settings instance from environment variables. + + Loads configuration from SNOWFLAKE_* environment variables. + """ + return cls.model_validate({}) + + +class SnowflakeSettings(SnowflakeAdminSettings): + """Full settings for Snowflake data operations. + + Extends admin settings with warehouse, database, and schema + required for executing queries and loading data. + """ + + warehouse: str + database: str + schema_name: str = Field( + default=DEFAULT_SNOWFLAKE_SCHEMA, + validation_alias="SNOWFLAKE_SCHEMA", + ) diff --git a/integration/snowflake/snowflake_session.py b/integration/snowflake/snowflake_session.py new file mode 100644 index 0000000..6a2599a --- /dev/null +++ b/integration/snowflake/snowflake_session.py @@ -0,0 +1,85 @@ +"""Snowflake session management for getML Feature Store integration. + +This module provides factory functions for creating Snowflake Snowpark sessions. +Sessions support the context manager protocol for automatic cleanup. + +Usage example: + from settings import SnowflakeAdminSettings, SnowflakeSettings + from snowflake_session import create_admin_session, create_session + + # Admin session for bootstrap operations + with create_admin_session(SnowflakeAdminSettings.from_env()) as session: + session.sql("CREATE DATABASE IF NOT EXISTS mydb").collect() + + # Full session for data operations + with create_session(SnowflakeSettings.from_env()) as session: + session.sql("SELECT * FROM mytable").collect() +""" + +import logging + +from snowflake.snowpark import Session + +from settings import SnowflakeAdminSettings, SnowflakeSettings + +logger: logging.Logger = logging.getLogger(__name__) + +# TODO: Evaluate single SnowflakeSettings class with optional fields + + +def create_admin_session(settings: SnowflakeAdminSettings) -> Session: + """Create a Snowflake session for administrative operations. + + This session connects without specifying warehouse or database, + allowing creation of those resources during bootstrap. + + Raises: + SnowflakeSessionError: If connection to Snowflake fails. + """ + connection_params: dict[str, str | int] = { + "account": settings.account, + "user": settings.user, + "password": settings.password.get_secret_value(), + "role": settings.role, + } + + return _create_session(connection_params) + + +def create_session(settings: SnowflakeSettings) -> Session: + """Create a Snowflake session for data operations. + + This session connects with full context including warehouse, + database, and schema for executing queries and loading data. + + Raises: + SnowflakeSessionError: If connection to Snowflake fails. + """ + connection_params: dict[str, str | int] = { + "account": settings.account, + "user": settings.user, + "password": settings.password.get_secret_value(), + "role": settings.role, + "warehouse": settings.warehouse, + "database": settings.database, + "schema": settings.schema_name, + } + + return _create_session(connection_params) + + +class SnowflakeSessionError(Exception): + """Raised when Snowflake session creation fails.""" + + +def _create_session(connection_params: dict[str, str | int]) -> Session: + """Create a Snowpark session with the given parameters.""" + try: + return Session.builder.configs(connection_params).create() + except Exception as e: + raise SnowflakeSessionError( + f"Failed to create Snowflake session. " + f"Account: {connection_params.get('account')}, " + f"User: {connection_params.get('user')}. " + f"Error: {e}" + ) from e diff --git a/integration/snowflake/tests/__init__.py b/integration/snowflake/tests/__init__.py new file mode 100644 index 0000000..5011730 --- /dev/null +++ b/integration/snowflake/tests/__init__.py @@ -0,0 +1 @@ +"""Test package for getML Snowflake integration.""" diff --git a/integration/snowflake/tests/cleanup_test_schemas.py b/integration/snowflake/tests/cleanup_test_schemas.py new file mode 100644 index 0000000..3ebacfd --- /dev/null +++ b/integration/snowflake/tests/cleanup_test_schemas.py @@ -0,0 +1,86 @@ +"""Cleanup utility for test schemas in Snowflake. + +Drops all TEST_* schemas created by integration tests. Can be run standalone +or imported by test fixtures for pre/post-test cleanup. + +Usage: + # Standalone execution + uv run python tests/cleanup_test_schemas.py + + # Import in fixtures + from tests.cleanup_test_schemas import cleanup_test_schemas + cleanup_test_schemas(session) +""" + +# ruff: noqa: G004, S608, PLC0415 + +import logging +import sys +from pathlib import Path + +from snowflake.snowpark import Row, Session + +logger: logging.Logger = logging.getLogger(__name__) + +TEST_SCHEMA_PREFIX = "TEST_" + + +def cleanup_test_schemas(session: Session, database: str | None = None) -> list[str]: + """Drop all schemas with TEST_ prefix in the specified database. + + Args: + session: Active Snowflake session. + database: Database to clean. If None, uses session's current database. + + Returns: + List of dropped schema names. + """ + if database: + _ = session.sql(f"USE DATABASE {database}").collect() + + # Get all schemas with TEST_ prefix (prefix is a constant, not user input) + result: list[Row] = session.sql(f""" + SELECT SCHEMA_NAME + FROM INFORMATION_SCHEMA.SCHEMATA + WHERE SCHEMA_NAME LIKE '{TEST_SCHEMA_PREFIX}%' + """).collect() + + dropped_schemas: list[str] = [] + + for row in result: + schema_name = str(row["SCHEMA_NAME"]) # pyright: ignore[reportUnknownArgumentType] + _ = session.sql(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE").collect() + dropped_schemas.append(schema_name) + logger.info(f"Dropped schema: {schema_name}") + + return dropped_schemas + + +def main() -> None: + """Run cleanup as standalone script.""" + # Add project root to path for imports when run standalone + sys.path.insert(0, str(Path(__file__).parent.parent)) + + from settings import SnowflakeSettings + from snowflake_session import create_session + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + ) + + logger.info("Starting test schema cleanup...") + + settings = SnowflakeSettings.from_env() + + with create_session(settings) as session: + dropped: list[str] = cleanup_test_schemas(session, settings.database) + + if dropped: + logger.info(f"Cleanup complete. Dropped {len(dropped)} test schema(s).") + else: + logger.info("No test schemas found to clean up.") + + +if __name__ == "__main__": + main() diff --git a/integration/snowflake/tests/conftest.py b/integration/snowflake/tests/conftest.py new file mode 100644 index 0000000..5cdd685 --- /dev/null +++ b/integration/snowflake/tests/conftest.py @@ -0,0 +1,53 @@ +"""Shared test fixtures for unit tests. + +Provides mock Snowflake session factories and utilities for testing +data pipeline logic without requiring actual Snowflake connections. +""" + +# pyright: reportAny=false + +import uuid +from collections.abc import Callable +from unittest.mock import MagicMock + +import pytest + + +@pytest.fixture +def test_run_id() -> str: + """Generate unique identifier for test isolation. + + Returns short UUID suffix for creating isolated test schemas. + """ + return uuid.uuid4().hex[:8] + + +@pytest.fixture +def mock_session() -> MagicMock: + """Create a mock Snowflake Snowpark session. + + The mock supports chained calls like `session.sql(...).collect()`. + Configure return values per test using: + `mock_session.sql.return_value.collect.return_value`. + """ + session = MagicMock() + session.sql.return_value.collect.return_value = [] + return session + + +@pytest.fixture +def mock_session_factory() -> Callable[[], MagicMock]: + """Factory fixture for creating mock sessions with different behaviors. + + Usage: + def test_something(mock_session_factory): + session = mock_session_factory() + session.sql.return_value.collect.return_value = [{"COUNT": 100}] + """ + + def _create_mock_session() -> MagicMock: + session = MagicMock() + session.sql.return_value.collect.return_value = [] + return session + + return _create_mock_session diff --git a/integration/snowflake/tests/integration/__init__.py b/integration/snowflake/tests/integration/__init__.py new file mode 100644 index 0000000..d387fff --- /dev/null +++ b/integration/snowflake/tests/integration/__init__.py @@ -0,0 +1 @@ +"""Integration test package for getML Snowflake integration.""" diff --git a/integration/snowflake/tests/integration/conftest.py b/integration/snowflake/tests/integration/conftest.py new file mode 100644 index 0000000..38a79d8 --- /dev/null +++ b/integration/snowflake/tests/integration/conftest.py @@ -0,0 +1,126 @@ +"""Fixtures for integration tests requiring real Snowflake connections. + +Provides session-scoped fixtures that: +1. Run cleanup of stale TEST_* schemas before tests +2. Create isolated schemas with TEST_{uuid} prefix for test isolation +3. Clean up test schemas after the test session completes +""" + +# ruff: noqa: G004 + +import logging +import os +import sys +import uuid +from collections.abc import Generator +from pathlib import Path + +import pytest +from pydantic import ValidationError +from snowflake.snowpark import Session + +# Add project root to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from settings import SnowflakeSettings +from snowflake_session import create_session +from tests.cleanup_test_schemas import cleanup_test_schemas + +logger = logging.getLogger(__name__) + + +def _snowflake_credentials_available() -> bool: + """Check if required Snowflake environment variables are set.""" + required_vars: list[str] = [ + "SNOWFLAKE_ACCOUNT", + "SNOWFLAKE_USER", + "SNOWFLAKE_PASSWORD", + "SNOWFLAKE_WAREHOUSE", + "SNOWFLAKE_DATABASE", + ] + return all(os.environ.get(var) for var in required_vars) + + +@pytest.fixture(scope="session") +def integration_test_id() -> str: + """Generate unique identifier for this test session. + + Used to create isolated schemas that don't conflict with + parallel test runs. + """ + return uuid.uuid4().hex[:8].upper() + + +@pytest.fixture(scope="session") +def snowflake_settings() -> SnowflakeSettings: + """Load Snowflake settings from environment variables. + + Skips tests if credentials are not available. + """ + if not _snowflake_credentials_available(): + pytest.skip("Snowflake credentials not available in environment") + + try: + return SnowflakeSettings.from_env() + except ValidationError as e: + pytest.skip(f"Invalid Snowflake configuration: {e}") + + +@pytest.fixture(scope="session") +def snowflake_session( + snowflake_settings: SnowflakeSettings, + integration_test_id: str, +) -> Generator[Session, None, None]: + """Create a Snowflake session for integration tests. + + Performs pre-test cleanup of stale TEST_* schemas, then yields + the session for test use. Post-test cleanup removes schemas + created during this test session. + """ + with create_session(snowflake_settings) as session: + # Pre-test cleanup: remove any stale TEST_* schemas from previous runs + logger.info("Running pre-test cleanup of stale test schemas...") + dropped = cleanup_test_schemas(session, snowflake_settings.database) + if dropped: + logger.info(f"Pre-test cleanup dropped {len(dropped)} stale schema(s)") + + yield session + + # Post-test cleanup: remove schemas created during this test session + logger.info(f"Running post-test cleanup for test session {integration_test_id}") + _ = cleanup_test_schemas(session, snowflake_settings.database) + + +@pytest.fixture(scope="session") +def test_raw_schema( + snowflake_session: Session, + integration_test_id: str, +) -> str: + """Create an isolated RAW schema for testing data ingestion. + + Creates TEST_RAW_{uuid} schema that is cleaned up after tests. + """ + schema_name = f"TEST_RAW_{integration_test_id}" + + logger.info(f"Creating test RAW schema: {schema_name}") + _ = snowflake_session.sql(f"CREATE SCHEMA IF NOT EXISTS {schema_name}").collect() + _ = snowflake_session.sql(f"USE SCHEMA {schema_name}").collect() + + return schema_name + + +@pytest.fixture(scope="session") +def test_prepared_schema( + snowflake_session: Session, + integration_test_id: str, +) -> str: + """Create an isolated PREPARED schema for testing data preparation. + + Creates TEST_PREPARED_{uuid} schema that is cleaned up after tests. + """ + schema_name = f"TEST_PREPARED_{integration_test_id}" + + logger.info(f"Creating test PREPARED schema: {schema_name}") + _ = snowflake_session.sql(f"CREATE SCHEMA IF NOT EXISTS {schema_name}").collect() + + return schema_name diff --git a/integration/snowflake/tests/integration/test_pipeline.py b/integration/snowflake/tests/integration/test_pipeline.py new file mode 100644 index 0000000..31bfb78 --- /dev/null +++ b/integration/snowflake/tests/integration/test_pipeline.py @@ -0,0 +1,177 @@ +"""Integration tests for the full data pipeline. + +These tests require real Snowflake credentials and verify end-to-end +functionality of data ingestion and preparation. + +Run with: uv run pytest tests/integration/ -m integration +""" + +# ruff: noqa: G004, S608, E501 + +import logging +import sys +from pathlib import Path + +import pytest +from snowflake.snowpark import Row, Session + +# Add project root to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from data.ingestion import ( + JAFFLE_SHOP_TABLES, + S3_BUCKET_URL, + STAGE_NAME, +) + +logger = logging.getLogger(__name__) + +pytestmark = pytest.mark.integration + + +class TestDataIngestion: + """Integration tests for S3 to Snowflake data loading.""" + + def test_load_jaffle_shop_data_creates_tables_with_rows( + self, + snowflake_session: Session, + test_raw_schema: str, + ) -> None: + """Verify all Jaffle Shop tables are created with data.""" + # Ensure we're using the test schema + _ = snowflake_session.sql(f"USE SCHEMA {test_raw_schema}").collect() + + # Create the stage in test schema + _ = snowflake_session.sql(f""" + CREATE OR REPLACE STAGE {STAGE_NAME} + URL = '{S3_BUCKET_URL}' + FILE_FORMAT = (TYPE = CSV FIELD_OPTIONALLY_ENCLOSED_BY = '"' SKIP_HEADER = 1) + """).collect() + + # Load data using the pipeline function (modified to use current schema) + results = self._load_tables_to_test_schema(snowflake_session, test_raw_schema) + + # Verify all tables were loaded + assert set(results.keys()) == set(JAFFLE_SHOP_TABLES.keys()) + + # Verify all tables have rows + for table_name, row_count in results.items(): + assert row_count > 0, f"Table {table_name} has 0 rows" + + def _load_tables_to_test_schema( + self, + session: Session, + schema_name: str, + ) -> dict[str, int]: + """Load tables into the test schema. + + Simplified version of load_jaffle_shop_data for test isolation. + """ + results: dict[str, int] = {} + + for table_name, schema_def in JAFFLE_SHOP_TABLES.items(): + _ = session.sql(f""" + CREATE OR REPLACE TABLE {schema_name}.{table_name} {schema_def} + """).collect() + + _ = session.sql(f""" + COPY INTO {schema_name}.{table_name} + FROM @{schema_name}.{STAGE_NAME}/{table_name}.csv + FORCE = FALSE + """).collect() + + count_result: list[Row] = session.sql(f""" + SELECT COUNT(*) as count FROM {schema_name}.{table_name} + """).collect() + + row_count = int(count_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] + results[table_name] = row_count + logger.info(f"Loaded {table_name}: {row_count} rows") + + return results + + def test_tables_have_expected_columns( + self, + snowflake_session: Session, + test_raw_schema: str, + ) -> None: + """Verify tables have expected column structure.""" + # Check raw_orders has expected columns + result: list[Row] = snowflake_session.sql(f""" + SELECT COLUMN_NAME + FROM INFORMATION_SCHEMA.COLUMNS + WHERE TABLE_SCHEMA = '{test_raw_schema}' + AND TABLE_NAME = 'RAW_ORDERS' + ORDER BY ORDINAL_POSITION + """).collect() + + column_names = [str(row["COLUMN_NAME"]) for row in result] # pyright: ignore[reportUnknownArgumentType] + + expected_columns = [ + "ID", + "CUSTOMER", + "ORDERED_AT", + "STORE_ID", + "SUBTOTAL", + "TAX_PAID", + "ORDER_TOTAL", + ] + assert column_names == expected_columns + + +class TestDataPreparation: + """Integration tests for data preparation pipeline.""" + + def test_prepare_weekly_sales_creates_population_tables( + self, + snowflake_session: Session, + test_raw_schema: str, + test_prepared_schema: str, + ) -> None: + """Verify preparation creates population tables with correct structure.""" + # First ensure data is loaded (reuse from ingestion test if already run) + count_result: list[Row] = snowflake_session.sql(f""" + SELECT COUNT(*) as count FROM {test_raw_schema}.raw_orders + """).collect() + + if int(count_result[0]["COUNT"]) == 0: # pyright: ignore[reportArgumentType] + pytest.skip("Data ingestion test must run first") + + # Create population table in test prepared schema + _ = snowflake_session.sql(f""" + CREATE OR REPLACE TABLE {test_prepared_schema}.population_weekly_by_store AS + SELECT + ROW_NUMBER() OVER (ORDER BY s.name, DATE_TRUNC('week', o.ordered_at)) as id, + s.id as store_id, + s.name as store_name, + DATE_TRUNC('week', o.ordered_at) as week_start, + DATEADD('day', 6, DATE_TRUNC('week', o.ordered_at)) as snapshot_date, + SUM(o.order_total) / 100.0 as weekly_sales, + COUNT(*) as weekly_orders + FROM {test_raw_schema}.raw_stores s + JOIN {test_raw_schema}.raw_orders o ON s.id = o.store_id + GROUP BY s.id, s.name, DATE_TRUNC('week', o.ordered_at) + """).collect() + + # Verify table was created with data + verification_result: list[Row] = snowflake_session.sql(f""" + SELECT COUNT(*) as count + FROM {test_prepared_schema}.population_weekly_by_store + """).collect() + + row_count = int(verification_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] + assert row_count > 0, "Population table should have data" + + def test_population_table_has_multiple_stores( + self, + snowflake_session: Session, + test_prepared_schema: str, + ) -> None: + """Verify population table contains data for multiple stores.""" + result: list[Row] = snowflake_session.sql(f""" + SELECT COUNT(DISTINCT store_id) as store_count + FROM {test_prepared_schema}.population_weekly_by_store + """).collect() + + store_count = int(result[0]["STORE_COUNT"]) # pyright: ignore[reportArgumentType] + assert store_count > 1, "Population table should have multiple stores" diff --git a/integration/snowflake/tests/test_data_init.py b/integration/snowflake/tests/test_data_init.py new file mode 100644 index 0000000..a8392d9 --- /dev/null +++ b/integration/snowflake/tests/test_data_init.py @@ -0,0 +1,53 @@ +"""Tests for data package utilities. + +Tests the load_sql function which handles SQL file loading +and parameter substitution. +""" + +# pyright: reportAny=false + +import pytest + +from data import load_sql + + +class TestLoadSql: + """Tests for load_sql utility function.""" + + def test_loads_existing_sql_file(self) -> None: + """Verify load_sql reads content from existing SQL file.""" + content = load_sql("preparation/create_prepared_schema.sql") + + assert "CREATE SCHEMA" in content + assert "PREPARED" in content + + def test_applies_formatting_when_kwargs_provided(self) -> None: + """Verify load_sql applies string formatting with provided kwargs.""" + content = load_sql( + "preparation/sample_data_by_store.sql", + store_name="Test Store", + ) + + assert "Test Store" in content + + def test_returns_raw_content_when_no_kwargs(self) -> None: + """Verify load_sql returns unmodified content without kwargs.""" + content = load_sql("ingestion/create_raw_schema.sql") + + # Content should contain the placeholder syntax if any exist, + # or just be the raw SQL + assert isinstance(content, str) + assert len(content) > 0 + + def test_raises_file_not_found_for_missing_file(self) -> None: + """Verify FileNotFoundError propagates for nonexistent SQL files.""" + with pytest.raises(FileNotFoundError): + _ = load_sql("nonexistent/missing.sql") + + def test_preserves_placeholders_when_no_kwargs(self) -> None: + """Verify load_sql preserves {placeholders} when no kwargs provided.""" + # sample_data_by_store.sql has {store_name} placeholder + content: str = load_sql("preparation/sample_data_by_store.sql") + + # Placeholder should remain in the content + assert "{store_name}" in content diff --git a/integration/snowflake/tests/test_ingestion.py b/integration/snowflake/tests/test_ingestion.py new file mode 100644 index 0000000..86f6612 --- /dev/null +++ b/integration/snowflake/tests/test_ingestion.py @@ -0,0 +1,135 @@ +"""Tests for data ingestion module. + +Tests error handling, rollback behavior, and successful data loading +orchestration logic using mocked Snowflake sessions. +""" + +# pyright: reportAny=false + +from unittest.mock import MagicMock + +import pytest + +from data.ingestion import ( + DataIngestionError, + load_jaffle_shop_data, + load_single_table, + rollback_transaction, +) + + +class TestLoadJaffleShopData: + """Tests for the main load_jaffle_shop_data orchestration function.""" + + def test_returns_dict_with_all_table_names_on_success( + self, mock_session: MagicMock + ) -> None: + """Verify successful load returns dict mapping table names to row counts.""" + # Configure mock to return appropriate responses for different SQL calls + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_parsed": 100} + ] + + result = load_jaffle_shop_data(mock_session) + + assert isinstance(result, dict) + expected_tables = { + "raw_customers", + "raw_orders", + "raw_order_items", + "raw_products", + "raw_stores", + "raw_supplies", + } + assert set(result.keys()) == expected_tables + + def test_executes_begin_and_commit_on_success( + self, mock_session: MagicMock + ) -> None: + """Verify transaction BEGIN and COMMIT are called on successful load.""" + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_parsed": 100} + ] + + _ = load_jaffle_shop_data(mock_session) + + sql_calls = [c[0][0] for c in mock_session.sql.call_args_list] + assert "BEGIN" in sql_calls + assert "COMMIT" in sql_calls + + def test_executes_rollback_on_exception(self, mock_session: MagicMock) -> None: + """Verify ROLLBACK is called when an exception occurs during loading.""" + # First call (BEGIN) succeeds, subsequent calls fail + mock_session.sql.return_value.collect.side_effect = [ + [], # BEGIN + Exception("Database error"), # Schema creation fails + ] + + with pytest.raises(DataIngestionError): + _ = load_jaffle_shop_data(mock_session) + + sql_calls = [c[0][0] for c in mock_session.sql.call_args_list] + assert "ROLLBACK" in sql_calls + + def test_wraps_exception_in_data_ingestion_error( + self, mock_session: MagicMock + ) -> None: + """Verify exceptions are wrapped in DataIngestionError with context.""" + mock_session.sql.return_value.collect.side_effect = [ + [], # BEGIN + Exception("Connection lost"), + ] + + with pytest.raises(DataIngestionError) as exc_info: + _ = load_jaffle_shop_data(mock_session) + + assert "Connection lost" in str(exc_info.value) + assert "Failed to load Jaffle Shop data" in str(exc_info.value) + + +class TestLoadSingleTable: + """Tests for individual table loading logic.""" + + def test_raises_error_when_row_count_is_zero(self, mock_session: MagicMock) -> None: + """Verify DataIngestionError raised when loaded table has 0 rows.""" + # Mock: CREATE succeeds, COPY succeeds, COUNT returns 0 + mock_session.sql.return_value.collect.side_effect = [ + [], # CREATE TABLE + [{"rows_parsed": 0}], # COPY INTO + [{"COUNT": 0}], # SELECT COUNT(*) + ] + + with pytest.raises(DataIngestionError) as exc_info: + _ = load_single_table(mock_session, "test_table", "(id TEXT)") + + assert "test_table" in str(exc_info.value) + assert "0 rows" in str(exc_info.value) + + def test_returns_row_count_on_success(self, mock_session: MagicMock) -> None: + """Verify function returns row count when loading succeeds.""" + mock_session.sql.return_value.collect.side_effect = [ + [], # CREATE TABLE + [{"rows_parsed": 500}], # COPY INTO + [{"COUNT": 500}], # SELECT COUNT(*) + ] + + result = load_single_table(mock_session, "test_table", "(id TEXT)") + + assert result == 500 + + +class TestRollbackTransaction: + """Tests for transaction rollback behavior.""" + + def test_executes_rollback_sql(self, mock_session: MagicMock) -> None: + """Verify ROLLBACK SQL is executed.""" + rollback_transaction(mock_session) + + mock_session.sql.assert_called_with("ROLLBACK") + + def test_suppresses_rollback_exceptions(self, mock_session: MagicMock) -> None: + """Verify exceptions during rollback are suppressed (logged only).""" + mock_session.sql.return_value.collect.side_effect = Exception("Rollback failed") + + # Should not raise + rollback_transaction(mock_session) diff --git a/integration/snowflake/tests/test_preparation.py b/integration/snowflake/tests/test_preparation.py new file mode 100644 index 0000000..365454f --- /dev/null +++ b/integration/snowflake/tests/test_preparation.py @@ -0,0 +1,57 @@ +"""Tests for data preparation module. + +Tests validation logic and pipeline orchestration using mocked +Snowflake sessions. +""" + +# pyright: reportAny=false + +from unittest.mock import MagicMock + +import pytest + +from data.preparation import prepare_weekly_sales_by_store, validate_raw_schema + + +class TestValidateRawSchema: + """Tests for RAW schema validation logic.""" + + def test_raises_value_error_when_table_query_fails( + self, mock_session: MagicMock + ) -> None: + """Verify ValueError raised when required table doesn't exist.""" + mock_session.sql.return_value.collect.side_effect = Exception( + "Table RAW.raw_stores does not exist" + ) + + with pytest.raises(ValueError, match="Failed to validate RAW schema"): + validate_raw_schema(mock_session) + 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.../data/sql/preparation/count_stores.sql | 2 +- .../create_population_weekly_by_store.sql | 56 - .../create_population_with_target.sql | 23 - .../sql/preparation/create_weekly_stores.sql | 67 + .../drop_population_weekly_by_store.sql | 2 - .../drop_population_with_target.sql | 2 - .../sql/preparation/drop_weekly_stores.sql | 2 + .../preparation/recent_snapshots_by_store.sql | 5 +- .../sql/preparation/sample_data_by_store.sql | 5 +- .../sql/preparation/snapshots_per_store.sql | 6 +- .../GETML_WEEKLY_SALES_DATA_PREPARATION.md | 52 +- integration/snowflake/pyproject.toml | 5 + .../snowflake/tests/cleanup_test_resources.py | 120 ++ .../snowflake/tests/cleanup_test_schemas.py | 86 -- .../snowflake/tests/integration/conftest.py | 87 +- .../tests/integration/test_pipeline.py | 218 +-- integration/snowflake/uv.lock | 1202 ++++++++++------- 22 files changed, 1242 insertions(+), 884 deletions(-) create mode 100644 integration/snowflake/data/GENERATE_JAFFLE_SHOP_PARQUET.md create mode 100644 integration/snowflake/data/convert_jaffle_csv_to_parquet.py create mode 100644 integration/snowflake/data/sql/preparation/calculate_target.sql delete mode 100644 integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql delete mode 100644 integration/snowflake/data/sql/preparation/create_population_with_target.sql create mode 100644 integration/snowflake/data/sql/preparation/create_weekly_stores.sql delete mode 100644 integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql delete mode 100644 integration/snowflake/data/sql/preparation/drop_population_with_target.sql create mode 100644 integration/snowflake/data/sql/preparation/drop_weekly_stores.sql create mode 100644 integration/snowflake/tests/cleanup_test_resources.py delete mode 100644 integration/snowflake/tests/cleanup_test_schemas.py diff --git a/integration/snowflake/data/GENERATE_JAFFLE_SHOP_PARQUET.md b/integration/snowflake/data/GENERATE_JAFFLE_SHOP_PARQUET.md new file mode 100644 index 0000000..f578e65 --- /dev/null +++ b/integration/snowflake/data/GENERATE_JAFFLE_SHOP_PARQUET.md @@ -0,0 +1,32 @@ +# Generate Parquet files from Jaffle Shop CSV data + +This script reads the Jaffle Shop CSV files and converts them to Parquet format for more efficient storage and querying in Snowflake. + +## Generate Jaffle Shop Data (CSV) + +To generate the Jaffle Shop CSV data, run the following command: + +```bash +pipx run jafgen 6 +``` + +This will create the necessary CSV files in the `jaffle-data` directory. + +## Convert CSV to Parquet + +To convert the generated CSV files to Parquet format, run the following script: + +```bash +python convert_jaffle_csv_to_parquet.py +``` + +This will read each CSV file from the `jaffle-data` directory and save the corresponding Parquet files in the `jaffle-data/parquet` directory. + +## Upload Parquet Files to GCP + +To upload the Parquet files to your GCP bucket, use the following commands: + +```bash +gcloud config set project getml-infra +gcloud storage cp jaffle-data/parquet/*.parquet gs://static.getml.com/datasets/jaffle_shop/ +``` diff --git a/integration/snowflake/data/convert_jaffle_csv_to_parquet.py b/integration/snowflake/data/convert_jaffle_csv_to_parquet.py new file mode 100644 index 0000000..e97ae58 --- /dev/null +++ b/integration/snowflake/data/convert_jaffle_csv_to_parquet.py @@ -0,0 +1,39 @@ +from pathlib import Path + +import pandas as pd + +NAMES: list[str] = [ + "raw_customers", + "raw_items", + "raw_orders", + "raw_products", + "raw_stores", + "raw_supplies", + "raw_tweets", +] + +JAFFLE_CSV_DATA_PATH = Path("jaffle-data") + +if not JAFFLE_CSV_DATA_PATH.exists(): + raise FileNotFoundError( + f"Jaffle CSV data path {JAFFLE_CSV_DATA_PATH} does not exist." + " Please run `jafgen` to generate CSVs." + ) + +JAFFLE_PARQUET_DATA_PATH = JAFFLE_CSV_DATA_PATH / "parquet" +Path.mkdir(JAFFLE_PARQUET_DATA_PATH, exist_ok=True) + + +for name in NAMES: + csv_filepath = JAFFLE_CSV_DATA_PATH / f"{name}.csv" + parquet_filepath = JAFFLE_PARQUET_DATA_PATH / f"{name}.parquet" + print(f"Loading {csv_filepath}...") + + # 1. Read CSV into memory + df: pd.DataFrame = pd.read_csv(csv_filepath) + + # 2. Write DataFrame to Parquet + # 'index=False' prevents pandas from adding an extra index column + df.to_parquet(parquet_filepath, index=False) + + print(f"Converted {name} to parquet format at {parquet_filepath}.") diff --git a/integration/snowflake/data/preparation.py b/integration/snowflake/data/preparation.py index 8a14bba..1040b4d 100644 --- a/integration/snowflake/data/preparation.py +++ b/integration/snowflake/data/preparation.py @@ -1,9 +1,10 @@ """Prepare weekly sales forecasting data for getML - BY STORE. -This script creates a population table with weekly snapshots (Sunday nights) -per store and calculates the target: total sales for the following week. +This module creates: +- weekly_stores table: Store-week combinations with reference_date (Monday week start) +- population_weekly_by_store_with_target view: Adds target (next week's sales) -Only includes full weeks where the store was open for the entire week. +reference_date is the Monday (week start) derived from DATE_TRUNC('week', ordered_at). SQL queries are externalized in the sql/ directory for better maintainability. """ @@ -66,22 +67,18 @@ def _analyze_and_display_stores(session: Session) -> None: def _create_weekly_population_table(session: Session) -> list[Row]: - """Create population table with Sunday night snapshots per store. + """Create weekly_stores table with store-week combinations. + + Creates one row per store per week using reference_date (Monday week start). Returns: List of store information rows. """ - logger.info( - "\n2. Creating weekly population table per store (Sunday night snapshots)..." - ) - logger.info(" Only including FULL weeks (store open entire week)...") + logger.info("\n2. Creating weekly_stores table (store-week combinations)...") + logger.info(" reference_date is Monday (week start) from DATE_TRUNC('week', ...)") - _ = session.sql( - load_sql("preparation/drop_population_weekly_by_store.sql") - ).collect() - _ = session.sql( - load_sql("preparation/create_population_weekly_by_store.sql") - ).collect() + _ = session.sql(load_sql("preparation/drop_weekly_stores.sql")).collect() + _ = session.sql(load_sql("preparation/create_weekly_stores.sql")).collect() num_snapshots: int = cast( "int", session.sql(load_sql("preparation/count_snapshots.sql")).collect()[0][0] @@ -107,10 +104,9 @@ def _create_weekly_population_table(session: Session) -> list[Row]: def _add_target_column(session: Session) -> None: - """Calculate target - total sales for the following week per store.""" - logger.info("\n3. Calculating target: next week's total sales per store...") - _ = session.sql(load_sql("preparation/drop_population_with_target.sql")).collect() - _ = session.sql(load_sql("preparation/create_population_with_target.sql")).collect() + """Create view with target - total sales for the following week per store.""" + logger.info("\n3. Creating target view: next week's total sales per store...") + _ = session.sql(load_sql("preparation/calculate_target.sql")).collect() def _display_sample_data(session: Session, per_store: list[Row]) -> None: @@ -126,16 +122,10 @@ def _display_sample_data(session: Session, per_store: list[Row]) -> None: ) samples: list[Row] = session.sql(sample_query).collect() - logger.info( - f"{'ID':<8} {'Snapshot (Sunday)':<20} {'Predict Week':<20} " - f"{'Sales $':<15} {'Orders':<10}" - ) - logger.info("-" * 80) + logger.info(f"{'ID':<8} {'Week Start':<20} {'Sales $':<15} {'Orders':<10}") + logger.info("-" * 60) for row in samples: - logger.info( - f"{row[0]:<8} {row[1]!s:<20} {row[2]!s:<20} " - f"${row[3]:<14,.2f} {row[4]:<10,}" - ) + logger.info(f"{row[0]:<8} {row[1]!s:<20} ${row[2]:<14,.2f} {row[3]:<10,}") def _display_store_statistics(session: Session) -> None: @@ -214,26 +204,19 @@ def _display_recent_snapshots(session: Session, per_store: list[Row]) -> None: ) recent: list[Row] = session.sql(recent_query).collect() - logger.info( - f"{'ID':<8} {'Snapshot (Sunday)':<20} {'Predict Week':<20} " - f"{'Sales $':<15} {'Orders':<10}" - ) - logger.info("-" * 80) + logger.info(f"{'ID':<8} {'Week Start':<20} {'Sales $':<15} {'Orders':<10}") + logger.info("-" * 60) for row in recent: - logger.info( - f"{row[0]:<8} {row[1]!s:<20} {row[2]!s:<20} " - f"${row[3]:<14,.2f} {row[4]:<10,}" - ) + logger.info(f"{row[0]:<8} {row[1]!s:<20} ${row[2]:<14,.2f} {row[3]:<10,}") def prepare_weekly_sales_by_store(session: Session) -> None: - """Prepare data for getML to predict next week's sales from Sunday night, grouped by store. + """Prepare data for getML to predict next week's sales, grouped by store. Creates: - - population_weekly_by_store: Weekly snapshots per store at Sunday 23:59:59 - - population_weekly_by_store_with_target: Population table with target column - - Target: Sum of order_total for the following 7 days for that store - - Only includes complete weeks (store was open entire week) + - weekly_stores: Table with store-week combinations (reference_date = Monday) + - population_weekly_by_store_with_target: View adding target column + - Target: Sum of order_total for the 7-day window starting at reference_date Args: session: Active Snowflake Snowpark session @@ -261,9 +244,11 @@ def prepare_weekly_sales_by_store(session: Session) -> None: logger.info("\n" + "=" * 80) logger.info("DATA PREPARATION COMPLETE!") logger.info("=" * 80) - logger.info("\nTables created in PREPARED schema:") - logger.info(" - population_weekly_by_store") - logger.info(" - population_weekly_by_store_with_target (USE THIS FOR GETML)") + logger.info("\nObjects created in PREPARED schema:") + logger.info(" - weekly_stores (TABLE)") + logger.info( + " - population_weekly_by_store_with_target (VIEW - USE THIS FOR GETML)" + ) logger.info( "\nFor getML integration instructions, see: " "docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md" diff --git a/integration/snowflake/data/sql/preparation/calculate_target.sql b/integration/snowflake/data/sql/preparation/calculate_target.sql new file mode 100644 index 0000000..330593b --- /dev/null +++ b/integration/snowflake/data/sql/preparation/calculate_target.sql @@ -0,0 +1,40 @@ +-- Create view with target: next week's sales per store +-- +-- This view joins weekly_stores with raw_orders to calculate the target variable. +-- Target is the sum of order_total for the 7-day window starting at reference_date. +-- +-- Window: [reference_date, reference_date + 7 days) +-- - reference_date is Monday 00:00:00 (week start) +-- - Target covers Monday through Sunday of that week +CREATE OR REPLACE VIEW PREPARED.population_weekly_by_store_with_target AS +SELECT + ws.snapshot_id, + ws.store_id, + ws.store_name, + ws.reference_date, + ws.year, + ws.month, + ws.week_number, + ws.days_since_open, + ws.is_full_week_after_opening, + ws.has_order_activity, + ws.has_min_history, + COALESCE( + ( + SELECT SUM(o.order_total) / 100.0 + FROM RAW.raw_orders o + WHERE o.store_id = ws.store_id + AND o.ordered_at >= ws.reference_date + AND o.ordered_at < ws.reference_date + INTERVAL '7 days' + ), 0 + ) as next_week_sales, + COALESCE( + ( + SELECT COUNT(*) + FROM RAW.raw_orders o + WHERE o.store_id = ws.store_id + AND o.ordered_at >= ws.reference_date + AND o.ordered_at < ws.reference_date + INTERVAL '7 days' + ), 0 + ) as next_week_orders +FROM PREPARED.weekly_stores ws; diff --git a/integration/snowflake/data/sql/preparation/count_snapshots.sql b/integration/snowflake/data/sql/preparation/count_snapshots.sql index c915aef..e867907 100644 --- a/integration/snowflake/data/sql/preparation/count_snapshots.sql +++ b/integration/snowflake/data/sql/preparation/count_snapshots.sql @@ -1,2 +1,2 @@ -- Count total snapshots -SELECT COUNT(*) FROM PREPARED.population_weekly_by_store +SELECT COUNT(*) FROM PREPARED.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/count_stores.sql b/integration/snowflake/data/sql/preparation/count_stores.sql index 59a20de..c4a5c8d 100644 --- a/integration/snowflake/data/sql/preparation/count_stores.sql +++ b/integration/snowflake/data/sql/preparation/count_stores.sql @@ -1,2 +1,2 @@ -- Count distinct stores -SELECT COUNT(DISTINCT store_id) FROM PREPARED.population_weekly_by_store +SELECT COUNT(DISTINCT store_id) FROM PREPARED.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql b/integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql deleted file mode 100644 index a65d317..0000000 --- a/integration/snowflake/data/sql/preparation/create_population_weekly_by_store.sql +++ /dev/null @@ -1,56 +0,0 @@ --- Create weekly snapshots per store --- Only include weeks where: --- - Store opened BEFORE the week started (store was open at beginning of week) --- - Store has orders throughout the entire prediction week (store was open entire week) -CREATE TABLE PREPARED.population_weekly_by_store AS -WITH store_weeks AS ( - -- Get all weeks from data - SELECT DISTINCT - DATE_TRUNC('week', ordered_at) as week_start - FROM RAW.raw_orders - WHERE ordered_at IS NOT NULL -), -store_activity AS ( - -- Get first full week for each store (week after opening) - SELECT - s.id as store_id, - s.name as store_name, - s.opened_at, - -- First full week is the Monday after opening week - DATE_TRUNC('week', s.opened_at) + INTERVAL '7 days' as first_full_week, - MAX(o.ordered_at) as last_order_date, - DATE_TRUNC('week', MAX(o.ordered_at)) as last_order_week - FROM RAW.raw_stores s - LEFT JOIN RAW.raw_orders o ON o.store_id = s.id - GROUP BY s.id, s.name, s.opened_at -), -valid_store_weeks AS ( - -- Cross join stores with weeks, filter to valid weeks only - SELECT - sa.store_id, - sa.store_name, - sw.week_start, - sw.week_start - INTERVAL '1 second' as snapshot_time, - sw.week_start as prediction_week_start, - (sw.week_start + INTERVAL '7 days' - INTERVAL '1 second') - as prediction_week_end - FROM store_activity sa - CROSS JOIN store_weeks sw - WHERE - -- Store was open before this week started - sw.week_start >= sa.first_full_week - -- Don't include the last week (might be incomplete) - AND sw.week_start < sa.last_order_week -) -SELECT - ROW_NUMBER() OVER (ORDER BY snapshot_time, store_id) as snapshot_id, - store_id, - store_name, - snapshot_time, - prediction_week_start, - prediction_week_end, - EXTRACT(year FROM snapshot_time) as year, - EXTRACT(month FROM snapshot_time) as month, - EXTRACT(week FROM snapshot_time) as week_number -FROM valid_store_weeks -ORDER BY snapshot_time, store_id diff --git a/integration/snowflake/data/sql/preparation/create_population_with_target.sql b/integration/snowflake/data/sql/preparation/create_population_with_target.sql deleted file mode 100644 index 9b01bcc..0000000 --- a/integration/snowflake/data/sql/preparation/create_population_with_target.sql +++ /dev/null @@ -1,23 +0,0 @@ --- Calculate target - total sales for the following week per store -CREATE TABLE PREPARED.population_weekly_by_store_with_target AS -SELECT - p.*, - COALESCE( - ( - SELECT SUM(order_total) / 100.0 - FROM RAW.raw_orders o - WHERE o.store_id = p.store_id - AND o.ordered_at >= p.prediction_week_start - AND o.ordered_at <= p.prediction_week_end - ), 0 - ) as next_week_sales, - COALESCE( - ( - SELECT COUNT(*) - FROM RAW.raw_orders o - WHERE o.store_id = p.store_id - AND o.ordered_at >= p.prediction_week_start - AND o.ordered_at <= p.prediction_week_end - ), 0 - ) as next_week_orders -FROM PREPARED.population_weekly_by_store p diff --git a/integration/snowflake/data/sql/preparation/create_weekly_stores.sql b/integration/snowflake/data/sql/preparation/create_weekly_stores.sql new file mode 100644 index 0000000..fee746a --- /dev/null +++ b/integration/snowflake/data/sql/preparation/create_weekly_stores.sql @@ -0,0 +1,67 @@ +-- Create store-week combinations for weekly sales forecasting +-- +-- This table creates the base population for getML: one row per store per week. +-- reference_date is the Monday (week start) derived from DATE_TRUNC('week', ordered_at). +-- +-- Filtering logic: +-- - Week must be >= store's opened_at date (store existed) +-- - Week must be < last_order_week (exclude incomplete final week) +-- +-- Boolean flags for data quality filtering: +-- - is_full_week_after_opening: Store had a full week of operation before this week +-- - has_order_activity: Store has order data spanning this week +-- - has_min_history: At least 7 days since store opened +CREATE TABLE PREPARED.weekly_stores AS +WITH store_activity AS ( + SELECT + s.id as store_id, + s.name as store_name, + s.opened_at, + DATE_TRUNC('week', s.opened_at) + INTERVAL '7 days' as first_full_week, + MIN(o.ordered_at) as first_order_date, + MAX(o.ordered_at) as last_order_date, + DATE_TRUNC('week', MIN(o.ordered_at)) as first_order_week, + DATE_TRUNC('week', MAX(o.ordered_at)) as last_order_week + FROM RAW.raw_stores s + LEFT JOIN RAW.raw_orders o ON o.store_id = s.id + GROUP BY s.id, s.name, s.opened_at +), + +all_weeks AS ( + SELECT DISTINCT + DATE_TRUNC('week', ordered_at) as reference_date + FROM RAW.raw_orders + WHERE ordered_at IS NOT NULL +), + +store_weeks AS ( + SELECT + sa.store_id, + sa.store_name, + w.reference_date, + sa.opened_at, + sa.first_full_week, + sa.first_order_week, + sa.last_order_week + FROM store_activity sa + CROSS JOIN all_weeks w + WHERE w.reference_date >= sa.opened_at + AND w.reference_date < sa.last_order_week +) + +SELECT + ROW_NUMBER() OVER (ORDER BY reference_date, store_id) as snapshot_id, + store_id, + store_name, + reference_date, + EXTRACT(year FROM reference_date) as year, + EXTRACT(month FROM reference_date) as month, + EXTRACT(week FROM reference_date) as week_number, + DATEDIFF('day', opened_at, reference_date) as days_since_open, + reference_date >= first_full_week as is_full_week_after_opening, + first_order_week IS NOT NULL + AND reference_date >= first_order_week + AND reference_date < last_order_week as has_order_activity, + DATEDIFF('day', opened_at, reference_date) >= 7 as has_min_history +FROM store_weeks +ORDER BY reference_date, store_id; diff --git a/integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql b/integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql deleted file mode 100644 index 7c8f0bb..0000000 --- a/integration/snowflake/data/sql/preparation/drop_population_weekly_by_store.sql +++ /dev/null @@ -1,2 +0,0 @@ --- Drop the population table if it exists -DROP TABLE IF EXISTS PREPARED.population_weekly_by_store diff --git a/integration/snowflake/data/sql/preparation/drop_population_with_target.sql b/integration/snowflake/data/sql/preparation/drop_population_with_target.sql deleted file mode 100644 index 8c29d4d..0000000 --- a/integration/snowflake/data/sql/preparation/drop_population_with_target.sql +++ /dev/null @@ -1,2 +0,0 @@ --- Drop the population table with target if it exists -DROP TABLE IF EXISTS PREPARED.population_weekly_by_store_with_target diff --git a/integration/snowflake/data/sql/preparation/drop_weekly_stores.sql b/integration/snowflake/data/sql/preparation/drop_weekly_stores.sql new file mode 100644 index 0000000..58d22bb --- /dev/null +++ b/integration/snowflake/data/sql/preparation/drop_weekly_stores.sql @@ -0,0 +1,2 @@ +-- Drop the weekly_stores table if it exists +DROP TABLE IF EXISTS PREPARED.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql b/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql index 39a7809..b13234b 100644 --- a/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql +++ b/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql @@ -1,11 +1,10 @@ -- Most recent snapshots for a specific store SELECT snapshot_id, - snapshot_time, - prediction_week_start, + reference_date, next_week_sales, next_week_orders FROM PREPARED.population_weekly_by_store_with_target WHERE store_name = '{store_name}' -ORDER BY snapshot_time DESC +ORDER BY reference_date DESC LIMIT 3 diff --git a/integration/snowflake/data/sql/preparation/sample_data_by_store.sql b/integration/snowflake/data/sql/preparation/sample_data_by_store.sql index 0ddffea..ceaa47d 100644 --- a/integration/snowflake/data/sql/preparation/sample_data_by_store.sql +++ b/integration/snowflake/data/sql/preparation/sample_data_by_store.sql @@ -1,11 +1,10 @@ -- Sample data - first 5 snapshots for a specific store SELECT snapshot_id, - snapshot_time, - prediction_week_start, + reference_date, next_week_sales, next_week_orders FROM PREPARED.population_weekly_by_store_with_target WHERE store_name = '{store_name}' -ORDER BY snapshot_time +ORDER BY reference_date LIMIT 5 diff --git a/integration/snowflake/data/sql/preparation/snapshots_per_store.sql b/integration/snowflake/data/sql/preparation/snapshots_per_store.sql index 97f105c..2dd43af 100644 --- a/integration/snowflake/data/sql/preparation/snapshots_per_store.sql +++ b/integration/snowflake/data/sql/preparation/snapshots_per_store.sql @@ -2,8 +2,8 @@ SELECT store_name, COUNT(*) as num_snapshots, - MIN(snapshot_time) as first_snapshot, - MAX(snapshot_time) as last_snapshot -FROM PREPARED.population_weekly_by_store + MIN(reference_date) as first_snapshot, + MAX(reference_date) as last_snapshot +FROM PREPARED.weekly_stores GROUP BY store_name ORDER BY first_snapshot diff --git a/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md b/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md index fe79729..391585b 100644 --- a/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md +++ b/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md @@ -2,22 +2,30 @@ **This guide explains how to use the prepared weekly sales forecasting data with getML for store-level predictions.** -The data preparation pipeline creates population tables with weekly snapshots (Sunday nights) per store, calculating the target as total sales for the following week. Only full weeks where the store was open for the entire week are included. +The data preparation pipeline creates: + +- `weekly_stores` (TABLE): Store-week combinations with `reference_date` (Monday week start) +- `population_weekly_by_store_with_target` (VIEW): Adds target column (next week's sales) --- -## 1. Population Table +## 1. Population View -**Table name:** `population_weekly_by_store_with_target` +**View name:** `population_weekly_by_store_with_target` | Column | Description | |--------|-------------| | `snapshot_id` | Unique identifier for each prediction point | | `store_id` | The store being predicted | | `store_name` | Store name (for reference) | -| `snapshot_time` | The time of prediction (Sunday 23:59:59) | -| `prediction_week_start` | Start of the week being predicted | -| `prediction_week_end` | End of the week being predicted | +| `reference_date` | Monday (week start) from DATE_TRUNC('week', ...) | +| `year`, `month`, `week_number` | Temporal components | +| `days_since_open` | Days since store opened | +| `is_full_week_after_opening` | True if store had a full week of operation before this week | +| `has_order_activity` | True if store has order data spanning this week | +| `has_min_history` | True if at least 7 days since store opened | +| `next_week_sales` | Target: sum of sales for 7-day window starting at reference_date | +| `next_week_orders` | Number of orders in the target window | --- @@ -25,17 +33,18 @@ The data preparation pipeline creates population tables with weekly snapshots (S **Column name:** `next_week_sales` -- This is the sum of sales for the following 7 days **for that specific store** +- Sum of order_total for the 7-day window: [reference_date, reference_date + 7 days) - Unit: dollars (already divided by 100) --- ## 3. Time Column -**Column name:** `snapshot_time` +**Column name:** `reference_date` - Mark this as `time_stamp` role in getML -- Ensures no data leakage (only past data used for prediction) +- Represents Monday 00:00:00 (week start) +- Ensures no data leakage (only past data used for prediction via `ordered_at < reference_date`) --- @@ -52,11 +61,11 @@ The data preparation pipeline creates population tables with weekly snapshots (S | Table | Join Condition | |-------|----------------| | `raw_stores` | `store.id = population.store_id` | -| `raw_orders` | `order.store_id = population.store_id AND order.ordered_at <= snapshot_time` | +| `raw_orders` | `order.store_id = population.store_id AND order.ordered_at < reference_date` | | `raw_customers` | Join through orders | | `raw_items` | Join through orders | | `raw_products` | Join through items | -| `raw_tweets` | `tweet.tweeted_at <= snapshot_time` (if relevant to stores) | +| `raw_tweets` | `tweet.tweeted_at < reference_date` (if relevant to stores) | --- @@ -74,10 +83,16 @@ population = getml.DataFrame.from_db( # Set roles population.set_role("snapshot_id", getml.data.roles.join_key) population.set_role("store_id", getml.data.roles.join_key) -population.set_role("snapshot_time", getml.data.roles.time_stamp) +population.set_role("reference_date", getml.data.roles.time_stamp) population.set_role("next_week_sales", getml.data.roles.target) population.set_role("store_name", getml.data.roles.categorical) +# Boolean flags can be used as features or for filtering +population.set_role( + ["is_full_week_after_opening", "has_order_activity", "has_min_history"], + getml.data.roles.categorical +) + # Load peripheral tables stores = getml.DataFrame.from_db(name="stores", table_name="raw_stores") stores.set_role("id", getml.data.roles.join_key) @@ -114,3 +129,16 @@ predictions = pipe.predict(container.test) - **Feature scope:** Features will be store-specific (that store's history) - **Analysis capability:** Can compare store performance, identify store-specific patterns - **Training data:** More training examples (6 stores × N weeks instead of just N weeks) + +--- + +## 8. Data Quality Flags + +The boolean flags can be used to filter training data: + +```python +# Only use rows with sufficient history +filtered = population[population["has_min_history"] == True] + +# Or use as features - getML can learn from these patterns +``` diff --git a/integration/snowflake/pyproject.toml b/integration/snowflake/pyproject.toml index a12e17b..9ceb647 100644 --- a/integration/snowflake/pyproject.toml +++ b/integration/snowflake/pyproject.toml @@ -28,6 +28,10 @@ readme = "README.md" requires-python = ">=3.12" dependencies = [ + "fastparquet>=2024.11.0", + "jafgen>=0.4.14", + "pandas>=2.3.3", + "pyarrow>=22.0.0", "pydantic-settings>=2.12.0", "snowflake-connector-python>=4.1.0", "snowflake-snowpark-python>=1.42.0", @@ -39,6 +43,7 @@ dev = [ "basedpyright~=1.28.4", "pytest~=8.0.0", "pytest-cov>=6.2.1", + "pytest-dependency>=0.6.0", ] [tool.uv] diff --git a/integration/snowflake/tests/cleanup_test_resources.py b/integration/snowflake/tests/cleanup_test_resources.py new file mode 100644 index 0000000..10260de --- /dev/null +++ b/integration/snowflake/tests/cleanup_test_resources.py @@ -0,0 +1,120 @@ +"""Cleanup utility for test resources in Snowflake. + +Drops test databases (JAFFLE_SHOP_TEST_*) and test schemas (TEST_*) created +by integration tests. Can be run standalone or imported by test fixtures for +pre/post-test cleanup. + +Usage: + # Standalone execution + uv run python tests/cleanup_test_resources.py + + # Import in fixtures + from tests.cleanup_test_resources import cleanup_test_databases + cleanup_test_databases(session) +""" + +# ruff: noqa: G004, S608, PLC0415 + +import logging +import sys +from pathlib import Path + +from snowflake.snowpark import Row, Session + +logger: logging.Logger = logging.getLogger(__name__) + +TEST_DATABASE_PREFIX = "JAFFLE_SHOP_TEST_" +TEST_SCHEMA_PREFIX = "TEST_" + + +def cleanup_test_databases(session: Session) -> list[str]: + """Drop all databases with JAFFLE_SHOP_TEST_ prefix. + + Args: + session: Active Snowflake session with appropriate privileges. + + Returns: + List of dropped database names. + """ + result: list[Row] = session.sql(f""" + SELECT DATABASE_NAME + FROM INFORMATION_SCHEMA.DATABASES + WHERE DATABASE_NAME LIKE '{TEST_DATABASE_PREFIX}%' + """).collect() + + dropped_databases: list[str] = [] + + for row in result: + db_name = str(row["DATABASE_NAME"]) # pyright: ignore[reportUnknownArgumentType] + _ = session.sql(f"DROP DATABASE IF EXISTS {db_name} CASCADE").collect() + dropped_databases.append(db_name) + logger.info(f"Dropped database: {db_name}") + + return dropped_databases + + +def cleanup_test_schemas(session: Session, database: str | None = None) -> list[str]: + """Drop all schemas with TEST_ prefix in the specified database. + + Args: + session: Active Snowflake session. + database: Database to clean. If None, uses session's current database. + + Returns: + List of dropped schema names. + """ + if database: + _ = session.sql(f"USE DATABASE {database}").collect() + + result: list[Row] = session.sql(f""" + SELECT SCHEMA_NAME + FROM INFORMATION_SCHEMA.SCHEMATA + WHERE SCHEMA_NAME LIKE '{TEST_SCHEMA_PREFIX}%' + """).collect() + + dropped_schemas: list[str] = [] + + for row in result: + schema_name = str(row["SCHEMA_NAME"]) # pyright: ignore[reportUnknownArgumentType] + _ = session.sql(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE").collect() + dropped_schemas.append(schema_name) + logger.info(f"Dropped schema: {schema_name}") + + return dropped_schemas + + +def main() -> None: + """Run cleanup as standalone script.""" + sys.path.insert(0, str(Path(__file__).parent.parent)) + + from settings import SnowflakeSettings + from snowflake_session import create_session + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + ) + + logger.info("Starting test resource cleanup...") + + settings = SnowflakeSettings.from_env() + + with create_session(settings) as session: + # Clean up test databases first + dropped_dbs: list[str] = cleanup_test_databases(session) + if dropped_dbs: + logger.info(f"Dropped {len(dropped_dbs)} test database(s).") + + # Also clean up any stray test schemas in the main database + dropped_schemas: list[str] = cleanup_test_schemas(session, settings.database) + if dropped_schemas: + logger.info(f"Dropped {len(dropped_schemas)} test schema(s).") + + if not dropped_dbs and not dropped_schemas: + logger.info("No test resources found to clean up.") + else: + logger.info("Cleanup complete.") + + +if __name__ == "__main__": + main() diff --git a/integration/snowflake/tests/cleanup_test_schemas.py b/integration/snowflake/tests/cleanup_test_schemas.py deleted file mode 100644 index 3ebacfd..0000000 --- a/integration/snowflake/tests/cleanup_test_schemas.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Cleanup utility for test schemas in Snowflake. - -Drops all TEST_* schemas created by integration tests. Can be run standalone -or imported by test fixtures for pre/post-test cleanup. - -Usage: - # Standalone execution - uv run python tests/cleanup_test_schemas.py - - # Import in fixtures - from tests.cleanup_test_schemas import cleanup_test_schemas - cleanup_test_schemas(session) -""" - -# ruff: noqa: G004, S608, PLC0415 - -import logging -import sys -from pathlib import Path - -from snowflake.snowpark import Row, Session - -logger: logging.Logger = logging.getLogger(__name__) - -TEST_SCHEMA_PREFIX = "TEST_" - - -def cleanup_test_schemas(session: Session, database: str | None = None) -> list[str]: - """Drop all schemas with TEST_ prefix in the specified database. - - Args: - session: Active Snowflake session. - database: Database to clean. If None, uses session's current database. - - Returns: - List of dropped schema names. - """ - if database: - _ = session.sql(f"USE DATABASE {database}").collect() - - # Get all schemas with TEST_ prefix (prefix is a constant, not user input) - result: list[Row] = session.sql(f""" - SELECT SCHEMA_NAME - FROM INFORMATION_SCHEMA.SCHEMATA - WHERE SCHEMA_NAME LIKE '{TEST_SCHEMA_PREFIX}%' - """).collect() - - dropped_schemas: list[str] = [] - - for row in result: - schema_name = str(row["SCHEMA_NAME"]) # pyright: ignore[reportUnknownArgumentType] - _ = session.sql(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE").collect() - dropped_schemas.append(schema_name) - logger.info(f"Dropped schema: {schema_name}") - - return dropped_schemas - - -def main() -> None: - """Run cleanup as standalone script.""" - # Add project root to path for imports when run standalone - sys.path.insert(0, str(Path(__file__).parent.parent)) - - from settings import SnowflakeSettings - from snowflake_session import create_session - - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(levelname)s - %(message)s", - ) - - logger.info("Starting test schema cleanup...") - - settings = SnowflakeSettings.from_env() - - with create_session(settings) as session: - dropped: list[str] = cleanup_test_schemas(session, settings.database) - - if dropped: - logger.info(f"Cleanup complete. Dropped {len(dropped)} test schema(s).") - else: - logger.info("No test schemas found to clean up.") - - -if __name__ == "__main__": - main() diff --git a/integration/snowflake/tests/integration/conftest.py b/integration/snowflake/tests/integration/conftest.py index 38a79d8..e95ea7f 100644 --- a/integration/snowflake/tests/integration/conftest.py +++ b/integration/snowflake/tests/integration/conftest.py @@ -1,9 +1,12 @@ """Fixtures for integration tests requiring real Snowflake connections. Provides session-scoped fixtures that: -1. Run cleanup of stale TEST_* schemas before tests -2. Create isolated schemas with TEST_{uuid} prefix for test isolation -3. Clean up test schemas after the test session completes +1. Run cleanup of stale JAFFLE_SHOP_TEST_* databases before tests +2. Create an isolated test database (JAFFLE_SHOP_TEST_{uuid}) for test isolation +3. Clean up test database after the test session completes + +Tests use the actual RAW and PREPARED schemas within the isolated test database, +allowing the real ingestion and preparation functions to run without modification. """ # ruff: noqa: G004 @@ -24,7 +27,7 @@ from settings import SnowflakeSettings from snowflake_session import create_session -from tests.cleanup_test_schemas import cleanup_test_schemas +from tests.cleanup_test_resources import cleanup_test_databases logger = logging.getLogger(__name__) @@ -45,8 +48,8 @@ def _snowflake_credentials_available() -> bool: def integration_test_id() -> str: """Generate unique identifier for this test session. - Used to create isolated schemas that don't conflict with - parallel test runs. + Used to create an isolated test database that doesn't conflict + with parallel test runs. """ return uuid.uuid4().hex[:8].upper() @@ -67,60 +70,48 @@ def snowflake_settings() -> SnowflakeSettings: @pytest.fixture(scope="session") -def snowflake_session( +def test_database( snowflake_settings: SnowflakeSettings, integration_test_id: str, -) -> Generator[Session, None, None]: - """Create a Snowflake session for integration tests. +) -> Generator[str, None, None]: + """Create an isolated test database for this test session. - Performs pre-test cleanup of stale TEST_* schemas, then yields - the session for test use. Post-test cleanup removes schemas - created during this test session. + Creates JAFFLE_SHOP_TEST_{uuid} database that is cleaned up after tests. + This provides complete isolation from production data. """ + db_name = f"JAFFLE_SHOP_TEST_{integration_test_id}" + with create_session(snowflake_settings) as session: - # Pre-test cleanup: remove any stale TEST_* schemas from previous runs - logger.info("Running pre-test cleanup of stale test schemas...") - dropped = cleanup_test_schemas(session, snowflake_settings.database) + # Pre-test cleanup: remove any stale test databases from previous runs + logger.info("Running pre-test cleanup of stale test databases...") + dropped = cleanup_test_databases(session) if dropped: - logger.info(f"Pre-test cleanup dropped {len(dropped)} stale schema(s)") - - yield session + logger.info(f"Pre-test cleanup dropped {len(dropped)} stale database(s)") - # Post-test cleanup: remove schemas created during this test session - logger.info(f"Running post-test cleanup for test session {integration_test_id}") - _ = cleanup_test_schemas(session, snowflake_settings.database) + # Create the test database + logger.info(f"Creating test database: {db_name}") + _ = session.sql(f"CREATE DATABASE IF NOT EXISTS {db_name}").collect() + yield db_name -@pytest.fixture(scope="session") -def test_raw_schema( - snowflake_session: Session, - integration_test_id: str, -) -> str: - """Create an isolated RAW schema for testing data ingestion. - - Creates TEST_RAW_{uuid} schema that is cleaned up after tests. - """ - schema_name = f"TEST_RAW_{integration_test_id}" - - logger.info(f"Creating test RAW schema: {schema_name}") - _ = snowflake_session.sql(f"CREATE SCHEMA IF NOT EXISTS {schema_name}").collect() - _ = snowflake_session.sql(f"USE SCHEMA {schema_name}").collect() - - return schema_name + # Post-test cleanup: drop the test database + logger.info(f"Dropping test database: {db_name}") + _ = session.sql(f"DROP DATABASE IF EXISTS {db_name} CASCADE").collect() @pytest.fixture(scope="session") -def test_prepared_schema( - snowflake_session: Session, - integration_test_id: str, -) -> str: - """Create an isolated PREPARED schema for testing data preparation. +def snowflake_session( + snowflake_settings: SnowflakeSettings, + test_database: str, +) -> Generator[Session, None, None]: + """Create a Snowflake session for integration tests using the test database. - Creates TEST_PREPARED_{uuid} schema that is cleaned up after tests. + The session is configured to use the isolated test database where RAW and + PREPARED schemas can be created without affecting production data. """ - schema_name = f"TEST_PREPARED_{integration_test_id}" - - logger.info(f"Creating test PREPARED schema: {schema_name}") - _ = snowflake_session.sql(f"CREATE SCHEMA IF NOT EXISTS {schema_name}").collect() + with create_session(snowflake_settings) as session: + # Switch to the test database + logger.info(f"Using test database: {test_database}") + _ = session.sql(f"USE DATABASE {test_database}").collect() - return schema_name + yield session diff --git a/integration/snowflake/tests/integration/test_pipeline.py b/integration/snowflake/tests/integration/test_pipeline.py index 31bfb78..2a1b5b4 100644 --- a/integration/snowflake/tests/integration/test_pipeline.py +++ b/integration/snowflake/tests/integration/test_pipeline.py @@ -6,7 +6,7 @@ Run with: uv run pytest tests/integration/ -m integration """ -# ruff: noqa: G004, S608, E501 +# ruff: noqa: E501 import logging import sys @@ -18,11 +18,8 @@ # Add project root to path for imports sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -from data.ingestion import ( - JAFFLE_SHOP_TABLES, - S3_BUCKET_URL, - STAGE_NAME, -) +from data.ingestion import JAFFLE_SHOP_TABLES, load_jaffle_shop_data +from data.preparation import prepare_weekly_sales_by_store logger = logging.getLogger(__name__) @@ -32,24 +29,13 @@ class TestDataIngestion: """Integration tests for S3 to Snowflake data loading.""" + @pytest.mark.dependency(name="data_ingestion") def test_load_jaffle_shop_data_creates_tables_with_rows( self, snowflake_session: Session, - test_raw_schema: str, ) -> None: """Verify all Jaffle Shop tables are created with data.""" - # Ensure we're using the test schema - _ = snowflake_session.sql(f"USE SCHEMA {test_raw_schema}").collect() - - # Create the stage in test schema - _ = snowflake_session.sql(f""" - CREATE OR REPLACE STAGE {STAGE_NAME} - URL = '{S3_BUCKET_URL}' - FILE_FORMAT = (TYPE = CSV FIELD_OPTIONALLY_ENCLOSED_BY = '"' SKIP_HEADER = 1) - """).collect() - - # Load data using the pipeline function (modified to use current schema) - results = self._load_tables_to_test_schema(snowflake_session, test_raw_schema) + results = load_jaffle_shop_data(snowflake_session) # Verify all tables were loaded assert set(results.keys()) == set(JAFFLE_SHOP_TABLES.keys()) @@ -58,49 +44,16 @@ def test_load_jaffle_shop_data_creates_tables_with_rows( for table_name, row_count in results.items(): assert row_count > 0, f"Table {table_name} has 0 rows" - def _load_tables_to_test_schema( - self, - session: Session, - schema_name: str, - ) -> dict[str, int]: - """Load tables into the test schema. - - Simplified version of load_jaffle_shop_data for test isolation. - """ - results: dict[str, int] = {} - - for table_name, schema_def in JAFFLE_SHOP_TABLES.items(): - _ = session.sql(f""" - CREATE OR REPLACE TABLE {schema_name}.{table_name} {schema_def} - """).collect() - - _ = session.sql(f""" - COPY INTO {schema_name}.{table_name} - FROM @{schema_name}.{STAGE_NAME}/{table_name}.csv - FORCE = FALSE - """).collect() - - count_result: list[Row] = session.sql(f""" - SELECT COUNT(*) as count FROM {schema_name}.{table_name} - """).collect() - - row_count = int(count_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] - results[table_name] = row_count - logger.info(f"Loaded {table_name}: {row_count} rows") - - return results - + @pytest.mark.dependency(depends=["data_ingestion"]) def test_tables_have_expected_columns( self, snowflake_session: Session, - test_raw_schema: str, ) -> None: """Verify tables have expected column structure.""" - # Check raw_orders has expected columns - result: list[Row] = snowflake_session.sql(f""" + result: list[Row] = snowflake_session.sql(""" SELECT COLUMN_NAME FROM INFORMATION_SCHEMA.COLUMNS - WHERE TABLE_SCHEMA = '{test_raw_schema}' + WHERE TABLE_SCHEMA = 'RAW' AND TABLE_NAME = 'RAW_ORDERS' ORDER BY ORDINAL_POSITION """).collect() @@ -122,56 +75,137 @@ def test_tables_have_expected_columns( class TestDataPreparation: """Integration tests for data preparation pipeline.""" - def test_prepare_weekly_sales_creates_population_tables( + @pytest.mark.dependency(name="data_preparation", depends=["data_ingestion"]) + def test_prepare_weekly_sales_creates_weekly_stores_table( self, snowflake_session: Session, - test_raw_schema: str, - test_prepared_schema: str, ) -> None: - """Verify preparation creates population tables with correct structure.""" - # First ensure data is loaded (reuse from ingestion test if already run) - count_result: list[Row] = snowflake_session.sql(f""" - SELECT COUNT(*) as count FROM {test_raw_schema}.raw_orders + """Verify preparation creates weekly_stores table with data.""" + prepare_weekly_sales_by_store(snowflake_session) + + # Verify table was created with data + result: list[Row] = snowflake_session.sql(""" + SELECT COUNT(*) as count FROM PREPARED.weekly_stores """).collect() - if int(count_result[0]["COUNT"]) == 0: # pyright: ignore[reportArgumentType] - pytest.skip("Data ingestion test must run first") + row_count = int(result[0]["COUNT"]) # pyright: ignore[reportArgumentType] + assert row_count > 0, "weekly_stores table should have data" - # Create population table in test prepared schema - _ = snowflake_session.sql(f""" - CREATE OR REPLACE TABLE {test_prepared_schema}.population_weekly_by_store AS - SELECT - ROW_NUMBER() OVER (ORDER BY s.name, DATE_TRUNC('week', o.ordered_at)) as id, - s.id as store_id, - s.name as store_name, - DATE_TRUNC('week', o.ordered_at) as week_start, - DATEADD('day', 6, DATE_TRUNC('week', o.ordered_at)) as snapshot_date, - SUM(o.order_total) / 100.0 as weekly_sales, - COUNT(*) as weekly_orders - FROM {test_raw_schema}.raw_stores s - JOIN {test_raw_schema}.raw_orders o ON s.id = o.store_id - GROUP BY s.id, s.name, DATE_TRUNC('week', o.ordered_at) + @pytest.mark.dependency(depends=["data_preparation"]) + def test_weekly_stores_has_multiple_stores( + self, + snowflake_session: Session, + ) -> None: + """Verify weekly_stores table contains data for multiple stores.""" + result: list[Row] = snowflake_session.sql(""" + SELECT COUNT(DISTINCT store_id) as store_count + FROM PREPARED.weekly_stores """).collect() - # Verify table was created with data - verification_result: list[Row] = snowflake_session.sql(f""" - SELECT COUNT(*) as count - FROM {test_prepared_schema}.population_weekly_by_store + store_count = int(result[0]["STORE_COUNT"]) # pyright: ignore[reportArgumentType] + assert store_count > 1, "weekly_stores should have multiple stores" + + @pytest.mark.dependency(depends=["data_preparation"]) + def test_weekly_stores_has_correct_columns( + self, + snowflake_session: Session, + ) -> None: + """Verify weekly_stores table has the expected column structure.""" + result: list[Row] = snowflake_session.sql(""" + SELECT COLUMN_NAME + FROM INFORMATION_SCHEMA.COLUMNS + WHERE TABLE_SCHEMA = 'PREPARED' + AND TABLE_NAME = 'WEEKLY_STORES' + ORDER BY ORDINAL_POSITION + """).collect() + + column_names = [str(row["COLUMN_NAME"]) for row in result] # pyright: ignore[reportUnknownArgumentType] + + expected_columns = [ + "SNAPSHOT_ID", + "STORE_ID", + "STORE_NAME", + "REFERENCE_DATE", + "YEAR", + "MONTH", + "WEEK_NUMBER", + "DAYS_SINCE_OPEN", + "IS_FULL_WEEK_AFTER_OPENING", + "HAS_ORDER_ACTIVITY", + "HAS_MIN_HISTORY", + ] + assert column_names == expected_columns + + @pytest.mark.dependency(depends=["data_preparation"]) + def test_target_view_exists_with_sales_columns( + self, + snowflake_session: Session, + ) -> None: + """Verify population_weekly_by_store_with_target view exists with target columns.""" + result: list[Row] = snowflake_session.sql(""" + SELECT COLUMN_NAME + FROM INFORMATION_SCHEMA.COLUMNS + WHERE TABLE_SCHEMA = 'PREPARED' + AND TABLE_NAME = 'POPULATION_WEEKLY_BY_STORE_WITH_TARGET' + ORDER BY ORDINAL_POSITION """).collect() - row_count = int(verification_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] - assert row_count > 0, "Population table should have data" + column_names = [str(row["COLUMN_NAME"]) for row in result] # pyright: ignore[reportUnknownArgumentType] + + # Should have all weekly_stores columns plus target columns + assert "NEXT_WEEK_SALES" in column_names, "View should have NEXT_WEEK_SALES" + assert "NEXT_WEEK_ORDERS" in column_names, "View should have NEXT_WEEK_ORDERS" + assert "REFERENCE_DATE" in column_names, "View should have REFERENCE_DATE" - def test_population_table_has_multiple_stores( + @pytest.mark.dependency(depends=["data_preparation"]) + def test_target_view_has_valid_sales_data( self, snowflake_session: Session, - test_prepared_schema: str, ) -> None: - """Verify population table contains data for multiple stores.""" - result: list[Row] = snowflake_session.sql(f""" - SELECT COUNT(DISTINCT store_id) as store_count - FROM {test_prepared_schema}.population_weekly_by_store + """Verify the target view calculates sales correctly.""" + result: list[Row] = snowflake_session.sql(""" + SELECT + COUNT(*) as total_rows, + SUM(CASE WHEN next_week_sales > 0 THEN 1 ELSE 0 END) as rows_with_sales, + AVG(next_week_sales) as avg_sales + FROM PREPARED.population_weekly_by_store_with_target """).collect() - store_count = int(result[0]["STORE_COUNT"]) # pyright: ignore[reportArgumentType] - assert store_count > 1, "Population table should have multiple stores" + total_rows = int(result[0]["TOTAL_ROWS"]) # pyright: ignore[reportArgumentType] + rows_with_sales = int(result[0]["ROWS_WITH_SALES"]) # pyright: ignore[reportArgumentType] + avg_sales = float(result[0]["AVG_SALES"]) # pyright: ignore[reportArgumentType] + + assert total_rows > 0, "View should have rows" + assert rows_with_sales > 0, "Some rows should have sales > 0" + assert avg_sales > 0, "Average sales should be positive" + + @pytest.mark.dependency(depends=["data_preparation"]) + def test_boolean_flags_are_calculated( + self, + snowflake_session: Session, + ) -> None: + """Verify boolean flags are properly calculated in weekly_stores.""" + result: list[Row] = snowflake_session.sql(""" + SELECT + COUNT(*) as total, + SUM(CASE WHEN is_full_week_after_opening THEN 1 ELSE 0 END) as full_week_count, + SUM(CASE WHEN has_order_activity THEN 1 ELSE 0 END) as activity_count, + SUM(CASE WHEN has_min_history THEN 1 ELSE 0 END) as min_history_count + FROM PREPARED.weekly_stores + """).collect() + + total = int(result[0]["TOTAL"]) # pyright: ignore[reportArgumentType] + full_week_count = int(result[0]["FULL_WEEK_COUNT"]) # pyright: ignore[reportArgumentType] + activity_count = int(result[0]["ACTIVITY_COUNT"]) # pyright: ignore[reportArgumentType] + min_history_count = int(result[0]["MIN_HISTORY_COUNT"]) # pyright: ignore[reportArgumentType] + + # All flags should have some TRUE values (not all zeros) + assert full_week_count > 0, ( + "Some rows should have is_full_week_after_opening=TRUE" + ) + assert activity_count > 0, "Some rows should have has_order_activity=TRUE" + assert min_history_count > 0, "Some rows should have has_min_history=TRUE" + + # Not 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--- .../data => }/GENERATE_JAFFLE_SHOP_PARQUET.md | 0 .../convert_jaffle_csv_to_parquet.py | 0 integration/snowflake/bootstrap.py | 270 ++- integration/snowflake/data/__init__.py | 53 +- integration/snowflake/data/ingestion.py | 404 +++- integration/snowflake/data/preparation.py | 417 ++-- .../create_storage_integration_gcs.sql | 7 + .../describe_storage_integration.sql | 3 + .../validate_storage_integration.sql | 9 + .../data/sql/ingestion/copy_parquet.sql | 6 + .../sql/ingestion/create_external_stage.sql | 9 - .../ingestion/create_parquet_file_format.sql | 5 + .../data/sql/ingestion/create_raw_schema.sql | 2 - .../data/sql/ingestion/create_schema.sql | 2 + .../data/sql/ingestion/create_stage_gcs.sql | 6 + .../sql/ingestion/create_stage_s3_public.sql | 5 + .../create_storage_integration_gcs.sql | 7 + .../describe_storage_integration.sql | 3 + .../sql/ingestion/infer_and_create_table.sql | 12 + .../validate_storage_integration.sql | 3 + .../data/sql/preparation/analyze_stores.sql | 4 +- .../data/sql/preparation/calculate_target.sql | 8 +- .../data/sql/preparation/count_snapshots.sql | 2 +- .../data/sql/preparation/count_stores.sql | 2 +- .../preparation/create_prepared_schema.sql | 2 - .../data/sql/preparation/create_schema.sql | 2 + .../sql/preparation/create_weekly_stores.sql | 10 +- .../sql/preparation/data_quality_check.sql | 2 +- .../sql/preparation/drop_weekly_stores.sql | 2 +- .../sql/preparation/overall_statistics.sql | 2 +- .../preparation/recent_snapshots_by_store.sql | 2 +- .../sql/preparation/sample_data_by_store.sql | 2 +- .../sql/preparation/snapshots_per_store.sql | 2 +- .../data/sql/preparation/store_statistics.sql | 2 +- .../preparation/zero_sales_investigation.sql | 2 +- .../docs/GCS_STORAGE_INTEGRATION_SETUP.md | 187 ++ .../GETML_WEEKLY_SALES_DATA_PREPARATION.md | 36 +- integration/snowflake/pyproject.toml | 8 +- .../{data/_sql_loader.py => sql_loader.py} | 14 +- .../snowflake/tests/integration/conftest.py | 104 +- .../tests/integration/test_pipeline.py | 29 +- integration/snowflake/tests/test_ingestion.py | 258 ++- .../snowflake/tests/test_preparation.py | 138 +- .../{test_data_init.py => test_sql_loader.py} | 31 +- integration/snowflake/uv.lock | 1769 +++++++++++++++-- 45 files changed, 3275 insertions(+), 568 deletions(-) rename integration/{snowflake/data => }/GENERATE_JAFFLE_SHOP_PARQUET.md (100%) rename integration/{snowflake/data => }/convert_jaffle_csv_to_parquet.py (100%) create mode 100644 integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql create mode 100644 integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql create mode 100644 integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql create mode 100644 integration/snowflake/data/sql/ingestion/copy_parquet.sql delete mode 100644 integration/snowflake/data/sql/ingestion/create_external_stage.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_parquet_file_format.sql delete mode 100644 integration/snowflake/data/sql/ingestion/create_raw_schema.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_schema.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_stage_gcs.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_stage_s3_public.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql create mode 100644 integration/snowflake/data/sql/ingestion/describe_storage_integration.sql create mode 100644 integration/snowflake/data/sql/ingestion/infer_and_create_table.sql create mode 100644 integration/snowflake/data/sql/ingestion/validate_storage_integration.sql delete mode 100644 integration/snowflake/data/sql/preparation/create_prepared_schema.sql create mode 100644 integration/snowflake/data/sql/preparation/create_schema.sql create mode 100644 integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md rename integration/snowflake/{data/_sql_loader.py => sql_loader.py} (75%) rename integration/snowflake/tests/{test_data_init.py => test_sql_loader.py} (57%) diff --git a/integration/snowflake/data/GENERATE_JAFFLE_SHOP_PARQUET.md b/integration/GENERATE_JAFFLE_SHOP_PARQUET.md similarity index 100% rename from integration/snowflake/data/GENERATE_JAFFLE_SHOP_PARQUET.md rename to integration/GENERATE_JAFFLE_SHOP_PARQUET.md diff --git a/integration/snowflake/data/convert_jaffle_csv_to_parquet.py b/integration/convert_jaffle_csv_to_parquet.py similarity index 100% rename from integration/snowflake/data/convert_jaffle_csv_to_parquet.py rename to integration/convert_jaffle_csv_to_parquet.py diff --git a/integration/snowflake/bootstrap.py b/integration/snowflake/bootstrap.py index 0960dba..87fe0ff 100644 --- a/integration/snowflake/bootstrap.py +++ b/integration/snowflake/bootstrap.py @@ -1,34 +1,114 @@ """Bootstrap Snowflake infrastructure for the Jaffle Shop dataset. -Creates the required warehouse and database if they don't exist. -All operations are idempotent using CREATE ... IF NOT EXISTS. +Creates the required warehouse, database, and optionally GCS storage integration. +All operations are idempotent using CREATE ... IF NOT EXISTS / CREATE OR REPLACE. Usage example: - from settings import SnowflakeAdminSettings, SnowflakeSettings - from snowflake_session import create_admin_session + from settings import SnowflakeSettings + from snowflake_session import create_session from bootstrap import bootstrap_snowflake_infrastructure - admin_settings = SnowflakeAdminSettings.from_env() - full_settings = SnowflakeSettings.from_env() + settings = SnowflakeSettings.from_env() - with create_admin_session(admin_settings) as session: - bootstrap_snowflake_infrastructure(session, full_settings) + with create_session(settings) as session: + bootstrap_snowflake_infrastructure(session, settings) + +For GCS storage integration setup: + from bootstrap import setup_gcs_storage_integration + + with create_session(settings) as session: + info = setup_gcs_storage_integration(session) + # Follow printed instructions to grant IAM permissions in GCP """ -# ruff: noqa: G004 +# ruff: noqa: G004, T201 + +from __future__ import annotations import logging +from dataclasses import dataclass -from snowflake.snowpark import Session +from snowflake.snowpark import Row, Session -from data import load_sql from settings import SnowflakeSettings +from sql_loader import load_sql logger: logging.Logger = logging.getLogger(__name__) WAREHOUSE_SIZE = "X-SMALL" AUTO_SUSPEND_SECONDS = 60 +DEFAULT_GCS_ALLOWED_LOCATION = "gcs://static.getml.com/datasets/" +DEFAULT_INTEGRATION_NAME = "GETML_GCS_INTEGRATION" + + +class BootstrapError(Exception): + """Raised when bootstrap operations fail.""" + + +@dataclass +class StorageIntegrationInfo: + """Information about a GCS storage integration. + + Attributes: + name: Integration name. + enabled: Whether the integration is enabled. + allowed_locations: GCS locations the integration can access. + service_account: GCS service account to grant IAM permissions to. + """ + + name: str + enabled: bool + allowed_locations: str + service_account: str + + +# ============================================================================= +# Internal Helpers +# ============================================================================= + + +def _extract_bucket_name(gcs_url: str) -> str: + """Extract bucket name from GCS URL.""" + url = gcs_url.lower().replace("gcs://", "").replace("gs://", "") + return url.split("/")[0] + + +def _print_gcp_setup_guide( + info: StorageIntegrationInfo, + bucket_name: str, +) -> None: + """Print actionable GCP setup instructions after integration creation.""" + print(f""" +====================================================================== +GCS STORAGE INTEGRATION CREATED +====================================================================== + +Integration: {info.name} +Service Account: {info.service_account} + +---------------------------------------------------------------------- +REQUIRED: Grant IAM permissions in GCP +---------------------------------------------------------------------- + +Run this command to grant read access: + + gsutil iam ch \\ + serviceAccount:{info.service_account}:objectViewer \\ + gs://{bucket_name} + +---------------------------------------------------------------------- +Then retry the data loading operation. +---------------------------------------------------------------------- + +====================================================================== +""") + + +# ============================================================================= +# Core Bootstrap Functions +# ============================================================================= + def bootstrap_snowflake_infrastructure( session: Session, @@ -37,24 +117,24 @@ def bootstrap_snowflake_infrastructure( """Create Snowflake warehouse and database if they don't exist. Raises: - Exception: If warehouse or database creation fails. + BootstrapError: If warehouse or database creation fails. """ - _create_warehouse(session, settings.warehouse) - _create_database(session, settings.database) - logger.info("Snowflake infrastructure bootstrap complete") + _create_warehouse(session, warehouse_name=settings.warehouse) + _create_database(session, database_name=settings.database) + logger.info("✓ Snowflake infrastructure bootstrap complete") def _create_warehouse(session: Session, warehouse_name: str) -> None: """Create warehouse with X-SMALL size and auto-suspend enabled.""" logger.info(f"Creating warehouse '{warehouse_name}' if not exists...") - sql = load_sql( - "bootstrap/create_warehouse.sql", + sql: str = load_sql( + path="bootstrap/create_warehouse.sql", warehouse_name=warehouse_name, warehouse_size=WAREHOUSE_SIZE, auto_suspend_seconds=str(AUTO_SUSPEND_SECONDS), ) - _ = session.sql(sql).collect() + _ = session.sql(query=sql).collect() logger.info(f"✓ Warehouse '{warehouse_name}' ready") @@ -63,7 +143,157 @@ def _create_database(session: Session, database_name: str) -> None: """Create database if it doesn't exist.""" logger.info(f"Creating database '{database_name}' if not exists...") - sql = load_sql("bootstrap/create_database.sql", database_name=database_name) - _ = session.sql(sql).collect() + sql: str = load_sql( + path="bootstrap/create_database.sql", database_name=database_name + ) + _ = session.sql(query=sql).collect() logger.info(f"✓ Database '{database_name}' ready") + + +# ============================================================================= +# GCS Storage Integration Setup +# ============================================================================= + + +def setup_gcs_storage_integration( + session: Session, + integration_name: str = DEFAULT_INTEGRATION_NAME, + allowed_location: str = DEFAULT_GCS_ALLOWED_LOCATION, +) -> StorageIntegrationInfo: + """Create GCS storage integration and display required GCP setup commands. + + Creates the Snowflake storage integration for GCS access and prints + the gcloud commands needed to grant IAM permissions. + + Requires ACCOUNTADMIN role or CREATE INTEGRATION privilege. + + Args: + session: Active Snowflake Snowpark session. + integration_name: Name for the storage integration. + allowed_location: GCS path prefix to allow access to. + + Returns: + StorageIntegrationInfo with the service account for IAM setup. + + Raises: + BootstrapError: If integration creation fails. + """ + logger.info(f"Creating GCS storage integration '{integration_name}'...") + + try: + sql: str = load_sql( + path="bootstrap/create_storage_integration_gcs.sql", + integration_name=integration_name, + allowed_location=allowed_location, + ) + _ = session.sql(query=sql).collect() + except Exception as e: + raise BootstrapError( + f"Failed to create storage integration. " + f"Ensure you have ACCOUNTADMIN role. Error: {e}" + ) from e + + logger.info(f"✓ Storage integration '{integration_name}' created") + + info: StorageIntegrationInfo = get_storage_integration_info( + session, integration_name + ) + bucket_name: str = _extract_bucket_name(gcs_url=allowed_location) + _print_gcp_setup_guide(info, bucket_name) + + return info + + +def get_storage_integration_info( + session: Session, + integration_name: str = DEFAULT_INTEGRATION_NAME, +) -> StorageIntegrationInfo: + """Retrieve details about an existing storage integration. + + Args: + session: Active Snowflake Snowpark session. + integration_name: Name of the storage integration. + + Returns: + StorageIntegrationInfo with integration details. + + Raises: + BootstrapError: If integration doesn't exist or query fails. + """ + try: + sql: str = load_sql( + path="bootstrap/describe_storage_integration.sql", + integration_name=integration_name, + ) + rows: list[Row] = session.sql(query=sql).collect() + except Exception as e: + raise BootstrapError( + f"Storage integration '{integration_name}' not found. " + f"Run setup_gcs_storage_integration() first. Error: {e}" + ) from e + + properties: dict[str, str] = { + str(row["property"]): str(row["property_value"]) # pyright: ignore[reportUnknownArgumentType] + for row in rows + } + + return StorageIntegrationInfo( + name=integration_name, + enabled=properties.get("ENABLED", "").lower() == "true", + allowed_locations=properties.get("STORAGE_ALLOWED_LOCATIONS", ""), + service_account=properties.get("STORAGE_GCP_SERVICE_ACCOUNT", ""), + ) + + +def validate_storage_integration( + session: Session, + integration_name: str = DEFAULT_INTEGRATION_NAME, + storage_path: str = DEFAULT_GCS_ALLOWED_LOCATION, +) -> str: + """Validate a storage integration can list files in GCS. + + Uses Snowflake's native SYSTEM$VALIDATE_STORAGE_INTEGRATION function + with the 'list' action to verify read permissions on the storage path. + + Args: + session: Active Snowflake Snowpark session. + integration_name: Name of the storage integration. + storage_path: GCS path to validate access to (must be within + the integration's STORAGE_ALLOWED_LOCATIONS). + + Returns: + JSON string with validation status and action results. + + Raises: + BootstrapError: If validation fails or permissions are insufficient. + """ + logger.info(f"Validating storage integration '{integration_name}'...") + + try: + sql: str = load_sql( + path="bootstrap/validate_storage_integration.sql", + integration_name=integration_name, + storage_path=storage_path, + test_file="validation_test.tmp", + ) + rows: list[Row] = session.sql(sql).collect() + except Exception as e: + raise BootstrapError( + f"Storage integration validation failed. Error: {e}" + ) from e + else: + result = str(rows[0]["VALIDATION_RESULT"]) if rows else "No result" # pyright: ignore[reportUnknownArgumentType] + logger.info(f"Validation result: {result}") + return result + + +# Re-export for external usage +__all__ = [ + "BootstrapError", + "StorageIntegrationInfo", + "bootstrap_snowflake_infrastructure", + "get_storage_integration_info", + "setup_gcs_storage_integration", + "validate_storage_integration", +] diff --git a/integration/snowflake/data/__init__.py b/integration/snowflake/data/__init__.py index 0d64c40..97284db 100644 --- a/integration/snowflake/data/__init__.py +++ b/integration/snowflake/data/__init__.py @@ -3,31 +3,56 @@ Usage example: from settings import SnowflakeSettings from snowflake_session import create_session - from data import load_jaffle_shop_data, prepare_weekly_sales_by_store + from data import ( + load_from_gcs, + create_weekly_sales_by_store_with_target, + get_table_names, + ) # Settings auto-load from SNOWFLAKE_* environment variables settings = SnowflakeSettings.from_env() with create_session(settings) as session: - load_jaffle_shop_data(session) - prepare_weekly_sales_by_store(session) + # Load data from GCS (requires storage integration) + load_from_gcs( + session, + storage_integration="GETML_GCS_INTEGRATION", + ) + + # Prepare weekly sales forecasting data + population_table = create_weekly_sales_by_store_with_target( + session, + table_name="WEEKLY_SALES_BY_STORE_WITH_TARGET", + ) + + # Access tables for Arrow export + tables = get_table_names("RAW") + orders_arrow = session.table(tables["orders"]).to_arrow() + population_arrow = session.table(population_table).to_arrow() """ -from ._sql_loader import load_sql from .ingestion import ( + DEFAULT_GCS_BUCKET, + JAFFLE_SHOP_TABLE_NAMES, DataIngestionError, - load_jaffle_shop_data, - load_single_table, - rollback_transaction, + get_table_names, + load_from_gcs, + load_from_s3, +) +from .preparation import ( + DEFAULT_POPULATION_TABLE_NAME, + DataPreparationError, + create_weekly_sales_by_store_with_target, ) -from .preparation import prepare_weekly_sales_by_store, validate_raw_schema __all__ = [ + "DEFAULT_GCS_BUCKET", + "DEFAULT_POPULATION_TABLE_NAME", + "JAFFLE_SHOP_TABLE_NAMES", "DataIngestionError", - "load_jaffle_shop_data", - "load_single_table", - "load_sql", - "prepare_weekly_sales_by_store", - "rollback_transaction", - "validate_raw_schema", + "DataPreparationError", + "create_weekly_sales_by_store_with_target", + "get_table_names", + "load_from_gcs", + "load_from_s3", ] diff --git a/integration/snowflake/data/ingestion.py b/integration/snowflake/data/ingestion.py index 8e2667b..c48895a 100644 --- a/integration/snowflake/data/ingestion.py +++ b/integration/snowflake/data/ingestion.py @@ -1,125 +1,361 @@ -"""Data ingestion from S3 to Snowflake for the Jaffle Shop dataset. +"""Data ingestion from cloud storage to Snowflake for the Jaffle Shop dataset. -This module loads CSV data from a public S3 bucket into Snowflake's RAW schema -using native COPY INTO commands with external staging. +This module loads Parquet data from S3 or GCS buckets into Snowflake using +native COPY INTO commands with external staging. Schema is automatically +inferred from Parquet files using INFER_SCHEMA. + +Supports: +- GCS buckets (requires STORAGE_INTEGRATION, auto-created if permissions exist) +- Public S3 buckets (no credentials required) Usage example: from settings import SnowflakeSettings from snowflake_session import create_session - from data import load_jaffle_shop_data + from data import load_from_gcs, load_from_s3 + from bootstrap import setup_gcs_storage_integration with create_session(SnowflakeSettings.from_env()) as session: - results = load_jaffle_shop_data(session) + # Load from GCS (requires storage integration) + results = load_from_gcs( + session, + storage_integration="GETML_GCS_INTEGRATION", + integration_factory=setup_gcs_storage_integration, + ) + + # Or load from public S3 bucket + results = load_from_s3( + session, + bucket="s3://my-public-bucket/jaffle_shop", + ) """ -# ruff: noqa: G004, S608 +from __future__ import annotations +# ruff: noqa: G004, S608 import logging +from collections.abc import Callable +from typing import TYPE_CHECKING from snowflake.snowpark import Row, Session -from ._sql_loader import load_sql +from sql_loader import load_sql + +if TYPE_CHECKING: + from bootstrap import StorageIntegrationInfo logger: logging.Logger = logging.getLogger(__name__) -JAFFLE_SHOP_TABLES: dict[str, str] = { - "raw_customers": "(id TEXT, name TEXT)", - "raw_orders": ( - "(id TEXT, customer TEXT, ordered_at TIMESTAMP, store_id TEXT, " - "subtotal INTEGER, tax_paid INTEGER, order_total INTEGER)" - ), - "raw_order_items": "(id TEXT, order_id TEXT, sku TEXT)", - "raw_products": "(sku TEXT, name TEXT, type TEXT, price INTEGER, description TEXT)", - "raw_stores": "(id TEXT, name TEXT, opened_at TIMESTAMP, tax_rate FLOAT)", - "raw_supplies": "(id TEXT, name TEXT, cost INTEGER, perishable BOOLEAN, sku TEXT)", -} +IntegrationFactory = Callable[[Session, str, str], "StorageIntegrationInfo"] + +JAFFLE_SHOP_TABLE_NAMES: list[str] = [ + "raw_customers", + "raw_orders", + "raw_order_items", + "raw_products", + "raw_stores", + "raw_supplies", +] + +DEFAULT_GCS_BUCKET = "gcs://static.getml.com/datasets/jaffle_shop" +DEFAULT_STAGE_NAME = "JAFFLE_SHOP_STAGE" + + +class DataIngestionError(Exception): + """Raised when data ingestion fails.""" + + +# ============================================================================= +# Public API +# ============================================================================= + + +def get_table_names(schema: str = "RAW") -> dict[str, str]: + """Get fully qualified table names for Jaffle Shop data. + + Returns a dictionary mapping short table names (without 'raw_' prefix) + to their fully qualified Snowflake table names. -S3_BUCKET_URL = "s3://dbt-tutorial-public/long_term_dataset" -STAGE_NAME = "JAFFLE_SHOP_S3_STAGE" + Args: + schema: Schema where tables are located. + Returns: + Dictionary with keys like 'customers', 'orders', etc. and values + like 'RAW.raw_customers', 'RAW.raw_orders', etc. + + Usage example: + tables = get_table_names("RAW") + orders_table = tables["orders"] # "RAW.raw_orders" + session.table(orders_table).to_arrow() + """ + return { + name.removeprefix("raw_"): f"{schema}.{name}" + for name in JAFFLE_SHOP_TABLE_NAMES + } + + +def load_from_gcs( + session: Session, + storage_integration: str, + bucket: str = DEFAULT_GCS_BUCKET, + destination_schema: str = "RAW", + *, + integration_factory: IntegrationFactory | None = None, +) -> dict[str, int]: + """Load Jaffle Shop data from GCS bucket into Snowflake. + + Creates the destination schema if needed, sets up a Parquet file format + and external stage pointing to the GCS bucket, then loads all tables + using COPY INTO with schema inference. + + If the storage integration doesn't exist and an integration_factory is + provided, attempts to create it automatically (requires ACCOUNTADMIN role). + If no factory is provided and integration is missing, raises an error. + + Args: + session: Active Snowflake Snowpark session. + storage_integration: Name of the Snowflake storage integration for GCS. + bucket: GCS bucket URL (gcs:// or gs:// prefix). + destination_schema: Target schema name for loaded tables. + integration_factory: Optional callable to create storage integration. + Use bootstrap.setup_gcs_storage_integration for automatic creation. + + Raises: + DataIngestionError: If storage integration is missing and no factory + provided, or if data loading fails. + """ + _ensure_storage_integration( + session, storage_integration, bucket, integration_factory + ) + + return _load_data( + session=session, + bucket_url=bucket, + destination_schema=destination_schema, + storage_integration=storage_integration, + ) -def load_jaffle_shop_data(session: Session) -> dict[str, int]: - """Load Jaffle Shop data from S3 into Snowflake RAW schema. - Creates the RAW schema if needed, sets up an external stage pointing to - the public S3 bucket, and loads all tables using COPY INTO commands. - All operations are wrapped in a transaction that rolls back on failure. +def load_from_s3( + session: Session, + bucket: str, + destination_schema: str = "RAW", +) -> dict[str, int]: + """Load Jaffle Shop data from public S3 bucket into Snowflake. + + Creates the destination schema if needed, sets up a Parquet file format + and external stage pointing to the S3 bucket, then loads all tables + using COPY INTO with schema inference. + + Args: + session: Active Snowflake Snowpark session. + bucket: S3 bucket URL (s3:// prefix). Must be publicly accessible. + destination_schema: Target schema name for loaded tables. Raises: - DataIngestionError: If schema creation, stage setup, or data loading fails. + DataIngestionError: If data loading fails. + """ + if not bucket.lower().startswith("s3://"): + raise DataIngestionError( + f"Invalid S3 bucket URL: {bucket}. Must start with s3://" + ) + + return _load_data( + session=session, + bucket_url=bucket, + destination_schema=destination_schema, + storage_integration=None, + ) + + +# ============================================================================= +# Storage Integration Handling +# ============================================================================= + + +def _ensure_storage_integration( + session: Session, + integration_name: str, + bucket_url: str, + integration_factory: IntegrationFactory | None, +) -> None: + """Ensure storage integration exists, creating it if factory is provided. + + Checks if the integration exists. If not and a factory is provided, + attempts to create it. If no factory is provided and integration is + missing, raises an error with instructions. """ + if _storage_integration_exists(session, integration_name): + logger.info(f"✓ Storage integration '{integration_name}' verified") + return + + if integration_factory is None: + raise DataIngestionError( + f"Storage integration '{integration_name}' does not exist. " + f"Either create it manually, or pass integration_factory=" + f"bootstrap.setup_gcs_storage_integration to auto-create it " + f"(requires ACCOUNTADMIN role)." + ) + + logger.info(f"Storage integration '{integration_name}' not found. Creating...") + + try: + _ = integration_factory( + session, + integration_name, + bucket_url, + ) + logger.info(f"✓ Storage integration '{integration_name}' created") + except Exception as e: + raise DataIngestionError( + f"Storage integration '{integration_name}' could not be created " + f"(requires ACCOUNTADMIN role). Error: {e}" + ) from e + + +def _storage_integration_exists(session: Session, integration_name: str) -> bool: + """Check if a storage integration exists.""" + try: + sql: str = load_sql( + path="ingestion/describe_storage_integration.sql", + integration_name=integration_name, + ) + _ = session.sql(sql).collect() + except Exception: # noqa: BLE001 + return False + else: + return True + + +# ============================================================================= +# Core Loading Logic +# ============================================================================= + + +def _load_data( + session: Session, + bucket_url: str, + destination_schema: str, + storage_integration: str | None, +) -> dict[str, int]: + """Core data loading logic shared by GCS and S3 loaders.""" try: _ = session.sql("BEGIN").collect() - _create_raw_schema(session) - _create_external_stage(session) - results = _load_all_tables(session) + _create_schema(session, destination_schema) + _create_parquet_file_format(session, destination_schema) + _create_external_stage( + session, + bucket_url=bucket_url, + schema_name=destination_schema, + storage_integration=storage_integration, + ) + results = _load_all_tables(session, destination_schema) _ = session.sql("COMMIT").collect() except Exception as e: - rollback_transaction(session) + _rollback_transaction(session) raise DataIngestionError( f"Failed to load Jaffle Shop data into Snowflake. Error: {e}" ) from e - _log_completion_summary(results) + _log_completion_summary(results, destination_schema) return results -class DataIngestionError(Exception): - """Raised when data ingestion fails.""" - - -def _create_raw_schema(session: Session) -> None: - """Create RAW schema if it doesn't exist.""" - logger.info("Creating RAW schema if not exists...") - _ = session.sql(load_sql("ingestion/create_raw_schema.sql")).collect() - logger.info("✓ RAW schema ready") +def _create_schema(session: Session, schema_name: str) -> None: + """Create schema if it doesn't exist.""" + logger.info(f"Creating {schema_name} schema if not exists...") + sql: str = load_sql(path="ingestion/create_schema.sql", schema_name=schema_name) + _ = session.sql(query=sql).collect() + logger.info(f"✓ {schema_name} schema ready") -def _create_external_stage(session: Session) -> None: - """Create external stage pointing to S3 bucket.""" - logger.info(f"Creating external stage {STAGE_NAME}...") - sql = load_sql( - "ingestion/create_external_stage.sql", - stage_name=STAGE_NAME, - bucket_url=S3_BUCKET_URL, +def _create_parquet_file_format(session: Session, schema_name: str) -> None: + """Create Parquet file format for data ingestion.""" + logger.info("Creating Parquet file format...") + sql: str = load_sql( + path="ingestion/create_parquet_file_format.sql", + schema_name=schema_name, ) - _ = session.sql(sql).collect() - logger.info(f"✓ External stage {STAGE_NAME} created") - - -def _load_all_tables(session: Session) -> dict[str, int]: - """Load all Jaffle Shop tables from S3.""" - logger.info("Loading tables from S3...") + _ = session.sql(query=sql).collect() + logger.info("✓ Parquet file format ready") + + +def _create_external_stage( + session: Session, + bucket_url: str, + schema_name: str, + storage_integration: str | None, +) -> None: + """Create external stage pointing to cloud storage bucket.""" + logger.info(f"Creating external stage {DEFAULT_STAGE_NAME}...") + + if storage_integration: + sql: str = load_sql( + path="ingestion/create_stage_gcs.sql", + stage_name=DEFAULT_STAGE_NAME, + bucket_url=bucket_url, + storage_integration=storage_integration, + schema_name=schema_name, + ) + else: + sql = load_sql( + path="ingestion/create_stage_s3_public.sql", + stage_name=DEFAULT_STAGE_NAME, + bucket_url=bucket_url, + schema_name=schema_name, + ) + + _ = session.sql(query=sql).collect() + logger.info(f"✓ External stage {DEFAULT_STAGE_NAME} created") + + +def _load_all_tables(session: Session, schema_name: str) -> dict[str, int]: + """Load all Jaffle Shop tables from cloud storage.""" + logger.info("Loading tables from cloud storage...") results: dict[str, int] = {} - for table_name, schema_def in JAFFLE_SHOP_TABLES.items(): - row_count = load_single_table(session, table_name, schema_def) + for table_name in JAFFLE_SHOP_TABLE_NAMES: + row_count: int = _load_single_table(session, table_name, schema_name) results[table_name] = row_count return results -def load_single_table(session: Session, table_name: str, schema_def: str) -> int: - """Load a single table from S3 and return row count.""" - logger.info(f"Loading {table_name}...") - - _ = session.sql(f""" - CREATE OR REPLACE TABLE RAW.{table_name} {schema_def} - """).collect() +def _load_single_table( + session: Session, + table_name: str, + schema_name: str, +) -> int: + """Load a single table from cloud storage using schema inference. - copy_result: list[Row] = session.sql(f""" - COPY INTO RAW.{table_name} - FROM @RAW.{STAGE_NAME}/{table_name}.csv - FORCE = FALSE - """).collect() + Creates the table structure by inferring schema from the Parquet file, + then copies data using column name matching. + """ + logger.info(f"Loading {table_name}...") - count_result: list[Row] = session.sql(f""" - SELECT COUNT(*) as count FROM RAW.{table_name} - """).collect() + _ = session.sql( + query=load_sql( + path="ingestion/infer_and_create_table.sql", + table_name=table_name, + stage_name=DEFAULT_STAGE_NAME, + schema_name=schema_name, + ) + ).collect() + + copy_result: list[Row] = session.sql( + query=load_sql( + path="ingestion/copy_parquet.sql", + table_name=table_name, + stage_name=DEFAULT_STAGE_NAME, + schema_name=schema_name, + ) + ).collect() + + count_result: list[Row] = session.sql( + query=f"SELECT COUNT(*) as count FROM {schema_name}.{table_name}" + ).collect() - # TODO: Research proper typing for Snowpark Row objects row_count = int(count_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] - rows_copied = int(copy_result[0]["rows_parsed"]) if copy_result else 0 # pyright: ignore[reportArgumentType] + rows_copied = int(copy_result[0]["rows_loaded"]) if copy_result else 0 # pyright: ignore[reportArgumentType] logger.info(f" ✓ {rows_copied:,} rows copied, {row_count:,} total rows in table") @@ -129,20 +365,24 @@ def load_single_table(session: Session, table_name: str, schema_def: str) -> int return row_count -def _log_completion_summary(results: dict[str, int]) -> None: +def _log_completion_summary(results: dict[str, int], schema_name: str) -> None: """Log summary of loaded tables.""" - logger.info("=" * 80) - logger.info("DATA LOADING COMPLETE!") - logger.info("=" * 80) - logger.info(f"Successfully loaded {len(results)} tables into RAW schema:") - for table_name, count in results.items(): - logger.info(f" • {table_name}: {count:,} rows") + table_lines = "\n".join( + f" • {name}: {count:,} rows" for name, count in results.items() + ) + logger.info(f""" +================================================================================ +DATA LOADING COMPLETE! +================================================================================ +Successfully loaded {len(results)} tables into {schema_name} schema: +{table_lines} +""") -def rollback_transaction(session: Session) -> None: +def _rollback_transaction(session: Session) -> None: """Attempt to rollback the current transaction.""" try: - _ = session.sql("ROLLBACK").collect() + _ = session.sql(query="ROLLBACK").collect() logger.warning("Transaction rolled back due to error") except Exception: # noqa: BLE001 logger.debug("Rollback failed - transaction may not have been active") diff --git a/integration/snowflake/data/preparation.py b/integration/snowflake/data/preparation.py index 1040b4d..23a57bc 100644 --- a/integration/snowflake/data/preparation.py +++ b/integration/snowflake/data/preparation.py @@ -2,11 +2,26 @@ This module creates: - weekly_stores table: Store-week combinations with reference_date (Monday week start) -- population_weekly_by_store_with_target view: Adds target (next week's sales) +- Population view with target (next week's sales) reference_date is the Monday (week start) derived from DATE_TRUNC('week', ordered_at). SQL queries are externalized in the sql/ directory for better maintainability. + +Usage example: + from settings import SnowflakeSettings + from snowflake_session import create_session + from data import create_weekly_sales_by_store_with_target + + with create_session(SnowflakeSettings.from_env()) as session: + population_table = create_weekly_sales_by_store_with_target( + session, + source_schema="RAW", + target_schema="PREPARED", + table_name="WEEKLY_SALES_BY_STORE_WITH_TARGET", + ) + # population_table = "PREPARED.WEEKLY_SALES_BY_STORE_WITH_TARGET" + arrow_table = session.table(population_table).to_arrow() """ # ruff: noqa: G004, G003, S608, TRY301, E501 @@ -16,46 +31,136 @@ from snowflake.snowpark import Row, Session -from ._sql_loader import load_sql +from sql_loader import load_sql logger: logging.Logger = logging.getLogger(__name__) -def validate_raw_schema(session: Session) -> None: - """Validate that RAW schema exists and contains required tables.""" +class DataPreparationError(Exception): + """Raised when data preparation fails.""" + + +# ============================================================================= +# Public API +# ============================================================================= + + +DEFAULT_POPULATION_TABLE_NAME = "WEEKLY_SALES_BY_STORE_WITH_TARGET" + + +def create_weekly_sales_by_store_with_target( + session: Session, + source_schema: str = "RAW", + target_schema: str = "PREPARED", + table_name: str = DEFAULT_POPULATION_TABLE_NAME, +) -> str: + """Create weekly sales forecasting data for getML, grouped by store. + + Creates: + - weekly_stores: Table with store-week combinations (reference_date = Monday) + - Population view with target column (configurable name via table_name) + - Target: Sum of order_total for the 7-day window starting at reference_date + + Args: + session: Active Snowflake Snowpark session. + source_schema: Schema containing raw_stores and raw_orders tables. + target_schema: Schema where prepared tables/views will be created. + table_name: Name of the population view to create. + + Returns: + Fully qualified table name (e.g., "PREPARED.WEEKLY_SALES_BY_STORE_WITH_TARGET"). + + Raises: + DataPreparationError: If source tables are missing or preparation fails. + """ + _validate_source_tables(session, source_schema) + _ensure_target_schema(session, target_schema) + + logger.info(""" +================================================================================ +PREPARING WEEKLY SALES FORECASTING DATA BY STORE FOR GETML +================================================================================ +""") + + _analyze_and_display_stores(session, source_schema) + per_store = _create_weekly_stores_table(session, source_schema, target_schema) + _create_target_view(session, source_schema, target_schema, table_name) + + _display_sample_data(session, target_schema, per_store, table_name) + _display_store_statistics(session, target_schema, table_name) + _display_overall_statistics(session, target_schema, table_name) + _perform_data_quality_check(session, target_schema, table_name) + _display_recent_snapshots(session, target_schema, per_store, table_name) + + qualified_table_name = f"{target_schema}.{table_name}" + + logger.info(f""" +================================================================================ +DATA PREPARATION COMPLETE! +================================================================================ + +Objects created in {target_schema} schema: + - weekly_stores (TABLE) + - {table_name} (VIEW - USE THIS FOR GETML) + +Population table: {qualified_table_name} +For getML integration instructions, see: docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md +""") + + return qualified_table_name + + +# ============================================================================= +# Validation and Schema Setup +# ============================================================================= + + +def _validate_source_tables(session: Session, source_schema: str) -> None: + """Validate that source schema exists and contains required tables.""" required_tables: list[str] = ["raw_stores", "raw_orders"] - logger.info("Validating RAW schema and required tables...") + logger.info(f"Validating {source_schema} schema and required tables...") try: for table_name in required_tables: result: list[Row] = session.sql( - f"SELECT COUNT(*) as count FROM RAW.{table_name} LIMIT 1" + f"SELECT COUNT(*) as count FROM {source_schema}.{table_name} LIMIT 1" ).collect() if not result: - msg: str = ( - f"Table RAW.{table_name} does not exist or is not accessible. " - "Please run ingestion.load_jaffle_shop_data() first." + raise DataPreparationError( + f"Table {source_schema}.{table_name} does not exist or is not " + "accessible. Please run ingestion first." ) - raise ValueError(msg) + except DataPreparationError: + raise except Exception as e: - msg = f"Failed to validate RAW schema. Ensure data has been loaded. Error: {e}" - raise ValueError(msg) from e + raise DataPreparationError( + f"Failed to validate {source_schema} schema. " + f"Ensure data has been loaded. Error: {e}" + ) from e + + logger.info(f"✓ {source_schema} schema validated") + - logger.info("✓ RAW schema validated") +def _ensure_target_schema(session: Session, target_schema: str) -> None: + """Create target schema if it doesn't exist.""" + logger.info(f"Creating {target_schema} schema if not exists...") + sql: str = load_sql("preparation/create_schema.sql", schema_name=target_schema) + _ = session.sql(query=sql).collect() + logger.info(f"✓ {target_schema} schema ready") -def _ensure_prepared_schema(session: Session) -> None: - """Create PREPARED schema if it doesn't exist.""" - logger.info("Creating PREPARED schema if not exists...") - _ = session.sql(load_sql("preparation/create_prepared_schema.sql")).collect() - logger.info("✓ PREPARED schema ready") +# ============================================================================= +# Data Preparation Pipeline +# ============================================================================= -def _analyze_and_display_stores(session: Session) -> None: +def _analyze_and_display_stores(session: Session, source_schema: str) -> None: """Analyze and display store activity.""" logger.info("\n1. Analyzing store data...") stores: list[Row] = session.sql( - load_sql("preparation/analyze_stores.sql") + query=load_sql( + path="preparation/analyze_stores.sql", source_schema=source_schema + ) ).collect() logger.info(f"\n{'Store':<30} {'Opened':<20} {'Orders':<12} {'Total Sales':<15}") @@ -66,7 +171,11 @@ def _analyze_and_display_stores(session: Session) -> None: ) -def _create_weekly_population_table(session: Session) -> list[Row]: +def _create_weekly_stores_table( + session: Session, + source_schema: str, + target_schema: str, +) -> list[Row]: """Create weekly_stores table with store-week combinations. Creates one row per store per week using reference_date (Monday week start). @@ -77,22 +186,44 @@ def _create_weekly_population_table(session: Session) -> list[Row]: logger.info("\n2. Creating weekly_stores table (store-week combinations)...") logger.info(" reference_date is Monday (week start) from DATE_TRUNC('week', ...)") - _ = session.sql(load_sql("preparation/drop_weekly_stores.sql")).collect() - _ = session.sql(load_sql("preparation/create_weekly_stores.sql")).collect() + _ = session.sql( + load_sql(path="preparation/drop_weekly_stores.sql", target_schema=target_schema) + ).collect() + _ = session.sql( + query=load_sql( + path="preparation/create_weekly_stores.sql", + source_schema=source_schema, + target_schema=target_schema, + ) + ).collect() num_snapshots: int = cast( - "int", session.sql(load_sql("preparation/count_snapshots.sql")).collect()[0][0] + "int", + session.sql( + query=load_sql( + path="preparation/count_snapshots.sql", + target_schema=target_schema, + ) + ).collect()[0][0], ) num_stores: int = cast( - "int", session.sql(load_sql("preparation/count_stores.sql")).collect()[0][0] + "int", + session.sql( + query=load_sql( + path="preparation/count_stores.sql", + target_schema=target_schema, + ) + ).collect()[0][0], ) logger.info( f" Created {num_snapshots:,} weekly snapshots across {num_stores} stores" ) - # Show snapshots per store per_store: list[Row] = session.sql( - load_sql("preparation/snapshots_per_store.sql") + query=load_sql( + path="preparation/snapshots_per_store.sql", + target_schema=target_schema, + ) ).collect() logger.info(f"\n {'Store':<30} {'Snapshots':<12} {'First':<20} {'Last':<20}") @@ -103,81 +234,144 @@ def _create_weekly_population_table(session: Session) -> list[Row]: return per_store -def _add_target_column(session: Session) -> None: +def _create_target_view( + session: Session, + source_schema: str, + target_schema: str, + table_name: str, +) -> None: """Create view with target - total sales for the following week per store.""" logger.info("\n3. Creating target view: next week's total sales per store...") - _ = session.sql(load_sql("preparation/calculate_target.sql")).collect() + _ = session.sql( + query=load_sql( + path="preparation/calculate_target.sql", + source_schema=source_schema, + target_schema=target_schema, + table_name=table_name, + ) + ).collect() + + +# ============================================================================= +# Display and Analysis Functions +# ============================================================================= -def _display_sample_data(session: Session, per_store: list[Row]) -> None: +def _display_sample_data( + session: Session, + target_schema: str, + per_store: list[Row], + table_name: str, +) -> None: """Show sample data for first few stores.""" - logger.info("\n4. Sample data - first 5 snapshots per store:") - logger.info("-" * 120) + if not logger.isEnabledFor(logging.DEBUG): + return - for store_info in per_store[:3]: # Show first 3 stores + logger.debug("\n4. Sample data - first 5 snapshots per store:") + logger.debug("-" * 120) + + for store_info in per_store[:3]: store_name: str = cast("str", store_info[0]) - logger.info(f"\n{store_name}:") - sample_query = load_sql( - "preparation/sample_data_by_store.sql", store_name=store_name + logger.debug(f"\n{store_name}:") + sample_query: str = load_sql( + path="preparation/sample_data_by_store.sql", + target_schema=target_schema, + store_name=store_name, + table_name=table_name, ) samples: list[Row] = session.sql(sample_query).collect() - logger.info(f"{'ID':<8} {'Week Start':<20} {'Sales $':<15} {'Orders':<10}") - logger.info("-" * 60) + logger.debug(f"{'ID':<8} {'Week Start':<20} {'Sales $':<15} {'Orders':<10}") + logger.debug("-" * 60) for row in samples: - logger.info(f"{row[0]:<8} {row[1]!s:<20} ${row[2]:<14,.2f} {row[3]:<10,}") + logger.debug(f"{row[0]:<8} {row[1]!s:<20} ${row[2]:<14,.2f} {row[3]:<10,}") -def _display_store_statistics(session: Session) -> None: +def _display_store_statistics( + session: Session, + target_schema: str, + table_name: str, +) -> None: """Display target statistics per store.""" - logger.info("\n5. Target statistics per store:") - logger.info("-" * 120) + if not logger.isEnabledFor(logging.DEBUG): + return + + logger.debug("\n5. Target statistics per store:") + logger.debug("-" * 120) store_stats: list[Row] = session.sql( - load_sql("preparation/store_statistics.sql") + query=load_sql( + path="preparation/store_statistics.sql", + target_schema=target_schema, + table_name=table_name, + ) ).collect() - logger.info( + logger.debug( f"{'Store':<30} {'Snapshots':<12} {'Avg Week $':<15} " f"{'Min $':<15} {'Max $':<15} {'Std Dev $':<15}" ) - logger.info("-" * 120) + logger.debug("-" * 120) for row in store_stats: - logger.info( + logger.debug( f"{row[0]:<30} {row[1]:<12,} ${row[2]:<14,.2f} " f"${row[3]:<14,.2f} ${row[4]:<14,.2f} ${row[5]:<14,.2f}" ) -def _display_overall_statistics(session: Session) -> None: +def _display_overall_statistics( + session: Session, + target_schema: str, + table_name: str, +) -> None: """Display overall dataset statistics.""" - logger.info("\n6. Overall statistics:") - logger.info("-" * 80) + if not logger.isEnabledFor(logging.DEBUG): + return overall: Row = session.sql( - load_sql("preparation/overall_statistics.sql") + query=load_sql( + path="preparation/overall_statistics.sql", + target_schema=target_schema, + table_name=table_name, + ) ).collect()[0] - logger.info(f" Total snapshots: {overall[0]:,}") - logger.info(f" Number of stores: {overall[1]:,}") - logger.info(f" Average weekly sales per store: ${overall[2]:,.2f}") - logger.info(f" Total sales in dataset: ${overall[3]:,.2f}") - logger.info(f" Total orders in dataset: {overall[4]:,}") - - -def _perform_data_quality_check(session: Session) -> None: + logger.debug(f""" +6. Overall statistics: +-------------------------------------------------------------------------------- + Total snapshots: {overall[0]:,} + Number of stores: {overall[1]:,} + Average weekly sales per store: ${overall[2]:,.2f} + Total sales in dataset: ${overall[3]:,.2f} + Total orders in dataset: {overall[4]:,} +""") + + +def _perform_data_quality_check( + session: Session, + target_schema: str, + table_name: str, +) -> None: """Perform and display data quality checks.""" - logger.info("\n7. Data quality check:") - logger.info("-" * 80) + if not logger.isEnabledFor(logging.DEBUG): + return quality: Row = session.sql( - load_sql("preparation/data_quality_check.sql") + query=load_sql( + path="preparation/data_quality_check.sql", + target_schema=target_schema, + table_name=table_name, + ) ).collect()[0] - logger.info(f" Total snapshots: {quality[0]:,}") - logger.info(f" Weeks with zero sales: {quality[1]:,}") - logger.info(f" Weeks with zero orders: {quality[2]:,}") - logger.info(f" Invalid records (sales but no orders): {quality[3]:,}") + logger.debug(f""" +7. Data quality check: +-------------------------------------------------------------------------------- + Total snapshots: {quality[0]:,} + Weeks with zero sales: {quality[1]:,} + Weeks with zero orders: {quality[2]:,} + Invalid records (sales but no orders): {quality[3]:,} +""") zero_sales_count: int = cast("int", quality[1]) if zero_sales_count > 0: @@ -185,94 +379,65 @@ def _perform_data_quality_check(session: Session) -> None: f"\n WARNING: {zero_sales_count} snapshots have zero sales. Investigating..." ) zero_sales: list[Row] = session.sql( - load_sql("preparation/zero_sales_investigation.sql") + query=load_sql( + path="preparation/zero_sales_investigation.sql", + target_schema=target_schema, + table_name=table_name, + ) ).collect() for row in zero_sales: logger.warning(f" {row[0]}: {row[1]} weeks with zero sales") -def _display_recent_snapshots(session: Session, per_store: list[Row]) -> None: +def _display_recent_snapshots( + session: Session, + target_schema: str, + per_store: list[Row], + table_name: str, +) -> None: """Show most recent snapshots for first few stores.""" - logger.info("\n8. Most recent snapshots (last 3 per store):") - logger.info("-" * 120) + if not logger.isEnabledFor(logging.DEBUG): + return + + logger.debug("\n8. Most recent snapshots (last 3 per store):") + logger.debug("-" * 120) - for store_info in per_store[:3]: # Show first 3 stores + for store_info in per_store[:3]: store_name: str = cast("str", store_info[0]) - logger.info(f"\n{store_name}:") - recent_query = load_sql( - "preparation/recent_snapshots_by_store.sql", store_name=store_name + logger.debug(f"\n{store_name}:") + recent_query: str = load_sql( + path="preparation/recent_snapshots_by_store.sql", + target_schema=target_schema, + store_name=store_name, + table_name=table_name, ) recent: list[Row] = session.sql(recent_query).collect() - logger.info(f"{'ID':<8} {'Week Start':<20} {'Sales $':<15} {'Orders':<10}") - logger.info("-" * 60) + logger.debug(f"{'ID':<8} {'Week Start':<20} {'Sales $':<15} {'Orders':<10}") + logger.debug("-" * 60) for row in recent: - logger.info(f"{row[0]:<8} {row[1]!s:<20} ${row[2]:<14,.2f} {row[3]:<10,}") - - -def prepare_weekly_sales_by_store(session: Session) -> None: - """Prepare data for getML to predict next week's sales, grouped by store. - - Creates: - - weekly_stores: Table with store-week combinations (reference_date = Monday) - - population_weekly_by_store_with_target: View adding target column - - Target: Sum of order_total for the 7-day window starting at reference_date - - Args: - session: Active Snowflake Snowpark session - """ - # Setup and validation - validate_raw_schema(session) - _ensure_prepared_schema(session) - - logger.info("=" * 80) - logger.info("PREPARING WEEKLY SALES FORECASTING DATA BY STORE FOR GETML") - logger.info("=" * 80) - - # Data preparation pipeline - _analyze_and_display_stores(session) - per_store = _create_weekly_population_table(session) - _add_target_column(session) - - # Display analysis and quality checks - _display_sample_data(session, per_store) - _display_store_statistics(session) - _display_overall_statistics(session) - _perform_data_quality_check(session) - _display_recent_snapshots(session, per_store) - - logger.info("\n" + "=" * 80) - logger.info("DATA PREPARATION COMPLETE!") - logger.info("=" * 80) - logger.info("\nObjects created in PREPARED schema:") - logger.info(" - weekly_stores (TABLE)") - logger.info( - " - population_weekly_by_store_with_target (VIEW - USE THIS FOR GETML)" - ) - logger.info( - "\nFor getML integration instructions, see: " - "docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md" - ) - logger.info("\n") + logger.debug(f"{row[0]:<8} {row[1]!s:<20} ${row[2]:<14,.2f} {row[3]:<10,}") if __name__ == "__main__": - # Configure logging for standalone execution logging.basicConfig( level=logging.INFO, format="%(message)s", ) - # Example usage - requires connection parameters logger.info("This module requires a Snowflake session to be passed.") logger.info("Example usage:") logger.info(""" from settings import SnowflakeSettings from snowflake_session import create_session - from data import prepare_weekly_sales_by_store + from data import create_weekly_sales_by_store_with_target settings = SnowflakeSettings.from_env() with create_session(settings) as session: - prepare_weekly_sales_by_store(session) + population_table = create_weekly_sales_by_store_with_target( + session, + table_name="WEEKLY_SALES_BY_STORE_WITH_TARGET", + ) + # Use population_table with session.table(population_table).to_arrow() """) diff --git a/integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql b/integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql new file mode 100644 index 0000000..02fd24b --- /dev/null +++ b/integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql @@ -0,0 +1,7 @@ +-- Create a GCS storage integration for accessing Google Cloud Storage buckets +-- Requires ACCOUNTADMIN role or CREATE INTEGRATION privilege +CREATE OR REPLACE STORAGE INTEGRATION {integration_name} + TYPE = EXTERNAL_STAGE + STORAGE_PROVIDER = 'GCS' + ENABLED = TRUE + STORAGE_ALLOWED_LOCATIONS = ('{allowed_location}') diff --git a/integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql b/integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql new file mode 100644 index 0000000..c7d5a36 --- /dev/null +++ b/integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql @@ -0,0 +1,3 @@ +-- Describe a storage integration to retrieve configuration details +-- including the STORAGE_GCP_SERVICE_ACCOUNT for IAM setup +DESC STORAGE INTEGRATION {integration_name} diff --git a/integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql b/integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql new file mode 100644 index 0000000..4eb07ae --- /dev/null +++ b/integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql @@ -0,0 +1,9 @@ +-- Validate a storage integration can access GCS with list permissions +-- Uses Snowflake's native validation function with full signature +-- Returns JSON with status and action results +SELECT SYSTEM$VALIDATE_STORAGE_INTEGRATION( + '{integration_name}', + '{storage_path}', + '{test_file}', + 'list' +) AS VALIDATION_RESULT diff --git a/integration/snowflake/data/sql/ingestion/copy_parquet.sql b/integration/snowflake/data/sql/ingestion/copy_parquet.sql new file mode 100644 index 0000000..be10745 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/copy_parquet.sql @@ -0,0 +1,6 @@ +-- Copy Parquet data into table using column name matching +-- MATCH_BY_COLUMN_NAME handles column ordering differences between file and table +COPY INTO {schema_name}.{table_name} + FROM @{schema_name}.{stage_name}/{table_name}.parquet + FILE_FORMAT = {schema_name}.PARQUET_FORMAT + MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE diff --git a/integration/snowflake/data/sql/ingestion/create_external_stage.sql b/integration/snowflake/data/sql/ingestion/create_external_stage.sql deleted file mode 100644 index 6022d8d..0000000 --- a/integration/snowflake/data/sql/ingestion/create_external_stage.sql +++ /dev/null @@ -1,9 +0,0 @@ --- Create external stage pointing to S3 bucket for CSV data ingestion -CREATE OR REPLACE STAGE RAW.{stage_name} - URL = '{bucket_url}' - FILE_FORMAT = ( - TYPE = 'CSV' - FIELD_DELIMITER = ',' - SKIP_HEADER = 1 - FIELD_OPTIONALLY_ENCLOSED_BY = '"' - ) diff --git a/integration/snowflake/data/sql/ingestion/create_parquet_file_format.sql b/integration/snowflake/data/sql/ingestion/create_parquet_file_format.sql new file mode 100644 index 0000000..c6d6504 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_parquet_file_format.sql @@ -0,0 +1,5 @@ +-- Create Parquet file format for data ingestion +-- Uses CREATE OR REPLACE for idempotent execution +CREATE OR REPLACE FILE FORMAT {schema_name}.PARQUET_FORMAT + TYPE = PARQUET + USE_VECTORIZED_SCANNER = TRUE diff --git a/integration/snowflake/data/sql/ingestion/create_raw_schema.sql b/integration/snowflake/data/sql/ingestion/create_raw_schema.sql deleted file mode 100644 index 0a0b6a9..0000000 --- a/integration/snowflake/data/sql/ingestion/create_raw_schema.sql +++ /dev/null @@ -1,2 +0,0 @@ --- Create RAW schema for raw data ingestion -CREATE SCHEMA IF NOT EXISTS RAW diff --git a/integration/snowflake/data/sql/ingestion/create_schema.sql b/integration/snowflake/data/sql/ingestion/create_schema.sql new file mode 100644 index 0000000..7b869ca --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_schema.sql @@ -0,0 +1,2 @@ +-- Create schema for data ingestion +CREATE SCHEMA IF NOT EXISTS {schema_name} diff --git a/integration/snowflake/data/sql/ingestion/create_stage_gcs.sql b/integration/snowflake/data/sql/ingestion/create_stage_gcs.sql new file mode 100644 index 0000000..3bc7f96 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_stage_gcs.sql @@ -0,0 +1,6 @@ +-- Create external stage pointing to a GCS bucket for Parquet data ingestion +-- Requires a storage integration for authentication +CREATE OR REPLACE STAGE {schema_name}.{stage_name} + URL = '{bucket_url}' + STORAGE_INTEGRATION = {storage_integration} + FILE_FORMAT = {schema_name}.PARQUET_FORMAT diff --git a/integration/snowflake/data/sql/ingestion/create_stage_s3_public.sql b/integration/snowflake/data/sql/ingestion/create_stage_s3_public.sql new file mode 100644 index 0000000..b9f778e --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_stage_s3_public.sql @@ -0,0 +1,5 @@ +-- Create external stage pointing to a public S3 bucket for Parquet data ingestion +-- No credentials required for public buckets +CREATE OR REPLACE STAGE {schema_name}.{stage_name} + URL = '{bucket_url}' + FILE_FORMAT = {schema_name}.PARQUET_FORMAT diff --git a/integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql b/integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql new file mode 100644 index 0000000..02fd24b --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql @@ -0,0 +1,7 @@ +-- Create a GCS storage integration for accessing Google Cloud Storage buckets +-- Requires ACCOUNTADMIN role or CREATE INTEGRATION privilege +CREATE OR REPLACE STORAGE INTEGRATION {integration_name} + TYPE = EXTERNAL_STAGE + STORAGE_PROVIDER = 'GCS' + ENABLED = TRUE + STORAGE_ALLOWED_LOCATIONS = ('{allowed_location}') diff --git a/integration/snowflake/data/sql/ingestion/describe_storage_integration.sql b/integration/snowflake/data/sql/ingestion/describe_storage_integration.sql new file mode 100644 index 0000000..c7d5a36 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/describe_storage_integration.sql @@ -0,0 +1,3 @@ +-- Describe a storage integration to retrieve configuration details +-- including the STORAGE_GCP_SERVICE_ACCOUNT for IAM setup +DESC STORAGE INTEGRATION {integration_name} diff --git a/integration/snowflake/data/sql/ingestion/infer_and_create_table.sql b/integration/snowflake/data/sql/ingestion/infer_and_create_table.sql new file mode 100644 index 0000000..aa33ed7 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/infer_and_create_table.sql @@ -0,0 +1,12 @@ +-- Create table with schema inferred from Parquet file +-- Uses INFER_SCHEMA to automatically detect column names and types +CREATE OR REPLACE TABLE {schema_name}.{table_name} + USING TEMPLATE ( + SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*)) + FROM TABLE( + INFER_SCHEMA( + LOCATION => '@{schema_name}.{stage_name}/{table_name}.parquet', + FILE_FORMAT => '{schema_name}.PARQUET_FORMAT' + ) + ) + ) diff --git a/integration/snowflake/data/sql/ingestion/validate_storage_integration.sql b/integration/snowflake/data/sql/ingestion/validate_storage_integration.sql new file mode 100644 index 0000000..2ab4ee1 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/validate_storage_integration.sql @@ -0,0 +1,3 @@ +-- Validate a storage integration configuration +-- Returns success message or error details +SELECT SYSTEM$VALIDATE_STORAGE_INTEGRATION('{integration_name}') AS validation_result diff --git a/integration/snowflake/data/sql/preparation/analyze_stores.sql b/integration/snowflake/data/sql/preparation/analyze_stores.sql index 138f111..6dabcd4 100644 --- a/integration/snowflake/data/sql/preparation/analyze_stores.sql +++ b/integration/snowflake/data/sql/preparation/analyze_stores.sql @@ -7,7 +7,7 @@ SELECT MAX(o.ordered_at) as last_order, COUNT(o.id) as total_orders, SUM(COALESCE(o.order_total, 0)) / 100.0 as total_sales -FROM RAW.raw_stores s -LEFT JOIN RAW.raw_orders o ON o.store_id = s.id +FROM {source_schema}.raw_stores s +LEFT JOIN {source_schema}.raw_orders o ON o.store_id = s.id GROUP BY s.id, s.name, s.opened_at ORDER BY s.opened_at diff --git a/integration/snowflake/data/sql/preparation/calculate_target.sql b/integration/snowflake/data/sql/preparation/calculate_target.sql index 330593b..008fd5c 100644 --- a/integration/snowflake/data/sql/preparation/calculate_target.sql +++ b/integration/snowflake/data/sql/preparation/calculate_target.sql @@ -6,7 +6,7 @@ -- Window: [reference_date, reference_date + 7 days) -- - reference_date is Monday 00:00:00 (week start) -- - Target covers Monday through Sunday of that week -CREATE OR REPLACE VIEW PREPARED.population_weekly_by_store_with_target AS +CREATE OR REPLACE VIEW {target_schema}.{table_name} AS SELECT ws.snapshot_id, ws.store_id, @@ -22,7 +22,7 @@ SELECT COALESCE( ( SELECT SUM(o.order_total) / 100.0 - FROM RAW.raw_orders o + FROM {source_schema}.raw_orders o WHERE o.store_id = ws.store_id AND o.ordered_at >= ws.reference_date AND o.ordered_at < ws.reference_date + INTERVAL '7 days' @@ -31,10 +31,10 @@ SELECT COALESCE( ( SELECT COUNT(*) - FROM RAW.raw_orders o + FROM {source_schema}.raw_orders o WHERE o.store_id = ws.store_id AND o.ordered_at >= ws.reference_date AND o.ordered_at < ws.reference_date + INTERVAL '7 days' ), 0 ) as next_week_orders -FROM PREPARED.weekly_stores ws; +FROM {target_schema}.weekly_stores ws; diff --git a/integration/snowflake/data/sql/preparation/count_snapshots.sql b/integration/snowflake/data/sql/preparation/count_snapshots.sql index e867907..03b67d8 100644 --- a/integration/snowflake/data/sql/preparation/count_snapshots.sql +++ b/integration/snowflake/data/sql/preparation/count_snapshots.sql @@ -1,2 +1,2 @@ -- Count total snapshots -SELECT COUNT(*) FROM PREPARED.weekly_stores +SELECT COUNT(*) FROM {target_schema}.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/count_stores.sql b/integration/snowflake/data/sql/preparation/count_stores.sql index c4a5c8d..692f46d 100644 --- a/integration/snowflake/data/sql/preparation/count_stores.sql +++ b/integration/snowflake/data/sql/preparation/count_stores.sql @@ -1,2 +1,2 @@ -- Count distinct stores -SELECT COUNT(DISTINCT store_id) FROM PREPARED.weekly_stores +SELECT COUNT(DISTINCT store_id) FROM {target_schema}.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/create_prepared_schema.sql b/integration/snowflake/data/sql/preparation/create_prepared_schema.sql deleted file mode 100644 index d959066..0000000 --- a/integration/snowflake/data/sql/preparation/create_prepared_schema.sql +++ /dev/null @@ -1,2 +0,0 @@ --- Create PREPARED schema for processed data ready for ML -CREATE SCHEMA IF NOT EXISTS PREPARED diff --git a/integration/snowflake/data/sql/preparation/create_schema.sql b/integration/snowflake/data/sql/preparation/create_schema.sql new file mode 100644 index 0000000..ef0ad6d --- /dev/null +++ b/integration/snowflake/data/sql/preparation/create_schema.sql @@ -0,0 +1,2 @@ +-- Create schema for processed data ready for ML +CREATE SCHEMA IF NOT EXISTS {schema_name} diff --git a/integration/snowflake/data/sql/preparation/create_weekly_stores.sql b/integration/snowflake/data/sql/preparation/create_weekly_stores.sql index fee746a..b3fafa3 100644 --- a/integration/snowflake/data/sql/preparation/create_weekly_stores.sql +++ b/integration/snowflake/data/sql/preparation/create_weekly_stores.sql @@ -1,6 +1,6 @@ -- Create store-week combinations for weekly sales forecasting -- --- This table creates the base population for getML: one row per store per week. +-- This table creates the base data for getML: one row per store per week. -- reference_date is the Monday (week start) derived from DATE_TRUNC('week', ordered_at). -- -- Filtering logic: @@ -11,7 +11,7 @@ -- - is_full_week_after_opening: Store had a full week of operation before this week -- - has_order_activity: Store has order data spanning this week -- - has_min_history: At least 7 days since store opened -CREATE TABLE PREPARED.weekly_stores AS +CREATE TABLE {target_schema}.weekly_stores AS WITH store_activity AS ( SELECT s.id as store_id, @@ -22,15 +22,15 @@ WITH store_activity AS ( MAX(o.ordered_at) as last_order_date, DATE_TRUNC('week', MIN(o.ordered_at)) as first_order_week, DATE_TRUNC('week', MAX(o.ordered_at)) as last_order_week - FROM RAW.raw_stores s - LEFT JOIN RAW.raw_orders o ON o.store_id = s.id + FROM {source_schema}.raw_stores s + LEFT JOIN {source_schema}.raw_orders o ON o.store_id = s.id GROUP BY s.id, s.name, s.opened_at ), all_weeks AS ( SELECT DISTINCT DATE_TRUNC('week', ordered_at) as reference_date - FROM RAW.raw_orders + FROM {source_schema}.raw_orders WHERE ordered_at IS NOT NULL ), diff --git a/integration/snowflake/data/sql/preparation/data_quality_check.sql b/integration/snowflake/data/sql/preparation/data_quality_check.sql index 35b28ea..bce0155 100644 --- a/integration/snowflake/data/sql/preparation/data_quality_check.sql +++ b/integration/snowflake/data/sql/preparation/data_quality_check.sql @@ -4,4 +4,4 @@ SELECT SUM(CASE WHEN next_week_sales = 0 THEN 1 ELSE 0 END) as zero_sales, SUM(CASE WHEN next_week_orders = 0 THEN 1 ELSE 0 END) as zero_orders, SUM(CASE WHEN next_week_sales > 0 AND next_week_orders = 0 THEN 1 ELSE 0 END) as invalid -FROM PREPARED.population_weekly_by_store_with_target +FROM {target_schema}.{table_name} diff --git a/integration/snowflake/data/sql/preparation/drop_weekly_stores.sql b/integration/snowflake/data/sql/preparation/drop_weekly_stores.sql index 58d22bb..d707af2 100644 --- a/integration/snowflake/data/sql/preparation/drop_weekly_stores.sql +++ b/integration/snowflake/data/sql/preparation/drop_weekly_stores.sql @@ -1,2 +1,2 @@ -- Drop the weekly_stores table if it exists -DROP TABLE IF EXISTS PREPARED.weekly_stores +DROP TABLE IF EXISTS {target_schema}.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/overall_statistics.sql b/integration/snowflake/data/sql/preparation/overall_statistics.sql index 8a53514..0772c2d 100644 --- a/integration/snowflake/data/sql/preparation/overall_statistics.sql +++ b/integration/snowflake/data/sql/preparation/overall_statistics.sql @@ -5,4 +5,4 @@ SELECT AVG(next_week_sales) as avg_weekly_sales, SUM(next_week_sales) as total_sales, SUM(next_week_orders) as total_orders -FROM PREPARED.population_weekly_by_store_with_target +FROM {target_schema}.{table_name} diff --git a/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql b/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql index b13234b..577c2e3 100644 --- a/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql +++ b/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql @@ -4,7 +4,7 @@ SELECT reference_date, next_week_sales, next_week_orders -FROM PREPARED.population_weekly_by_store_with_target +FROM {target_schema}.{table_name} WHERE store_name = '{store_name}' ORDER BY reference_date DESC LIMIT 3 diff --git a/integration/snowflake/data/sql/preparation/sample_data_by_store.sql b/integration/snowflake/data/sql/preparation/sample_data_by_store.sql index ceaa47d..5340322 100644 --- a/integration/snowflake/data/sql/preparation/sample_data_by_store.sql +++ b/integration/snowflake/data/sql/preparation/sample_data_by_store.sql @@ -4,7 +4,7 @@ SELECT reference_date, next_week_sales, next_week_orders -FROM PREPARED.population_weekly_by_store_with_target +FROM {target_schema}.{table_name} WHERE store_name = '{store_name}' ORDER BY reference_date LIMIT 5 diff --git a/integration/snowflake/data/sql/preparation/snapshots_per_store.sql b/integration/snowflake/data/sql/preparation/snapshots_per_store.sql index 2dd43af..2572a79 100644 --- a/integration/snowflake/data/sql/preparation/snapshots_per_store.sql +++ b/integration/snowflake/data/sql/preparation/snapshots_per_store.sql @@ -4,6 +4,6 @@ SELECT COUNT(*) as num_snapshots, MIN(reference_date) as first_snapshot, MAX(reference_date) as last_snapshot -FROM PREPARED.weekly_stores +FROM {target_schema}.weekly_stores GROUP BY store_name ORDER BY first_snapshot diff --git a/integration/snowflake/data/sql/preparation/store_statistics.sql b/integration/snowflake/data/sql/preparation/store_statistics.sql index 0776150..6bdeb4e 100644 --- a/integration/snowflake/data/sql/preparation/store_statistics.sql +++ b/integration/snowflake/data/sql/preparation/store_statistics.sql @@ -7,6 +7,6 @@ SELECT MAX(next_week_sales) as max_weekly_sales, STDDEV(next_week_sales) as stddev_weekly_sales, SUM(next_week_orders) as total_orders -FROM PREPARED.population_weekly_by_store_with_target +FROM {target_schema}.{table_name} GROUP BY store_name ORDER BY avg_weekly_sales DESC diff --git a/integration/snowflake/data/sql/preparation/zero_sales_investigation.sql b/integration/snowflake/data/sql/preparation/zero_sales_investigation.sql index 83533ea..7a03bb7 100644 --- a/integration/snowflake/data/sql/preparation/zero_sales_investigation.sql +++ b/integration/snowflake/data/sql/preparation/zero_sales_investigation.sql @@ -1,6 +1,6 @@ -- Investigate stores with zero sales SELECT store_name, COUNT(*) as count -FROM PREPARED.population_weekly_by_store_with_target +FROM {target_schema}.{table_name} WHERE next_week_sales = 0 GROUP BY store_name ORDER BY count DESC diff --git a/integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md b/integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md new file mode 100644 index 0000000..f323fb7 --- /dev/null +++ b/integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md @@ -0,0 +1,187 @@ +# GCS Storage Integration Setup for Snowflake + +This guide describes how to configure Snowflake to access Parquet files stored in Google Cloud Storage (GCS) for the Jaffle Shop dataset. + +## Overview + +Snowflake cannot access GCS buckets directly—even publicly accessible ones—without a **storage integration**. This integration creates a GCS service account that must be granted IAM permissions on the target bucket. + +**Default data location:** `gs://static.getml.com/datasets/jaffle_shop/` + +## Prerequisites + +- Snowflake account with `ACCOUNTADMIN` role access +- GCP project editor access (to grant IAM permissions on the bucket) +- Python environment with `uv` configured + +## Step 1: Create Storage Integration + +Use the provided helper function to create the storage integration: + +```python +from settings import SnowflakeSettings +from snowflake_session import create_session +from data import setup_gcs_integration + +settings = SnowflakeSettings.from_env() +with create_session(settings) as session: + info = setup_gcs_integration(session) + print(f"Service account: {info.service_account}") +``` + +This creates the integration and prints the service account that needs IAM permissions. + +For custom bucket locations: + +```python +from data import setup_gcs_integration + +info = setup_gcs_integration( + session, + integration_name="MY_CUSTOM_INTEGRATION", + allowed_location="gcs://my-bucket/path/", +) +``` + +You can also retrieve info about an existing integration: + +```python +from data import describe_storage_integration + +info = describe_storage_integration(session, "GETML_GCS_INTEGRATION") +print(f"Service account: {info.service_account}") +``` + +## Step 2: Grant IAM Permissions in GCP + +The Snowflake service account from Step 1 needs read access to the GCS bucket. + +### Using gcloud CLI (Recommended) + +```bash +# Set your project +gcloud config set project getml-infra + +# Store the service account from Step 1 +SERVICE_ACCOUNT="@gcpeuropewestX-X-XXXX.iam.gserviceaccount.com" + +# Grant Storage Object Viewer role on the bucket +gsutil iam ch "serviceAccount:${SERVICE_ACCOUNT}:objectViewer" gs://static.getml.com + +# Verify the binding was applied +gsutil iam get gs://static.getml.com +``` + +For granular permissions (alternative to objectViewer role): + +```bash +# Create a custom role with minimal permissions +gcloud iam roles create snowflake_gcs_reader \ + --project=getml-infra \ + --title="Snowflake GCS Reader" \ + --permissions=storage.buckets.get,storage.objects.get,storage.objects.list + +# Grant the custom role +gsutil iam ch "serviceAccount:${SERVICE_ACCOUNT}:projects/getml-infra/roles/snowflake_gcs_reader" \ + gs://static.getml.com +``` + +### Using Google Cloud Console + +1. Sign in to the [Google Cloud Console](https://console.cloud.google.com/) +2. Navigate to **Cloud Storage → Buckets** +3. Select the bucket `static.getml.com` +4. Go to **Permissions → Grant Access** +5. Add the Snowflake service account email as a principal +6. Assign role: **Storage Object Viewer** +7. Click **Save** + +## Step 3: Validate the Integration + +Validate that Snowflake can access the GCS bucket: + +```python +from data import validate_storage_integration + +result = validate_storage_integration(session, "GETML_GCS_INTEGRATION") +print(result) # Should show success +``` + +## Step 4: Configure Environment Variable + +For the integration tests to use this storage integration, set the environment variable: + +```bash +export GCS_STORAGE_INTEGRATION=GETML_GCS_INTEGRATION +``` + +Add this to your shell profile (`.bashrc`, `.zshrc`) or CI/CD secrets for persistence. + +## Running Integration Tests + +With the environment variable set, integration tests will automatically use the GCS storage integration: + +```bash +cd integration/snowflake +uv run pytest tests/integration/ -v +``` + +## Troubleshooting + +### Error: "Processing aborted due to error 300010" + +This typically means the Snowflake service account doesn't have IAM permissions on the GCS bucket. Verify: + +```bash +# Check current IAM bindings on the bucket +gsutil iam get gs://static.getml.com + +# Verify the service account is listed with objectViewer or equivalent role +``` + +### Error: "Stage does not exist or not authorized" + +The stage hasn't been created yet. The `load_jaffle_shop_data_parquet()` function creates it automatically during data ingestion. + +### Error: "Storage integration not found" + +Verify the integration exists: + +```python +from data import describe_storage_integration + +try: + info = describe_storage_integration(session, "GETML_GCS_INTEGRATION") + print(f"Integration exists: {info.enabled}") +except Exception as e: + print(f"Integration not found: {e}") +``` + +### Domain Restriction Policy (GCP Organizations) + +If your GCP organization was created after May 3, 2024, Google enforces domain restrictions by default. Update the organization policy: + +```bash +# Check current policy +gcloud org-policies describe iam.allowedPolicyMemberDomains \ + --project=getml-infra + +# If restricted, add Snowflake's domain to allowed list +# See: https://docs.snowflake.com/en/user-guide/data-load-gcs-allow +``` + +## Alternative: Using S3 Public Bucket + +If GCS setup isn't possible, you can use a public S3 bucket with Parquet files instead: + +```bash +export S3_BUCKET_URL=s3://your-public-bucket/jaffle_shop/ +``` + +S3 public buckets don't require a storage integration—Snowflake can access them directly. + +## References + +- [Snowflake: Configuring an integration for Google Cloud Storage](https://docs.snowflake.com/en/user-guide/data-load-gcs-config) +- [Snowflake: CREATE STORAGE INTEGRATION](https://docs.snowflake.com/en/sql-reference/sql/create-storage-integration) +- [GCP: IAM for Cloud Storage](https://cloud.google.com/storage/docs/access-control/iam) diff --git a/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md b/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md index 391585b..17b96cd 100644 --- a/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md +++ b/integration/snowflake/docs/GETML_WEEKLY_SALES_DATA_PREPARATION.md @@ -5,13 +5,13 @@ The data preparation pipeline creates: - `weekly_stores` (TABLE): Store-week combinations with `reference_date` (Monday week start) -- `population_weekly_by_store_with_target` (VIEW): Adds target column (next week's sales) +- `weekly_sales_by_store_with_target` (VIEW): Adds target column (next week's sales) --- -## 1. Population View +## 1. Weekly Sales View -**View name:** `population_weekly_by_store_with_target` +**View name:** `weekly_sales_by_store_with_target` | Column | Description | |--------|-------------| @@ -52,7 +52,7 @@ The data preparation pipeline creates: **Column name:** `store_id` -- Links population to store-specific data +- Links weekly sales data to store-specific data --- @@ -60,8 +60,8 @@ The data preparation pipeline creates: | Table | Join Condition | |-------|----------------| -| `raw_stores` | `store.id = population.store_id` | -| `raw_orders` | `order.store_id = population.store_id AND order.ordered_at < reference_date` | +| `raw_stores` | `store.id = weekly_sales.store_id` | +| `raw_orders` | `order.store_id = weekly_sales.store_id AND order.ordered_at < reference_date` | | `raw_customers` | Join through orders | | `raw_items` | Join through orders | | `raw_products` | Join through items | @@ -74,21 +74,21 @@ The data preparation pipeline creates: ```python import getml -# Load population (one row per store per week) -population = getml.DataFrame.from_db( - name="population", - table_name="population_weekly_by_store_with_target" +# Load weekly sales data (one row per store per week) +weekly_sales_by_store = getml.DataFrame.from_db( + name="weekly_sales_by_store", + table_name="weekly_sales_by_store_with_target" ) # Set roles -population.set_role("snapshot_id", getml.data.roles.join_key) -population.set_role("store_id", getml.data.roles.join_key) -population.set_role("reference_date", getml.data.roles.time_stamp) -population.set_role("next_week_sales", getml.data.roles.target) -population.set_role("store_name", getml.data.roles.categorical) +weekly_sales_by_store.set_role("snapshot_id", getml.data.roles.join_key) +weekly_sales_by_store.set_role("store_id", getml.data.roles.join_key) +weekly_sales_by_store.set_role("reference_date", getml.data.roles.time_stamp) +weekly_sales_by_store.set_role("next_week_sales", getml.data.roles.target) +weekly_sales_by_store.set_role("store_name", getml.data.roles.categorical) # Boolean flags can be used as features or for filtering -population.set_role( +weekly_sales_by_store.set_role( ["is_full_week_after_opening", "has_order_activity", "has_min_history"], getml.data.roles.categorical ) @@ -103,7 +103,7 @@ orders.set_role("store_id", getml.data.roles.join_key) orders.set_role("ordered_at", getml.data.roles.time_stamp) # Define container with store-specific relationships -container = getml.data.Container(population=population) +container = getml.data.Container(population=weekly_sales_by_store) container.add( stores=stores, orders=orders, @@ -138,7 +138,7 @@ The boolean flags can be used to filter training data: ```python # Only use rows with sufficient history -filtered = population[population["has_min_history"] == True] +filtered_weekly_sales = weekly_sales_by_store[weekly_sales_by_store["has_min_history"] == True] # Or use as features - getML can learn from these patterns ``` diff --git a/integration/snowflake/pyproject.toml b/integration/snowflake/pyproject.toml index 9ceb647..9c04923 100644 --- a/integration/snowflake/pyproject.toml +++ b/integration/snowflake/pyproject.toml @@ -29,12 +29,16 @@ requires-python = ">=3.12" dependencies = [ "fastparquet>=2024.11.0", + "ipykernel>=7.1.0", "jafgen>=0.4.14", "pandas>=2.3.3", - "pyarrow>=22.0.0", + "pyarrow>=18.0.0", + "pydantic>=2.12.5", "pydantic-settings>=2.12.0", - "snowflake-connector-python>=4.1.0", + "snowflake-connector-python>=3.17.3", "snowflake-snowpark-python>=1.42.0", + "getml>=1.0.0", + "snowflake-ml-python>=1.20.0", ] [dependency-groups] diff --git a/integration/snowflake/data/_sql_loader.py b/integration/snowflake/sql_loader.py similarity index 75% rename from integration/snowflake/data/_sql_loader.py rename to integration/snowflake/sql_loader.py index 3ef67eb..243c040 100644 --- a/integration/snowflake/data/_sql_loader.py +++ b/integration/snowflake/sql_loader.py @@ -1,13 +1,19 @@ -"""Internal SQL file loading utilities. +"""SQL file loading utilities. This module provides utilities for loading and formatting SQL queries -from the sql/ directory. It is intended for internal use within the -data package only. +from the data/sql/ directory. + +Usage example: + from sql_loader import load_sql + + sql = load_sql("preparation/create_schema.sql", schema_name="PREPARED") """ +from __future__ import annotations + from pathlib import Path -_SQL_DIR = Path(__file__).parent / "sql" +_SQL_DIR = Path(__file__).parent / "data" / "sql" def load_sql(path: str, **kwargs: str) -> str: diff --git a/integration/snowflake/tests/integration/conftest.py b/integration/snowflake/tests/integration/conftest.py index e95ea7f..4aafdfd 100644 --- a/integration/snowflake/tests/integration/conftest.py +++ b/integration/snowflake/tests/integration/conftest.py @@ -3,10 +3,16 @@ Provides session-scoped fixtures that: 1. Run cleanup of stale JAFFLE_SHOP_TEST_* databases before tests 2. Create an isolated test database (JAFFLE_SHOP_TEST_{uuid}) for test isolation -3. Clean up test database after the test session completes +3. Configure storage access for cloud bucket with Parquet files +4. Clean up test database after the test session completes -Tests use the actual RAW and PREPARED schemas within the isolated test database, -allowing the real ingestion and preparation functions to run without modification. +Storage Configuration: +- GCS: Set GCS_STORAGE_INTEGRATION to the name of a storage integration + with access to gs://static.getml.com/datasets/jaffle_shop/ +- S3: Set S3_BUCKET_URL to a public S3 bucket with Parquet files + (e.g., s3://your-bucket/jaffle_shop/) + +If neither is set, tests will be skipped. """ # ruff: noqa: G004 @@ -16,15 +22,18 @@ import sys import uuid from collections.abc import Generator +from enum import Enum from pathlib import Path import pytest from pydantic import ValidationError +from pydantic.dataclasses import dataclass from snowflake.snowpark import Session # Add project root to path for imports sys.path.insert(0, str(Path(__file__).parent.parent.parent)) +from data.ingestion import DEFAULT_GCS_BUCKET, load_from_gcs, load_from_s3 from settings import SnowflakeSettings from snowflake_session import create_session from tests.cleanup_test_resources import cleanup_test_databases @@ -32,6 +41,45 @@ logger = logging.getLogger(__name__) +class CloudProvider(Enum): + """Supported cloud storage providers for data ingestion.""" + + GCS = "gcs" + S3 = "s3" + + +@dataclass(frozen=True) +class StorageConfig: + """Configuration for cloud storage access in integration tests. + + Encapsulates the parameters needed to call either load_from_gcs or + load_from_s3, providing a unified interface for test fixtures. + """ + + provider: CloudProvider + bucket_url: str + storage_integration: str | None = None + + +def load_jaffle_shop_data( + session: Session, + config: StorageConfig, +) -> dict[str, int]: + """Load Jaffle Shop data using the appropriate loader for the storage provider. + + Dispatches to load_from_gcs or load_from_s3 based on the config's provider. + """ + if config.provider == CloudProvider.GCS: + if config.storage_integration is None: + raise ValueError("GCS provider requires storage_integration") + return load_from_gcs( + session, + storage_integration=config.storage_integration, + bucket=config.bucket_url, + ) + return load_from_s3(session, bucket=config.bucket_url) + + def _snowflake_credentials_available() -> bool: """Check if required Snowflake environment variables are set.""" required_vars: list[str] = [ @@ -44,6 +92,34 @@ def _snowflake_credentials_available() -> bool: return all(os.environ.get(var) for var in required_vars) +def _get_storage_config() -> StorageConfig | None: + """Build StorageConfig from environment variables. + + Priority: + 1. GCS with storage integration (GCS_STORAGE_INTEGRATION) + 2. S3 public bucket (S3_BUCKET_URL) + + Returns: + StorageConfig if configured, None otherwise. + """ + gcs_integration = os.environ.get("GCS_STORAGE_INTEGRATION") + if gcs_integration: + return StorageConfig( + provider=CloudProvider.GCS, + bucket_url=DEFAULT_GCS_BUCKET, + storage_integration=gcs_integration, + ) + + s3_bucket_url = os.environ.get("S3_BUCKET_URL") + if s3_bucket_url: + return StorageConfig( + provider=CloudProvider.S3, + bucket_url=s3_bucket_url, + ) + + return None + + @pytest.fixture(scope="session") def integration_test_id() -> str: """Generate unique identifier for this test session. @@ -69,6 +145,28 @@ def snowflake_settings() -> SnowflakeSettings: pytest.skip(f"Invalid Snowflake configuration: {e}") +@pytest.fixture(scope="session") +def storage_config() -> StorageConfig: + """Create StorageConfig for cloud storage access. + + Supports two configuration methods via environment variables: + 1. GCS: Set GCS_STORAGE_INTEGRATION to use gs://static.getml.com/datasets/jaffle_shop/ + 2. S3: Set S3_BUCKET_URL to a public S3 bucket with Parquet files + + Skips tests if neither is configured. + """ + config = _get_storage_config() + if config is None: + pytest.skip( + "No storage configuration available. Set one of:\n" + " - GCS_STORAGE_INTEGRATION: Name of Snowflake storage integration " + "for gs://static.getml.com/datasets/jaffle_shop/\n" + " - S3_BUCKET_URL: Public S3 bucket URL with Parquet files " + "(e.g., s3://your-bucket/jaffle_shop/)" + ) + return config + + @pytest.fixture(scope="session") def test_database( snowflake_settings: SnowflakeSettings, diff --git a/integration/snowflake/tests/integration/test_pipeline.py b/integration/snowflake/tests/integration/test_pipeline.py index 2a1b5b4..bba6e08 100644 --- a/integration/snowflake/tests/integration/test_pipeline.py +++ b/integration/snowflake/tests/integration/test_pipeline.py @@ -4,6 +4,10 @@ functionality of data ingestion and preparation. Run with: uv run pytest tests/integration/ -m integration + +Required environment variables: +- SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, etc. +- GCS_STORAGE_INTEGRATION: Name of the storage integration for GCS access """ # ruff: noqa: E501 @@ -18,8 +22,10 @@ # Add project root to path for imports sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -from data.ingestion import JAFFLE_SHOP_TABLES, load_jaffle_shop_data -from data.preparation import prepare_weekly_sales_by_store +from data.ingestion import JAFFLE_SHOP_TABLE_NAMES +from data.preparation import create_weekly_sales_by_store_with_target + +from .conftest import StorageConfig, load_jaffle_shop_data logger = logging.getLogger(__name__) @@ -27,18 +33,19 @@ class TestDataIngestion: - """Integration tests for S3 to Snowflake data loading.""" + """Integration tests for cloud storage to Snowflake data loading.""" @pytest.mark.dependency(name="data_ingestion") def test_load_jaffle_shop_data_creates_tables_with_rows( self, snowflake_session: Session, + storage_config: StorageConfig, ) -> None: """Verify all Jaffle Shop tables are created with data.""" - results = load_jaffle_shop_data(snowflake_session) + results = load_jaffle_shop_data(snowflake_session, storage_config) # Verify all tables were loaded - assert set(results.keys()) == set(JAFFLE_SHOP_TABLES.keys()) + assert set(results.keys()) == set(JAFFLE_SHOP_TABLE_NAMES) # Verify all tables have rows for table_name, row_count in results.items(): @@ -60,6 +67,7 @@ def test_tables_have_expected_columns( column_names = [str(row["COLUMN_NAME"]) for row in result] # pyright: ignore[reportUnknownArgumentType] + # Column names are inferred from Parquet, check key columns exist expected_columns = [ "ID", "CUSTOMER", @@ -69,7 +77,8 @@ def test_tables_have_expected_columns( "TAX_PAID", "ORDER_TOTAL", ] - assert column_names == expected_columns + for col in expected_columns: + assert col in column_names, f"Expected column {col} not found" class TestDataPreparation: @@ -81,7 +90,7 @@ def test_prepare_weekly_sales_creates_weekly_stores_table( snowflake_session: Session, ) -> None: """Verify preparation creates weekly_stores table with data.""" - prepare_weekly_sales_by_store(snowflake_session) + create_weekly_sales_by_store_with_target(snowflake_session) # Verify table was created with data result: list[Row] = snowflake_session.sql(""" @@ -141,12 +150,12 @@ def test_target_view_exists_with_sales_columns( self, snowflake_session: Session, ) -> None: - """Verify population_weekly_by_store_with_target view exists with target columns.""" + """Verify weekly_sales_by_store_with_target view exists with target columns.""" result: list[Row] = snowflake_session.sql(""" SELECT COLUMN_NAME FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = 'PREPARED' - AND TABLE_NAME = 'POPULATION_WEEKLY_BY_STORE_WITH_TARGET' + AND TABLE_NAME = 'WEEKLY_SALES_BY_STORE_WITH_TARGET' ORDER BY ORDINAL_POSITION """).collect() @@ -168,7 +177,7 @@ def test_target_view_has_valid_sales_data( COUNT(*) as total_rows, SUM(CASE WHEN next_week_sales > 0 THEN 1 ELSE 0 END) as rows_with_sales, AVG(next_week_sales) as avg_sales - FROM PREPARED.population_weekly_by_store_with_target + FROM PREPARED.weekly_sales_by_store_with_target """).collect() total_rows = int(result[0]["TOTAL_ROWS"]) # pyright: ignore[reportArgumentType] diff --git a/integration/snowflake/tests/test_ingestion.py b/integration/snowflake/tests/test_ingestion.py index 86f6612..4aada24 100644 --- a/integration/snowflake/tests/test_ingestion.py +++ b/integration/snowflake/tests/test_ingestion.py @@ -1,7 +1,7 @@ """Tests for data ingestion module. -Tests error handling, rollback behavior, and successful data loading -orchestration logic using mocked Snowflake sessions. +Tests error handling, rollback behavior, storage integration auto-creation, +and successful data loading orchestration logic using mocked Snowflake sessions. """ # pyright: reportAny=false @@ -11,26 +11,95 @@ import pytest from data.ingestion import ( + DEFAULT_GCS_BUCKET, + JAFFLE_SHOP_TABLE_NAMES, DataIngestionError, - load_jaffle_shop_data, - load_single_table, - rollback_transaction, + get_table_names, + load_from_gcs, + load_from_s3, ) -class TestLoadJaffleShopData: - """Tests for the main load_jaffle_shop_data orchestration function.""" +def _extract_sql_calls(mock_session: MagicMock) -> list[str]: + """Extract SQL strings from mock session.sql() calls. + + Handles both positional and keyword argument styles: + - session.sql("SELECT ...") + - session.sql(query="SELECT ...") + """ + calls: list[str] = [] + for call in mock_session.sql.call_args_list: + if call.args: + calls.append(call.args[0]) + elif "query" in call.kwargs: + calls.append(call.kwargs["query"]) + return calls + + +class TestGetTableNames: + """Tests for get_table_names helper function.""" + + def test_returns_dict_with_all_jaffle_shop_tables(self) -> None: + """Verify get_table_names returns all expected table names.""" + result = get_table_names() + + assert isinstance(result, dict) + assert len(result) == len(JAFFLE_SHOP_TABLE_NAMES) + expected_keys = { + "customers", + "orders", + "order_items", + "products", + "stores", + "supplies", + } + assert set(result.keys()) == expected_keys + + def test_uses_default_raw_schema(self) -> None: + """Verify default RAW schema is used when not specified.""" + result = get_table_names() + + assert all(name.startswith("RAW.") for name in result.values()) + assert result["orders"] == "RAW.raw_orders" + + def test_uses_custom_schema(self) -> None: + """Verify custom schema is used when specified.""" + result = get_table_names(schema="CUSTOM") + + assert all(name.startswith("CUSTOM.") for name in result.values()) + assert result["orders"] == "CUSTOM.raw_orders" + + def test_preserves_raw_prefix_in_table_name(self) -> None: + """Verify the raw_ prefix is preserved in the qualified table name.""" + result = get_table_names("MY_SCHEMA") + + assert result["customers"] == "MY_SCHEMA.raw_customers" + assert result["stores"] == "MY_SCHEMA.raw_stores" + + +class TestLoadFromS3: + """Tests for load_from_s3 function.""" + + def test_raises_error_for_invalid_bucket_url(self, mock_session: MagicMock) -> None: + """Verify error raised when bucket URL doesn't start with s3://.""" + with pytest.raises(DataIngestionError) as exc_info: + load_from_s3(mock_session, bucket="gcs://wrong-scheme/path") + + assert "Invalid S3 bucket URL" in str(exc_info.value) + assert "Must start with s3://" in str(exc_info.value) def test_returns_dict_with_all_table_names_on_success( self, mock_session: MagicMock ) -> None: """Verify successful load returns dict mapping table names to row counts.""" - # Configure mock to return appropriate responses for different SQL calls mock_session.sql.return_value.collect.return_value = [ - {"COUNT": 100, "rows_parsed": 100} + {"COUNT": 100, "rows_loaded": 100} ] - result = load_jaffle_shop_data(mock_session) + result = load_from_s3( + mock_session, + bucket="s3://test-bucket/jaffle-shop", + ) assert isinstance(result, dict) expected_tables = { @@ -48,88 +117,161 @@ def test_executes_begin_and_commit_on_success( ) -> None: """Verify transaction BEGIN and COMMIT are called on successful load.""" mock_session.sql.return_value.collect.return_value = [ - {"COUNT": 100, "rows_parsed": 100} + {"COUNT": 100, "rows_loaded": 100} ] - _ = load_jaffle_shop_data(mock_session) + _ = load_from_s3(mock_session, bucket="s3://test-bucket/data") - sql_calls = [c[0][0] for c in mock_session.sql.call_args_list] + sql_calls = _extract_sql_calls(mock_session) assert "BEGIN" in sql_calls assert "COMMIT" in sql_calls def test_executes_rollback_on_exception(self, mock_session: MagicMock) -> None: """Verify ROLLBACK is called when an exception occurs during loading.""" - # First call (BEGIN) succeeds, subsequent calls fail mock_session.sql.return_value.collect.side_effect = [ [], # BEGIN Exception("Database error"), # Schema creation fails ] with pytest.raises(DataIngestionError): - _ = load_jaffle_shop_data(mock_session) + _ = load_from_s3(mock_session, bucket="s3://test-bucket/data") - sql_calls = [c[0][0] for c in mock_session.sql.call_args_list] + sql_calls = _extract_sql_calls(mock_session) assert "ROLLBACK" in sql_calls - def test_wraps_exception_in_data_ingestion_error( + def test_uses_custom_destination_schema(self, mock_session: MagicMock) -> None: + """Verify custom destination schema is used in SQL calls.""" + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_loaded": 100} + ] + + _ = load_from_s3( + mock_session, + bucket="s3://test-bucket/data", + destination_schema="CUSTOM_SCHEMA", + ) + + sql_calls = _extract_sql_calls(mock_session) + schema_creation_calls = [c for c in sql_calls if "CREATE SCHEMA" in c] + assert any("CUSTOM_SCHEMA" in c for c in schema_creation_calls) + + +class TestLoadFromGcs: + """Tests for load_from_gcs function.""" + + def test_uses_default_gcs_bucket_when_not_specified( self, mock_session: MagicMock ) -> None: - """Verify exceptions are wrapped in DataIngestionError with context.""" - mock_session.sql.return_value.collect.side_effect = [ - [], # BEGIN - Exception("Connection lost"), + """Verify default GCS bucket URL is used when bucket not specified.""" + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_loaded": 100} ] - with pytest.raises(DataIngestionError) as exc_info: - _ = load_jaffle_shop_data(mock_session) + _ = load_from_gcs( + mock_session, + storage_integration="TEST_INTEGRATION", + ) - assert "Connection lost" in str(exc_info.value) - assert "Failed to load Jaffle Shop data" in str(exc_info.value) + sql_calls = _extract_sql_calls(mock_session) + stage_calls = [c for c in sql_calls if "CREATE OR REPLACE STAGE" in c] + assert any(DEFAULT_GCS_BUCKET in c for c in stage_calls) + + def test_verifies_storage_integration_exists(self, mock_session: MagicMock) -> None: + """Verify storage integration is checked before loading.""" + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_loaded": 100} + ] + _ = load_from_gcs( + mock_session, + storage_integration="MY_INTEGRATION", + ) -class TestLoadSingleTable: - """Tests for individual table loading logic.""" + sql_calls = _extract_sql_calls(mock_session) + describe_calls = [c for c in sql_calls if "DESCRIBE" in c.upper()] + assert len(describe_calls) > 0 - def test_raises_error_when_row_count_is_zero(self, mock_session: MagicMock) -> None: - """Verify DataIngestionError raised when loaded table has 0 rows.""" - # Mock: CREATE succeeds, COPY succeeds, COUNT returns 0 - mock_session.sql.return_value.collect.side_effect = [ - [], # CREATE TABLE - [{"rows_parsed": 0}], # COPY INTO - [{"COUNT": 0}], # SELECT COUNT(*) - ] + def test_raises_error_when_integration_missing_and_no_factory( + self, mock_session: MagicMock + ) -> None: + """Verify error raised when integration missing and no factory provided.""" + mock_session.sql.return_value.collect.side_effect = Exception( + "Integration not found" + ) with pytest.raises(DataIngestionError) as exc_info: - _ = load_single_table(mock_session, "test_table", "(id TEXT)") + _ = load_from_gcs( + mock_session, + storage_integration="MISSING_INTEGRATION", + ) - assert "test_table" in str(exc_info.value) - assert "0 rows" in str(exc_info.value) + assert "does not exist" in str(exc_info.value) + assert "integration_factory" in str(exc_info.value) - def test_returns_row_count_on_success(self, mock_session: MagicMock) -> None: - """Verify function returns row count when loading succeeds.""" - mock_session.sql.return_value.collect.side_effect = [ - [], # CREATE TABLE - [{"rows_parsed": 500}], # COPY INTO - [{"COUNT": 500}], # SELECT COUNT(*) + def test_attempts_auto_creation_when_factory_provided( + self, mock_session: MagicMock + ) -> None: + """Verify auto-creation is attempted when factory is provided.""" + mock_factory = MagicMock() + call_count = 0 + + def sql_side_effect( + sql: str | None = None, query: str | None = None + ) -> MagicMock: + nonlocal call_count + sql_str = sql or query or "" + result = MagicMock() + if "DESCRIBE" in sql_str.upper(): + call_count += 1 + if call_count == 1: + result.collect.side_effect = Exception("Integration not found") + else: + result.collect.return_value = [] + else: + result.collect.return_value = [{"COUNT": 100, "rows_loaded": 100}] + return result + + mock_session.sql.side_effect = sql_side_effect + + _ = load_from_gcs( + mock_session, + storage_integration="NEW_INTEGRATION", + integration_factory=mock_factory, + ) + + mock_factory.assert_called_once() + + def test_uses_storage_integration_in_stage_creation( + self, mock_session: MagicMock + ) -> None: + """Verify storage integration is included in stage creation SQL.""" + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_loaded": 100} ] - result = load_single_table(mock_session, "test_table", "(id TEXT)") + _ = load_from_gcs( + mock_session, + storage_integration="MY_GCS_INTEGRATION", + ) - assert result == 500 + sql_calls = _extract_sql_calls(mock_session) + stage_calls = [c for c in sql_calls if "STORAGE_INTEGRATION" in c] + assert len(stage_calls) > 0 + assert any("MY_GCS_INTEGRATION" in c for c in stage_calls) -class TestRollbackTransaction: - """Tests for transaction rollback behavior.""" +class TestDataIngestionError: + """Tests for error handling and messaging.""" - def test_executes_rollback_sql(self, mock_session: MagicMock) -> None: - """Verify ROLLBACK SQL is executed.""" - rollback_transaction(mock_session) - - mock_session.sql.assert_called_with("ROLLBACK") + def test_wraps_exception_with_context(self, mock_session: MagicMock) -> None: + """Verify exceptions are wrapped in DataIngestionError with context.""" + mock_session.sql.return_value.collect.side_effect = [ + [], # BEGIN + Exception("Connection lost"), + ] - def test_suppresses_rollback_exceptions(self, mock_session: MagicMock) -> None: - """Verify exceptions during rollback are suppressed (logged only).""" - mock_session.sql.return_value.collect.side_effect = Exception("Rollback failed") + with pytest.raises(DataIngestionError) as exc_info: + _ = load_from_s3(mock_session, bucket="s3://test/data") - # Should not raise - rollback_transaction(mock_session) + assert "Connection lost" in str(exc_info.value) + assert "Failed to load Jaffle Shop data" in str(exc_info.value) diff --git a/integration/snowflake/tests/test_preparation.py b/integration/snowflake/tests/test_preparation.py index 365454f..3f77cfe 100644 --- a/integration/snowflake/tests/test_preparation.py +++ b/integration/snowflake/tests/test_preparation.py @@ -6,52 +6,144 @@ # pyright: reportAny=false -from unittest.mock import MagicMock +from unittest.mock import MagicMock, patch import pytest -from data.preparation import prepare_weekly_sales_by_store, validate_raw_schema +from data.preparation import ( + DEFAULT_POPULATION_TABLE_NAME, + DataPreparationError, + _validate_source_tables, + create_weekly_sales_by_store_with_target, +) -class TestValidateRawSchema: - """Tests for RAW schema validation logic.""" +class TestValidateSourceTables: + """Tests for source table validation logic.""" - def test_raises_value_error_when_table_query_fails( - self, mock_session: MagicMock - ) -> None: - """Verify ValueError raised when required table doesn't exist.""" + def test_raises_error_when_table_query_fails(self, mock_session: MagicMock) -> None: + """Verify DataPreparationError raised when required table doesn't exist.""" mock_session.sql.return_value.collect.side_effect = Exception( "Table RAW.raw_stores does not exist" ) - with pytest.raises(ValueError, match="Failed to validate RAW schema"): - validate_raw_schema(mock_session) + with pytest.raises(DataPreparationError, match="Failed to validate"): + _validate_source_tables(mock_session, "RAW") - def test_raises_value_error_when_result_is_empty( - self, mock_session: MagicMock - ) -> None: - """Verify ValueError raised when table query returns empty result.""" + def test_raises_error_when_result_is_empty(self, mock_session: MagicMock) -> None: + """Verify DataPreparationError raised when table query returns empty result.""" mock_session.sql.return_value.collect.return_value = [] - with pytest.raises(ValueError, match="does not exist or is not accessible"): - validate_raw_schema(mock_session) + with pytest.raises( + DataPreparationError, match="does not exist or is not accessible" + ): + _validate_source_tables(mock_session, "RAW") def test_succeeds_when_tables_exist(self, mock_session: MagicMock) -> None: """Verify no error when required tables exist and are accessible.""" mock_session.sql.return_value.collect.return_value = [{"count": 100}] - # Should not raise - validate_raw_schema(mock_session) + _validate_source_tables(mock_session, "RAW") + + def test_uses_provided_source_schema(self, mock_session: MagicMock) -> None: + """Verify provided source schema is used in validation queries.""" + mock_session.sql.return_value.collect.return_value = [{"count": 100}] + _validate_source_tables(mock_session, "CUSTOM_SOURCE") -class TestPrepareWeeklySalesByStore: + sql_calls = [c[0][0] for c in mock_session.sql.call_args_list] + assert all("CUSTOM_SOURCE" in c for c in sql_calls) + + +class TestCreateWeeklySalesByStoreWithTarget: """Tests for the main preparation pipeline.""" - def test_validates_raw_schema_first(self, mock_session: MagicMock) -> None: - """Verify pipeline validates RAW schema before proceeding.""" + def test_validates_source_tables_first(self, mock_session: MagicMock) -> None: + """Verify pipeline validates source tables before proceeding.""" mock_session.sql.return_value.collect.side_effect = Exception( "Table does not exist" ) - with pytest.raises(ValueError, match="RAW schema"): - prepare_weekly_sales_by_store(mock_session) + with pytest.raises(DataPreparationError, match="Failed to validate"): + create_weekly_sales_by_store_with_target(mock_session) + + @patch("data.preparation._display_recent_snapshots") + @patch("data.preparation._perform_data_quality_check") + @patch("data.preparation._display_overall_statistics") + @patch("data.preparation._display_store_statistics") + @patch("data.preparation._display_sample_data") + @patch("data.preparation._create_target_view") + @patch("data.preparation._create_weekly_stores_table") + @patch("data.preparation._analyze_and_display_stores") + @patch("data.preparation._ensure_target_schema") + @patch("data.preparation._validate_source_tables") + def test_uses_default_schemas( # noqa: PLR0913, PLR0917 + self, + mock_validate: MagicMock, + mock_ensure_schema: MagicMock, + mock_analyze: MagicMock, + mock_create_table: MagicMock, + mock_create_view: MagicMock, + mock_sample: MagicMock, + mock_store_stats: MagicMock, + mock_overall_stats: MagicMock, + mock_quality: MagicMock, + mock_recent: MagicMock, + mock_session: MagicMock, + ) -> None: + """Verify default RAW and PREPARED schemas are used.""" + mock_create_table.return_value = [] + + result = create_weekly_sales_by_store_with_target(mock_session) + + mock_validate.assert_called_once_with(mock_session, "RAW") + mock_ensure_schema.assert_called_once_with(mock_session, "PREPARED") + mock_analyze.assert_called_once_with(mock_session, "RAW") + mock_create_table.assert_called_once_with(mock_session, "RAW", "PREPARED") + mock_create_view.assert_called_once_with( + mock_session, "RAW", "PREPARED", DEFAULT_POPULATION_TABLE_NAME + ) + assert result == f"PREPARED.{DEFAULT_POPULATION_TABLE_NAME}" + + @patch("data.preparation._display_recent_snapshots") + @patch("data.preparation._perform_data_quality_check") + @patch("data.preparation._display_overall_statistics") + @patch("data.preparation._display_store_statistics") + @patch("data.preparation._display_sample_data") + @patch("data.preparation._create_target_view") + @patch("data.preparation._create_weekly_stores_table") + @patch("data.preparation._analyze_and_display_stores") + @patch("data.preparation._ensure_target_schema") + @patch("data.preparation._validate_source_tables") + def test_uses_custom_schemas( # noqa: PLR0913, PLR0917 + self, + mock_validate: MagicMock, + mock_ensure_schema: MagicMock, + mock_analyze: MagicMock, + mock_create_table: MagicMock, + mock_create_view: MagicMock, + mock_sample: MagicMock, + mock_store_stats: MagicMock, + mock_overall_stats: MagicMock, + mock_quality: MagicMock, + mock_recent: MagicMock, + mock_session: MagicMock, + ) -> None: + """Verify custom source and target schemas are used when provided.""" + mock_create_table.return_value = [] + + result = create_weekly_sales_by_store_with_target( + mock_session, + source_schema="MY_RAW", + target_schema="MY_PREPARED", + table_name="CUSTOM_TABLE", + ) + + mock_validate.assert_called_once_with(mock_session, "MY_RAW") + mock_ensure_schema.assert_called_once_with(mock_session, "MY_PREPARED") + mock_analyze.assert_called_once_with(mock_session, "MY_RAW") + mock_create_table.assert_called_once_with(mock_session, "MY_RAW", "MY_PREPARED") + mock_create_view.assert_called_once_with( + mock_session, "MY_RAW", "MY_PREPARED", "CUSTOM_TABLE" + ) + assert result == "MY_PREPARED.CUSTOM_TABLE" diff --git a/integration/snowflake/tests/test_data_init.py b/integration/snowflake/tests/test_sql_loader.py similarity index 57% rename from integration/snowflake/tests/test_data_init.py rename to integration/snowflake/tests/test_sql_loader.py index a8392d9..baa4ac6 100644 --- a/integration/snowflake/tests/test_data_init.py +++ b/integration/snowflake/tests/test_sql_loader.py @@ -1,4 +1,4 @@ -"""Tests for data package utilities. +"""Tests for sql_loader module. Tests the load_sql function which handles SQL file loading and parameter substitution. @@ -8,7 +8,7 @@ import pytest -from data import load_sql +from sql_loader import load_sql class TestLoadSql: @@ -16,38 +16,45 @@ class TestLoadSql: def test_loads_existing_sql_file(self) -> None: """Verify load_sql reads content from existing SQL file.""" - content = load_sql("preparation/create_prepared_schema.sql") + content = load_sql("preparation/create_schema.sql", schema_name="PREPARED") assert "CREATE SCHEMA" in content assert "PREPARED" in content def test_applies_formatting_when_kwargs_provided(self) -> None: """Verify load_sql applies string formatting with provided kwargs.""" - content = load_sql( - "preparation/sample_data_by_store.sql", + content: str = load_sql( + path="preparation/sample_data_by_store.sql", + target_schema="PREPARED", + table_name="WEEKLY_SALES", store_name="Test Store", ) assert "Test Store" in content + assert "PREPARED.WEEKLY_SALES" in content def test_returns_raw_content_when_no_kwargs(self) -> None: """Verify load_sql returns unmodified content without kwargs.""" - content = load_sql("ingestion/create_raw_schema.sql") + content: str = load_sql( + path="ingestion/create_schema.sql", + schema_name="RAW", + ) - # Content should contain the placeholder syntax if any exist, - # or just be the raw SQL + # Content should contain the schema name after formatting assert isinstance(content, str) assert len(content) > 0 + assert "RAW" in content def test_raises_file_not_found_for_missing_file(self) -> None: """Verify FileNotFoundError propagates for nonexistent SQL files.""" with pytest.raises(FileNotFoundError): - _ = load_sql("nonexistent/missing.sql") + _ = load_sql(path="nonexistent/missing.sql") def test_preserves_placeholders_when_no_kwargs(self) -> None: """Verify load_sql preserves {placeholders} when no kwargs provided.""" - # sample_data_by_store.sql has {store_name} placeholder - content: str = load_sql("preparation/sample_data_by_store.sql") + # sample_data_by_store.sql has {store_name} and {table_name} placeholders + content: str = load_sql(path="preparation/sample_data_by_store.sql") - # Placeholder should remain in the content + # Placeholders should remain in the content assert "{store_name}" in content + assert "{table_name}" in content diff --git a/integration/snowflake/uv.lock b/integration/snowflake/uv.lock index 5ee2869..3ef8d12 100644 --- a/integration/snowflake/uv.lock +++ b/integration/snowflake/uv.lock @@ -1,5 +1,143 @@ version = 1 requires-python = ">=3.12" +resolution-markers = [ + "python_full_version < '3.14'", + "python_full_version >= '3.14'", +] + +[[package]] +name = "aiobotocore" +version = "2.26.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "aiohttp" }, + { name = "aioitertools" }, + { name = "botocore" }, + { name = "jmespath" }, + { name = "multidict" }, + { name = "python-dateutil" }, + { name = "wrapt" }, +] 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"https://files.pythonhosted.org/packages/73/ae/b48f95715333080afb75a4504487cbe142cae1268afc482d06692d605ae6/yarl-1.22.0-py3-none-any.whl", hash = "sha256:1380560bdba02b6b6c90de54133c81c9f2a453dee9912fe58c1dcced1edb7cff", size = 46814 }, +] From 41372e0baa043fd553f60f1ef1b4f26b0e89f526 Mon Sep 17 00:00:00 2001 From: Alexandros Ladas Date: Fri, 5 Dec 2025 14:46:18 +0100 Subject: [PATCH 4/4] feat(snowflake): Implement Snowflake infrastructure bootstrap and session management - Added bootstrap functionality to ensure the existence of Snowflake warehouse and database. - Created SnowflakeSettings class for managing authentication and connection settings. - Implemented session management for Snowflake using Snowpark. - Developed SQL loading utilities for dynamic SQL execution. - Added SQL scripts for creating schemas, tables, and stages, as well as data ingestion processes. - Prepared a script to load and prepare Jaffle Shop data for integration with getML Feature Store. --- integration/snowflake/.mcp.json | 15 + integration/snowflake/bootstrap.py | 299 -- integration/snowflake/data/__init__.py | 32 +- integration/snowflake/data/_bootstrap.py | 182 + integration/snowflake/data/_settings.py | 53 + .../snowflake/data/_snowflake_session.py | 54 + .../{sql_loader.py => data/_sql_loader.py} | 4 +- integration/snowflake/data/ingestion.py | 459 +-- integration/snowflake/data/preparation.py | 97 +- .../data/sql/bootstrap/create_database.sql | 1 - .../create_storage_integration_gcs.sql | 7 - .../data/sql/bootstrap/create_warehouse.sql | 1 - .../describe_storage_integration.sql | 3 - .../validate_storage_integration.sql | 9 - .../{ingestion => common}/create_schema.sql | 1 - .../data/sql/ingestion/copy_into_table.sql | 6 + .../data/sql/ingestion/copy_parquet.sql | 6 - .../sql/ingestion/create_internal_stage.sql | 4 + .../data/sql/ingestion/create_stage_gcs.sql | 6 - .../create_storage_integration_gcs.sql | 7 - .../ingestion/create_table_from_parquet.sql | 16 + .../describe_storage_integration.sql | 3 - .../sql/ingestion/infer_and_create_table.sql | 12 - .../validate_storage_integration.sql | 3 - .../data/sql/preparation/analyze_stores.sql | 10 +- .../data/sql/preparation/calculate_target.sql | 41 +- .../data/sql/preparation/count_snapshots.sql | 2 - .../data/sql/preparation/count_stores.sql | 2 - .../data/sql/preparation/create_schema.sql | 2 - .../sql/preparation/create_weekly_stores.sql | 20 +- .../preparation/recent_snapshots_by_store.sql | 10 - .../sql/preparation/sample_data_by_store.sql | 10 - .../sql/preparation/snapshots_by_store.sql | 11 + .../sql/preparation/snapshots_per_store.sql | 9 - .../sql/preparation/weekly_stores_summary.sql | 27 + .../docs/GCS_STORAGE_INTEGRATION_SETUP.md | 187 -- .../prepare_jaffle_shop_data_for_snowflake.py | 60 + integration/snowflake/pyproject.toml | 1 + integration/snowflake/settings.py | 61 - integration/snowflake/snowflake_session.py | 85 - .../snowflake/tests/cleanup_test_resources.py | 3 +- .../snowflake/tests/integration/conftest.py | 65 +- .../tests/integration/test_pipeline.py | 2 +- integration/snowflake/tests/test_ingestion.py | 344 +- .../snowflake/tests/test_preparation.py | 88 +- .../snowflake/tests/test_sql_loader.py | 16 +- integration/snowflake/uv.lock | 2977 +++++++++-------- 47 files changed, 2699 insertions(+), 2614 deletions(-) create mode 100644 integration/snowflake/.mcp.json delete mode 100644 integration/snowflake/bootstrap.py create mode 100644 integration/snowflake/data/_bootstrap.py create mode 100644 integration/snowflake/data/_settings.py create mode 100644 integration/snowflake/data/_snowflake_session.py rename integration/snowflake/{sql_loader.py => data/_sql_loader.py} (92%) delete mode 100644 integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql delete mode 100644 integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql delete mode 100644 integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql rename integration/snowflake/data/sql/{ingestion => common}/create_schema.sql (53%) create mode 100644 integration/snowflake/data/sql/ingestion/copy_into_table.sql delete mode 100644 integration/snowflake/data/sql/ingestion/copy_parquet.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_internal_stage.sql delete mode 100644 integration/snowflake/data/sql/ingestion/create_stage_gcs.sql delete mode 100644 integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql create mode 100644 integration/snowflake/data/sql/ingestion/create_table_from_parquet.sql delete mode 100644 integration/snowflake/data/sql/ingestion/describe_storage_integration.sql delete mode 100644 integration/snowflake/data/sql/ingestion/infer_and_create_table.sql delete mode 100644 integration/snowflake/data/sql/ingestion/validate_storage_integration.sql delete mode 100644 integration/snowflake/data/sql/preparation/count_snapshots.sql delete mode 100644 integration/snowflake/data/sql/preparation/count_stores.sql delete mode 100644 integration/snowflake/data/sql/preparation/create_schema.sql delete mode 100644 integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql delete mode 100644 integration/snowflake/data/sql/preparation/sample_data_by_store.sql create mode 100644 integration/snowflake/data/sql/preparation/snapshots_by_store.sql delete mode 100644 integration/snowflake/data/sql/preparation/snapshots_per_store.sql create mode 100644 integration/snowflake/data/sql/preparation/weekly_stores_summary.sql delete mode 100644 integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md create mode 100755 integration/snowflake/prepare_jaffle_shop_data_for_snowflake.py delete mode 100644 integration/snowflake/settings.py delete mode 100644 integration/snowflake/snowflake_session.py diff --git a/integration/snowflake/.mcp.json b/integration/snowflake/.mcp.json new file mode 100644 index 0000000..fa0750c --- /dev/null +++ b/integration/snowflake/.mcp.json @@ -0,0 +1,15 @@ +{ + "mcpServers": { + "context7": { + "type": "stdio", + "command": "npx", + "args": [ + "-y", + "@upstash/context7-mcp", + "--api-key", + "ctx7sk-409c8344-c67b-4dd0-b067-b5c7527afcce" + ], + "env": {} + } + } +} \ No newline at end of file diff --git a/integration/snowflake/bootstrap.py b/integration/snowflake/bootstrap.py deleted file mode 100644 index 87fe0ff..0000000 --- a/integration/snowflake/bootstrap.py +++ /dev/null @@ -1,299 +0,0 @@ -"""Bootstrap Snowflake infrastructure for the Jaffle Shop dataset. - -Creates the required warehouse, database, and optionally GCS storage integration. -All operations are idempotent using CREATE ... IF NOT EXISTS / CREATE OR REPLACE. - -Usage example: - from settings import SnowflakeSettings - from snowflake_session import create_session - from bootstrap import bootstrap_snowflake_infrastructure - - settings = SnowflakeSettings.from_env() - - with create_session(settings) as session: - bootstrap_snowflake_infrastructure(session, settings) - -For GCS storage integration setup: - from bootstrap import setup_gcs_storage_integration - - with create_session(settings) as session: - info = setup_gcs_storage_integration(session) - # Follow printed instructions to grant IAM permissions in GCP -""" - -# ruff: noqa: G004, T201 - -from __future__ import annotations - -import logging -from dataclasses import dataclass - -from snowflake.snowpark import Row, Session - -from settings import SnowflakeSettings -from sql_loader import load_sql - -logger: logging.Logger = logging.getLogger(__name__) - -WAREHOUSE_SIZE = "X-SMALL" -AUTO_SUSPEND_SECONDS = 60 - -DEFAULT_GCS_ALLOWED_LOCATION = "gcs://static.getml.com/datasets/" -DEFAULT_INTEGRATION_NAME = "GETML_GCS_INTEGRATION" - - -class BootstrapError(Exception): - """Raised when bootstrap operations fail.""" - - -@dataclass -class StorageIntegrationInfo: - """Information about a GCS storage integration. - - Attributes: - name: Integration name. - enabled: Whether the integration is enabled. - allowed_locations: GCS locations the integration can access. - service_account: GCS service account to grant IAM permissions to. - """ - - name: str - enabled: bool - allowed_locations: str - service_account: str - - -# ============================================================================= -# Internal Helpers -# ============================================================================= - - -def _extract_bucket_name(gcs_url: str) -> str: - """Extract bucket name from GCS URL.""" - url = gcs_url.lower().replace("gcs://", "").replace("gs://", "") - return url.split("/")[0] - - -def _print_gcp_setup_guide( - info: StorageIntegrationInfo, - bucket_name: str, -) -> None: - """Print actionable GCP setup instructions after integration creation.""" - print(f""" -====================================================================== -GCS STORAGE INTEGRATION CREATED -====================================================================== - -Integration: {info.name} -Service Account: {info.service_account} - ----------------------------------------------------------------------- -REQUIRED: Grant IAM permissions in GCP ----------------------------------------------------------------------- - -Run this command to grant read access: - - gsutil iam ch \\ - serviceAccount:{info.service_account}:objectViewer \\ - gs://{bucket_name} - ----------------------------------------------------------------------- -Then retry the data loading operation. ----------------------------------------------------------------------- - -====================================================================== -""") - - -# ============================================================================= -# Core Bootstrap Functions -# ============================================================================= - - -def bootstrap_snowflake_infrastructure( - session: Session, - settings: SnowflakeSettings, -) -> None: - """Create Snowflake warehouse and database if they don't exist. - - Raises: - BootstrapError: If warehouse or database creation fails. - """ - _create_warehouse(session, warehouse_name=settings.warehouse) - _create_database(session, database_name=settings.database) - logger.info("✓ Snowflake infrastructure bootstrap complete") - - -def _create_warehouse(session: Session, warehouse_name: str) -> None: - """Create warehouse with X-SMALL size and auto-suspend enabled.""" - logger.info(f"Creating warehouse '{warehouse_name}' if not exists...") - - sql: str = load_sql( - path="bootstrap/create_warehouse.sql", - warehouse_name=warehouse_name, - warehouse_size=WAREHOUSE_SIZE, - auto_suspend_seconds=str(AUTO_SUSPEND_SECONDS), - ) - _ = session.sql(query=sql).collect() - - logger.info(f"✓ Warehouse '{warehouse_name}' ready") - - -def _create_database(session: Session, database_name: str) -> None: - """Create database if it doesn't exist.""" - logger.info(f"Creating database '{database_name}' if not exists...") - - sql: str = load_sql( - path="bootstrap/create_database.sql", database_name=database_name - ) - _ = session.sql(query=sql).collect() - - logger.info(f"✓ Database '{database_name}' ready") - - -# ============================================================================= -# GCS Storage Integration Setup -# ============================================================================= - - -def setup_gcs_storage_integration( - session: Session, - integration_name: str = DEFAULT_INTEGRATION_NAME, - allowed_location: str = DEFAULT_GCS_ALLOWED_LOCATION, -) -> StorageIntegrationInfo: - """Create GCS storage integration and display required GCP setup commands. - - Creates the Snowflake storage integration for GCS access and prints - the gcloud commands needed to grant IAM permissions. - - Requires ACCOUNTADMIN role or CREATE INTEGRATION privilege. - - Args: - session: Active Snowflake Snowpark session. - integration_name: Name for the storage integration. - allowed_location: GCS path prefix to allow access to. - - Returns: - StorageIntegrationInfo with the service account for IAM setup. - - Raises: - BootstrapError: If integration creation fails. - """ - logger.info(f"Creating GCS storage integration '{integration_name}'...") - - try: - sql: str = load_sql( - path="bootstrap/create_storage_integration_gcs.sql", - integration_name=integration_name, - allowed_location=allowed_location, - ) - _ = session.sql(query=sql).collect() - except Exception as e: - raise BootstrapError( - f"Failed to create storage integration. " - f"Ensure you have ACCOUNTADMIN role. Error: {e}" - ) from e - - logger.info(f"✓ Storage integration '{integration_name}' created") - - info: StorageIntegrationInfo = get_storage_integration_info( - session, integration_name - ) - bucket_name: str = _extract_bucket_name(gcs_url=allowed_location) - _print_gcp_setup_guide(info, bucket_name) - - return info - - -def get_storage_integration_info( - session: Session, - integration_name: str = DEFAULT_INTEGRATION_NAME, -) -> StorageIntegrationInfo: - """Retrieve details about an existing storage integration. - - Args: - session: Active Snowflake Snowpark session. - integration_name: Name of the storage integration. - - Returns: - StorageIntegrationInfo with integration details. - - Raises: - BootstrapError: If integration doesn't exist or query fails. - """ - try: - sql: str = load_sql( - path="bootstrap/describe_storage_integration.sql", - integration_name=integration_name, - ) - rows: list[Row] = session.sql(query=sql).collect() - except Exception as e: - raise BootstrapError( - f"Storage integration '{integration_name}' not found. " - f"Run setup_gcs_storage_integration() first. Error: {e}" - ) from e - - properties: dict[str, str] = { - str(row["property"]): str(row["property_value"]) # pyright: ignore[reportUnknownArgumentType] - for row in rows - } - - return StorageIntegrationInfo( - name=integration_name, - enabled=properties.get("ENABLED", "").lower() == "true", - allowed_locations=properties.get("STORAGE_ALLOWED_LOCATIONS", ""), - service_account=properties.get("STORAGE_GCP_SERVICE_ACCOUNT", ""), - ) - - -def validate_storage_integration( - session: Session, - integration_name: str = DEFAULT_INTEGRATION_NAME, - storage_path: str = DEFAULT_GCS_ALLOWED_LOCATION, -) -> str: - """Validate a storage integration can list files in GCS. - - Uses Snowflake's native SYSTEM$VALIDATE_STORAGE_INTEGRATION function - with the 'list' action to verify read permissions on the storage path. - - Args: - session: Active Snowflake Snowpark session. - integration_name: Name of the storage integration. - storage_path: GCS path to validate access to (must be within - the integration's STORAGE_ALLOWED_LOCATIONS). - - Returns: - JSON string with validation status and action results. - - Raises: - BootstrapError: If validation fails or permissions are insufficient. - """ - logger.info(f"Validating storage integration '{integration_name}'...") - - try: - sql: str = load_sql( - path="bootstrap/validate_storage_integration.sql", - integration_name=integration_name, - storage_path=storage_path, - test_file="validation_test.tmp", - ) - rows: list[Row] = session.sql(sql).collect() - except Exception as e: - raise BootstrapError( - f"Storage integration validation failed. Error: {e}" - ) from e - else: - result = str(rows[0]["VALIDATION_RESULT"]) if rows else "No result" # pyright: ignore[reportUnknownArgumentType] - logger.info(f"Validation result: {result}") - return result - - -# Re-export for external usage -__all__ = [ - "BootstrapError", - "StorageIntegrationInfo", - "bootstrap_snowflake_infrastructure", - "get_storage_integration_info", - "setup_gcs_storage_integration", - "validate_storage_integration", -] diff --git a/integration/snowflake/data/__init__.py b/integration/snowflake/data/__init__.py index 97284db..4920eff 100644 --- a/integration/snowflake/data/__init__.py +++ b/integration/snowflake/data/__init__.py @@ -1,9 +1,12 @@ """Data operations for getML Feature Store integration with Snowflake. +When settings are provided to data loading and preparation functions, +infrastructure (warehouse, database) is automatically bootstrapped if needed. + Usage example: - from settings import SnowflakeSettings - from snowflake_session import create_session from data import ( + SnowflakeSettings, + create_session, load_from_gcs, create_weekly_sales_by_store_with_target, get_table_names, @@ -13,16 +16,14 @@ settings = SnowflakeSettings.from_env() with create_session(settings) as session: - # Load data from GCS (requires storage integration) - load_from_gcs( - session, - storage_integration="GETML_GCS_INTEGRATION", - ) + # Load data from GCS - auto-bootstraps warehouse + database + # No GCP credentials required - files are fetched via HTTPS + load_from_gcs(session, settings=settings) # Prepare weekly sales forecasting data population_table = create_weekly_sales_by_store_with_target( session, - table_name="WEEKLY_SALES_BY_STORE_WITH_TARGET", + settings=settings, ) # Access tables for Arrow export @@ -31,6 +32,15 @@ population_arrow = session.table(population_table).to_arrow() """ +from snowflake.snowpark.exceptions import SnowparkSessionException + +from ._bootstrap import ( + BootstrapError, + ensure_infrastructure, +) +from ._settings import SnowflakeSettings +from ._snowflake_session import create_session +from ._sql_loader import load_sql from .ingestion import ( DEFAULT_GCS_BUCKET, JAFFLE_SHOP_TABLE_NAMES, @@ -49,10 +59,16 @@ "DEFAULT_GCS_BUCKET", "DEFAULT_POPULATION_TABLE_NAME", "JAFFLE_SHOP_TABLE_NAMES", + "BootstrapError", "DataIngestionError", "DataPreparationError", + "SnowflakeSettings", + "SnowparkSessionException", + "create_session", "create_weekly_sales_by_store_with_target", + "ensure_infrastructure", "get_table_names", "load_from_gcs", "load_from_s3", + "load_sql", ] diff --git a/integration/snowflake/data/_bootstrap.py b/integration/snowflake/data/_bootstrap.py new file mode 100644 index 0000000..36e407f --- /dev/null +++ b/integration/snowflake/data/_bootstrap.py @@ -0,0 +1,182 @@ +"""Bootstrap Snowflake infrastructure for the Jaffle Shop dataset. + +Creates the required warehouse and database. +All operations are idempotent using CREATE ... IF NOT EXISTS. + +The main entry point is `ensure_infrastructure()` which auto-detects and creates +missing resources. It is called automatically by data loading functions when +settings are provided. + +Usage example: + from data import SnowflakeSettings, create_session, ensure_infrastructure + + settings = SnowflakeSettings.from_env() + + with create_session(settings) as session: + ensure_infrastructure(session, settings) + # Warehouse and database are now guaranteed to exist +""" + +# ruff: noqa: G004 + +from __future__ import annotations + +import logging + +from snowflake.snowpark import Row, Session +from snowflake.snowpark.exceptions import SnowparkSQLException + +from ._settings import SnowflakeSettings +from ._sql_loader import load_sql + +logger: logging.Logger = logging.getLogger(__name__) + +WAREHOUSE_SIZE = "X-SMALL" +AUTO_SUSPEND_SECONDS = 60 + + +class BootstrapError(Exception): + """Raised when bootstrap operations fail.""" + + +# ============================================================================= +# Internal Helpers +# ============================================================================= + + +def _warehouse_exists(session: Session, warehouse_name: str) -> bool: + """Check if a warehouse exists. + + Args: + session: Active Snowflake Snowpark session. + warehouse_name: Name of the warehouse to check. + + Returns: + True if warehouse exists, False otherwise. + """ + try: + rows: list[Row] = session.sql( + f"SHOW WAREHOUSES LIKE '{warehouse_name}'" + ).collect() + return len(rows) > 0 + except SnowparkSQLException: + return False + + +def _database_exists(session: Session, database_name: str) -> bool: + """Check if a database exists. + + Args: + session: Active Snowflake Snowpark session. + database_name: Name of the database to check. + + Returns: + True if database exists, False otherwise. + """ + try: + rows: list[Row] = session.sql( + f"SHOW DATABASES LIKE '{database_name}'" + ).collect() + return len(rows) > 0 + except SnowparkSQLException: + return False + + +# ============================================================================= +# Core Bootstrap Functions +# ============================================================================= + + +def ensure_infrastructure( + session: Session, + settings: SnowflakeSettings, +) -> None: + """Ensure warehouse and database exist, creating if missing. + + This is the main entry point for auto-bootstrapping. It checks if the + warehouse and database specified in settings exist, and creates them + if they don't. All operations are idempotent. + + Args: + session: Active Snowflake Snowpark session. + settings: Settings containing warehouse and database names. + + Raises: + BootstrapError: If required settings are missing or if resource + creation fails (e.g., due to insufficient privileges). + + Example: + from data import SnowflakeSettings, create_session, ensure_infrastructure + + settings = SnowflakeSettings.from_env() + with create_session(settings) as session: + ensure_infrastructure(session, settings) + # Warehouse and database are now guaranteed to exist + """ + if not settings.warehouse: + raise BootstrapError("warehouse must be specified in settings for bootstrap") + if not settings.database: + raise BootstrapError("database must be specified in settings for bootstrap") + + warehouse_ready = _warehouse_exists(session, settings.warehouse) + database_ready = _database_exists(session, settings.database) + + if warehouse_ready and database_ready: + logger.debug( + "Infrastructure already exists: warehouse=%s, database=%s", + settings.warehouse, + settings.database, + ) + return + + try: + if not warehouse_ready: + _create_warehouse(session, warehouse_name=settings.warehouse) + if not database_ready: + _create_database(session, database_name=settings.database) + logger.info("Snowflake infrastructure ready") + except Exception as e: + raise BootstrapError( + "Cannot create Snowflake infrastructure. " + "Your current role may lack CREATE WAREHOUSE or CREATE DATABASE " + "privilege.\n\n" + "Options:\n" + " 1. Ask an administrator to create the warehouse and database\n" + " 2. Switch to a role with CREATE WAREHOUSE/DATABASE privilege " + "(e.g., ACCOUNTADMIN)\n" + " 3. Create resources manually before running the pipeline\n\n" + f"Original error: {e}" + ) from e + + +def _create_warehouse(session: Session, warehouse_name: str) -> None: + """Create warehouse with X-SMALL size and auto-suspend enabled.""" + logger.info(f"Creating warehouse '{warehouse_name}' if not exists...") + + sql: str = load_sql( + path="bootstrap/create_warehouse.sql", + warehouse_name=warehouse_name, + warehouse_size=WAREHOUSE_SIZE, + auto_suspend_seconds=str(AUTO_SUSPEND_SECONDS), + ) + _ = session.sql(query=sql).collect() + + logger.info(f"✓ Warehouse '{warehouse_name}' ready") + + +def _create_database(session: Session, database_name: str) -> None: + """Create database if it doesn't exist.""" + logger.info(f"Creating database '{database_name}' if not exists...") + + sql: str = load_sql( + path="bootstrap/create_database.sql", database_name=database_name + ) + _ = session.sql(query=sql).collect() + + logger.info(f"✓ Database '{database_name}' ready") + + +__all__ = [ + "BootstrapError", + "ensure_infrastructure", +] diff --git a/integration/snowflake/data/_settings.py b/integration/snowflake/data/_settings.py new file mode 100644 index 0000000..cc25ffe --- /dev/null +++ b/integration/snowflake/data/_settings.py @@ -0,0 +1,53 @@ +"""Snowflake authentication and connection settings. + +Provides typed configuration that automatically loads from environment +variables with the SNOWFLAKE_ prefix. + +Usage example: + from data import SnowflakeSettings, create_session + + # Auto-loads from SNOWFLAKE_* environment variables + settings = SnowflakeSettings.from_env() + + with create_session(settings) as session: + session.sql("SELECT 1").collect() +""" + +from typing import ClassVar, Self + +from pydantic import Field, SecretStr +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class SnowflakeSettings(BaseSettings): + """Settings for Snowflake operations. + + Core fields (account, user, password, role) are required for all connections. + Optional fields (warehouse, database, schema_name) can be omitted for + administrative operations like creating warehouses or databases. + """ + + model_config: ClassVar[SettingsConfigDict] = SettingsConfigDict( + env_prefix="SNOWFLAKE_", + case_sensitive=False, + ) + + account: str + user: str + password: SecretStr + role: str + warehouse: str + database: str + # Can't use field name "schema" (Pydantic reserved); + # validation_alias maps standard SNOWFLAKE_SCHEMA env var to this field + schema_name: str = Field( + validation_alias="SNOWFLAKE_SCHEMA", + ) + + @classmethod + def from_env(cls) -> Self: + """Create settings instance from environment variables. + + Loads configuration from SNOWFLAKE_* environment variables. + """ + return cls.model_validate({}) diff --git a/integration/snowflake/data/_snowflake_session.py b/integration/snowflake/data/_snowflake_session.py new file mode 100644 index 0000000..cf66fe7 --- /dev/null +++ b/integration/snowflake/data/_snowflake_session.py @@ -0,0 +1,54 @@ +"""Snowflake session management for getML Feature Store integration. + +This module provides a factory function for creating Snowflake Snowpark sessions. +Sessions support the context manager protocol for automatic cleanup. + +Usage example: + from data import SnowflakeSettings, create_session + + with create_session(SnowflakeSettings.from_env()) as session: + session.sql("SELECT * FROM mytable").collect() +""" + +import logging + +from snowflake.snowpark import Session +from snowflake.snowpark.exceptions import SnowparkSessionException + +from ._settings import SnowflakeSettings + +logger: logging.Logger = logging.getLogger(__name__) + + +def create_session(settings: SnowflakeSettings) -> Session: + """Create a Snowflake Snowpark session. + + Connects using the provided settings. + + Raises: + SnowparkSessionException: If connection to Snowflake fails. + """ + connection_params: dict[str, str | int] = { + "account": settings.account, + "user": settings.user, + "password": settings.password.get_secret_value(), + "role": settings.role, + "warehouse": settings.warehouse, + "database": settings.database, + "schema": settings.schema_name, + } + + return _establish_session(connection_params) + + +def _establish_session(connection_params: dict[str, str | int]) -> Session: + """Create a Snowpark session with the given parameters.""" + try: + return Session.builder.configs(connection_params).create() + except Exception as e: + raise SnowparkSessionException( + f"Failed to create Snowflake session. " + f"Account: {connection_params.get('account')}, " + f"User: {connection_params.get('user')}. " + f"Error: {e}" + ) from e diff --git a/integration/snowflake/sql_loader.py b/integration/snowflake/data/_sql_loader.py similarity index 92% rename from integration/snowflake/sql_loader.py rename to integration/snowflake/data/_sql_loader.py index 243c040..0d63579 100644 --- a/integration/snowflake/sql_loader.py +++ b/integration/snowflake/data/_sql_loader.py @@ -4,7 +4,7 @@ from the data/sql/ directory. Usage example: - from sql_loader import load_sql + from data import load_sql sql = load_sql("preparation/create_schema.sql", schema_name="PREPARED") """ @@ -13,7 +13,7 @@ from pathlib import Path -_SQL_DIR = Path(__file__).parent / "data" / "sql" +_SQL_DIR = Path(__file__).parent / "sql" def load_sql(path: str, **kwargs: str) -> str: diff --git a/integration/snowflake/data/ingestion.py b/integration/snowflake/data/ingestion.py index c48895a..a81323a 100644 --- a/integration/snowflake/data/ingestion.py +++ b/integration/snowflake/data/ingestion.py @@ -1,56 +1,51 @@ """Data ingestion from cloud storage to Snowflake for the Jaffle Shop dataset. -This module loads Parquet data from S3 or GCS buckets into Snowflake using -native COPY INTO commands with external staging. Schema is automatically -inferred from Parquet files using INFER_SCHEMA. +This module loads Parquet data from GCS or S3 buckets into Snowflake. -Supports: -- GCS buckets (requires STORAGE_INTEGRATION, auto-created if permissions exist) -- Public S3 buckets (no credentials required) +For GCS: +- Downloads files via HTTPS to a local cache (~/.cache/getml/jaffle_shop/) +- Uploads to an internal Snowflake stage using session.file.put() +- No GCP credentials or storage integration required + +For S3: +- Uses external staging with COPY INTO commands +- Public S3 buckets work without credentials + +When settings are provided, the module auto-bootstraps required infrastructure +(warehouse and database) if they don't exist. Usage example: - from settings import SnowflakeSettings - from snowflake_session import create_session - from data import load_from_gcs, load_from_s3 - from bootstrap import setup_gcs_storage_integration - - with create_session(SnowflakeSettings.from_env()) as session: - # Load from GCS (requires storage integration) - results = load_from_gcs( - session, - storage_integration="GETML_GCS_INTEGRATION", - integration_factory=setup_gcs_storage_integration, - ) + from data import ( + SnowflakeSettings, + create_session, + load_from_gcs, + ) - # Or load from public S3 bucket - results = load_from_s3( - session, - bucket="s3://my-public-bucket/jaffle_shop", - ) + settings = SnowflakeSettings.from_env() + with create_session(settings) as session: + # Load from GCS - auto-bootstraps warehouse + database + results = load_from_gcs(session, settings=settings) """ from __future__ import annotations # ruff: noqa: G004, S608 import logging -from collections.abc import Callable -from typing import TYPE_CHECKING +from pathlib import Path +import httpx from snowflake.snowpark import Row, Session -from sql_loader import load_sql - -if TYPE_CHECKING: - from bootstrap import StorageIntegrationInfo +from ._bootstrap import ensure_infrastructure +from ._settings import SnowflakeSettings +from ._sql_loader import load_sql logger: logging.Logger = logging.getLogger(__name__) -IntegrationFactory = Callable[[Session, str, str], "StorageIntegrationInfo"] - JAFFLE_SHOP_TABLE_NAMES: list[str] = [ "raw_customers", "raw_orders", - "raw_order_items", + "raw_items", "raw_products", "raw_stores", "raw_supplies", @@ -95,49 +90,81 @@ def get_table_names(schema: str = "RAW") -> dict[str, str]: def load_from_gcs( session: Session, - storage_integration: str, + settings: SnowflakeSettings | None = None, bucket: str = DEFAULT_GCS_BUCKET, destination_schema: str = "RAW", - *, - integration_factory: IntegrationFactory | None = None, ) -> dict[str, int]: """Load Jaffle Shop data from GCS bucket into Snowflake. - Creates the destination schema if needed, sets up a Parquet file format - and external stage pointing to the GCS bucket, then loads all tables - using COPY INTO with schema inference. + Downloads parquet files via HTTPS to a local cache, uploads them to an + internal Snowflake stage, then loads into tables using COPY INTO. + + No GCP credentials or storage integration required - files are fetched + via public HTTPS URLs. - If the storage integration doesn't exist and an integration_factory is - provided, attempts to create it automatically (requires ACCOUNTADMIN role). - If no factory is provided and integration is missing, raises an error. + When settings are provided, auto-bootstraps the warehouse and database + if they don't exist. Args: session: Active Snowflake Snowpark session. - storage_integration: Name of the Snowflake storage integration for GCS. + settings: Optional settings for auto-bootstrapping warehouse and database. + When provided, ensures infrastructure exists before loading data. bucket: GCS bucket URL (gcs:// or gs:// prefix). destination_schema: Target schema name for loaded tables. - integration_factory: Optional callable to create storage integration. - Use bootstrap.setup_gcs_storage_integration for automatic creation. + + Returns: + Dictionary mapping table names to row counts. Raises: - DataIngestionError: If storage integration is missing and no factory - provided, or if data loading fails. + DataIngestionError: If data loading fails. + BootstrapError: If auto-bootstrap fails due to insufficient privileges. """ - _ensure_storage_integration( - session, storage_integration, bucket, integration_factory - ) + if settings is not None: + ensure_infrastructure(session, settings) + if settings.warehouse: + session.use_warehouse(settings.warehouse) + if settings.database: + session.use_database(settings.database) - return _load_data( - session=session, - bucket_url=bucket, - destination_schema=destination_schema, - storage_integration=storage_integration, - ) + try: + # Download parquet files to local cache + https_base_url: str = _gcs_to_https_url(bucket) + cache_dir: Path = _get_cache_dir() + local_files: dict[str, Path] = {} + + for table_name in JAFFLE_SHOP_TABLE_NAMES: + file_name: str = f"{table_name}.parquet" + url: str = f"{https_base_url}/{file_name}" + local_path: Path = cache_dir / file_name + _download_parquet_file(url, dest_path=local_path) + local_files[table_name] = local_path + + # Create schema, file format, and internal stage + _create_schema(session, destination_schema) + _create_parquet_file_format(session, destination_schema) + _create_internal_stage(session, destination_schema, DEFAULT_STAGE_NAME) + + # Upload files to internal stage + _upload_files_to_stage( + session, local_files, destination_schema, DEFAULT_STAGE_NAME + ) + + # Load tables from stage + results = _load_all_tables(session, destination_schema) + + except Exception as e: + raise DataIngestionError( + f"Failed to load Jaffle Shop data into Snowflake. Error: {e}" + ) from e + + _log_completion_summary(results, destination_schema) + return results def load_from_s3( session: Session, bucket: str, + settings: SnowflakeSettings | None = None, destination_schema: str = "RAW", ) -> dict[str, int]: """Load Jaffle Shop data from public S3 bucket into Snowflake. @@ -146,125 +173,173 @@ def load_from_s3( and external stage pointing to the S3 bucket, then loads all tables using COPY INTO with schema inference. + When settings are provided, auto-bootstraps the warehouse and database + if they don't exist. + Args: session: Active Snowflake Snowpark session. bucket: S3 bucket URL (s3:// prefix). Must be publicly accessible. + settings: Optional settings for auto-bootstrapping warehouse and database. + When provided, ensures infrastructure exists before loading data. destination_schema: Target schema name for loaded tables. + Returns: + Dictionary mapping table names to row counts. + Raises: DataIngestionError: If data loading fails. + BootstrapError: If auto-bootstrap fails due to insufficient privileges. """ if not bucket.lower().startswith("s3://"): raise DataIngestionError( f"Invalid S3 bucket URL: {bucket}. Must start with s3://" ) - return _load_data( - session=session, - bucket_url=bucket, - destination_schema=destination_schema, - storage_integration=None, - ) + if settings is not None: + ensure_infrastructure(session, settings) + if settings.warehouse: + session.use_warehouse(settings.warehouse) + if settings.database: + session.use_database(settings.database) + + try: + _create_schema(session, destination_schema) + _create_parquet_file_format(session, destination_schema) + _create_external_stage_s3( + session, + bucket_url=bucket, + schema_name=destination_schema, + ) + results: dict[str, int] = _load_all_tables(session, destination_schema) + except Exception as e: + raise DataIngestionError( + f"Failed to load Jaffle Shop data into Snowflake. Error: {e}" + ) from e + + _log_completion_summary(results, destination_schema) + return results # ============================================================================= -# Storage Integration Handling +# GCS Download Helpers # ============================================================================= -def _ensure_storage_integration( - session: Session, - integration_name: str, - bucket_url: str, - integration_factory: IntegrationFactory | None, -) -> None: - """Ensure storage integration exists, creating it if factory is provided. +def _gcs_to_https_url(gcs_url: str) -> str: + """Convert GCS URL to HTTPS URL for public access. - Checks if the integration exists. If not and a factory is provided, - attempts to create it. If no factory is provided and integration is - missing, raises an error with instructions. - """ - if _storage_integration_exists(session, integration_name): - logger.info(f"✓ Storage integration '{integration_name}' verified") - return + Args: + gcs_url: GCS bucket URL with gcs:// or gs:// prefix. - if integration_factory is None: + Returns: + HTTPS URL for the same resource. + + Examples: + gcs://static.getml.com/datasets/jaffle_shop + -> https://storage.googleapis.com/static.getml.com/datasets/jaffle_shop + """ + if gcs_url.lower().startswith("gcs://"): + path = gcs_url[6:] + elif gcs_url.lower().startswith("gs://"): + path = gcs_url[5:] + else: raise DataIngestionError( - f"Storage integration '{integration_name}' does not exist. " - f"Either create it manually, or pass integration_factory=" - f"bootstrap.setup_gcs_storage_integration to auto-create it " - f"(requires ACCOUNTADMIN role)." + f"Invalid GCS URL: {gcs_url}. Must start with gcs:// or gs://" ) - logger.info(f"Storage integration '{integration_name}' not found. Creating...") + path: str = path.rstrip("/") - try: - _ = integration_factory( - session, - integration_name, - bucket_url, - ) - logger.info(f"✓ Storage integration '{integration_name}' created") - except Exception as e: - raise DataIngestionError( - f"Storage integration '{integration_name}' could not be created " - f"(requires ACCOUNTADMIN role). Error: {e}" - ) from e + return f"https://storage.googleapis.com/{path}" -def _storage_integration_exists(session: Session, integration_name: str) -> bool: - """Check if a storage integration exists.""" - try: - sql: str = load_sql( - path="ingestion/describe_storage_integration.sql", - integration_name=integration_name, - ) - _ = session.sql(sql).collect() - except Exception: # noqa: BLE001 - return False - else: - return True +def _get_cache_dir() -> Path: + """Get persistent cache directory for downloaded parquet files. + Creates the directory if it doesn't exist. -# ============================================================================= -# Core Loading Logic -# ============================================================================= + Returns: + Path to cache directory (~/.cache/getml/jaffle_shop/). + """ + cache_dir = Path.home() / ".cache" / "getml" / "jaffle_shop" + cache_dir.mkdir(parents=True, exist_ok=True) + return cache_dir -def _load_data( - session: Session, - bucket_url: str, - destination_schema: str, - storage_integration: str | None, -) -> dict[str, int]: - """Core data loading logic shared by GCS and S3 loaders.""" +def _download_parquet_file(url: str, dest_path: Path) -> None: + """Download a parquet file from URL to local path. + + Skips download if file already exists in cache. + + Args: + url: HTTPS URL to download from. + dest_path: Local path to save the file. + + Raises: + DataIngestionError: If download fails. + """ + if dest_path.exists(): + logger.info(f"Using cached file: {dest_path.name}") + return + + logger.info(f"Downloading {dest_path.name}...") + try: - _ = session.sql("BEGIN").collect() - _create_schema(session, destination_schema) - _create_parquet_file_format(session, destination_schema) - _create_external_stage( - session, - bucket_url=bucket_url, - schema_name=destination_schema, - storage_integration=storage_integration, + with httpx.stream("GET", url, follow_redirects=True, timeout=60.0) as response: + _ = response.raise_for_status() + with dest_path.open("wb") as f: + for chunk in response.iter_bytes(chunk_size=8192): + _ = f.write(chunk) + except httpx.HTTPError as e: + # Clean up partial download + if dest_path.exists(): + dest_path.unlink() + raise DataIngestionError(f"Failed to download {url}: {e}") from e + + logger.info(f" ✓ Downloaded {dest_path.name}") + + +def _upload_files_to_stage( + session: Session, + local_files: dict[str, Path], + schema_name: str, + stage_name: str, +) -> None: + """Upload local parquet files to internal Snowflake stage. + + Args: + session: Active Snowflake Snowpark session. + local_files: Dictionary mapping table names to local file paths. + schema_name: Schema where the stage is located. + stage_name: Name of the internal stage. + """ + logger.info(f"Uploading files to stage @{schema_name}.{stage_name}...") + + for local_path in local_files.values(): + logger.info(f" Uploading {local_path.name}...") + # PUT file to stage - Snowpark handles the upload + put_result = session.file.put( + local_file_name=str(local_path), + stage_location=f"@{schema_name}.{stage_name}/", + auto_compress=False, + overwrite=True, ) - results = _load_all_tables(session, destination_schema) - _ = session.sql("COMMIT").collect() - except Exception as e: - _rollback_transaction(session) - raise DataIngestionError( - f"Failed to load Jaffle Shop data into Snowflake. Error: {e}" - ) from e + # Log upload result + if put_result: + for row in put_result: + status: str = row.status if hasattr(row, "status") else "UPLOADED" + logger.info(f" ✓ {local_path.name}: {status}") - _log_completion_summary(results, destination_schema) - return results + +# ============================================================================= +# Schema and Stage Creation +# ============================================================================= def _create_schema(session: Session, schema_name: str) -> None: """Create schema if it doesn't exist.""" - logger.info(f"Creating {schema_name} schema if not exists...") - sql: str = load_sql(path="ingestion/create_schema.sql", schema_name=schema_name) - _ = session.sql(query=sql).collect() + logger.info(f"Creating {schema_name} schema...") + sql: str = load_sql(path="common/create_schema.sql", schema_name=schema_name) + _ = session.sql(sql).collect() logger.info(f"✓ {schema_name} schema ready") @@ -275,42 +350,51 @@ def _create_parquet_file_format(session: Session, schema_name: str) -> None: path="ingestion/create_parquet_file_format.sql", schema_name=schema_name, ) - _ = session.sql(query=sql).collect() + _ = session.sql(sql).collect() logger.info("✓ Parquet file format ready") -def _create_external_stage( +def _create_internal_stage( + session: Session, + schema_name: str, + stage_name: str, +) -> None: + """Create internal stage for uploaded files.""" + logger.info(f"Creating internal stage {stage_name}...") + sql: str = load_sql( + path="ingestion/create_internal_stage.sql", + stage_name=stage_name, + schema_name=schema_name, + ) + _ = session.sql(sql).collect() + logger.info(f"✓ Internal stage {stage_name} created") + + +def _create_external_stage_s3( session: Session, bucket_url: str, schema_name: str, - storage_integration: str | None, ) -> None: - """Create external stage pointing to cloud storage bucket.""" + """Create external stage pointing to public S3 bucket.""" logger.info(f"Creating external stage {DEFAULT_STAGE_NAME}...") + sql: str = load_sql( + path="ingestion/create_stage_s3_public.sql", + stage_name=DEFAULT_STAGE_NAME, + bucket_url=bucket_url, + schema_name=schema_name, + ) + _ = session.sql(sql).collect() + logger.info(f"✓ External stage {DEFAULT_STAGE_NAME} created") - if storage_integration: - sql: str = load_sql( - path="ingestion/create_stage_gcs.sql", - stage_name=DEFAULT_STAGE_NAME, - bucket_url=bucket_url, - storage_integration=storage_integration, - schema_name=schema_name, - ) - else: - sql = load_sql( - path="ingestion/create_stage_s3_public.sql", - stage_name=DEFAULT_STAGE_NAME, - bucket_url=bucket_url, - schema_name=schema_name, - ) - _ = session.sql(query=sql).collect() - logger.info(f"✓ External stage {DEFAULT_STAGE_NAME} created") +# ============================================================================= +# Table Loading +# ============================================================================= def _load_all_tables(session: Session, schema_name: str) -> dict[str, int]: - """Load all Jaffle Shop tables from cloud storage.""" - logger.info("Loading tables from cloud storage...") + """Load all Jaffle Shop tables from stage.""" + logger.info("Loading tables from stage...") results: dict[str, int] = {} for table_name in JAFFLE_SHOP_TABLE_NAMES: @@ -325,39 +409,35 @@ def _load_single_table( table_name: str, schema_name: str, ) -> int: - """Load a single table from cloud storage using schema inference. + """Load a single table from stage. - Creates the table structure by inferring schema from the Parquet file, - then copies data using column name matching. + Uses a two-step process: + 1. Creates empty table with schema inferred from Parquet metadata (INFER_SCHEMA) + 2. Copies data from stage using MATCH_BY_COLUMN_NAME for proper column mapping """ logger.info(f"Loading {table_name}...") - _ = session.sql( - query=load_sql( - path="ingestion/infer_and_create_table.sql", - table_name=table_name, - stage_name=DEFAULT_STAGE_NAME, - schema_name=schema_name, - ) - ).collect() - - copy_result: list[Row] = session.sql( - query=load_sql( - path="ingestion/copy_parquet.sql", - table_name=table_name, - stage_name=DEFAULT_STAGE_NAME, - schema_name=schema_name, - ) - ).collect() + create_sql: str = load_sql( + path="ingestion/create_table_from_parquet.sql", + table_name=table_name, + stage_name=DEFAULT_STAGE_NAME, + schema_name=schema_name, + ) + _ = session.sql(create_sql).collect() - count_result: list[Row] = session.sql( - query=f"SELECT COUNT(*) as count FROM {schema_name}.{table_name}" - ).collect() + copy_sql: str = load_sql( + path="ingestion/copy_into_table.sql", + table_name=table_name, + stage_name=DEFAULT_STAGE_NAME, + schema_name=schema_name, + ) + _ = session.sql(copy_sql).collect() - row_count = int(count_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] - rows_copied = int(copy_result[0]["rows_loaded"]) if copy_result else 0 # pyright: ignore[reportArgumentType] + count_sql = f"SELECT COUNT(*) as count FROM {schema_name}.{table_name}" + count_result: list[Row] = session.sql(count_sql).collect() - logger.info(f" ✓ {rows_copied:,} rows copied, {row_count:,} total rows in table") + row_count = int(count_result[0]["COUNT"]) # pyright: ignore[reportArgumentType] + logger.info(f" ✓ {row_count:,} rows loaded") if row_count == 0: raise DataIngestionError(f"Table {table_name} has 0 rows after loading") @@ -377,12 +457,3 @@ def _log_completion_summary(results: dict[str, int], schema_name: str) -> None: Successfully loaded {len(results)} tables into {schema_name} schema: {table_lines} """) - - -def _rollback_transaction(session: Session) -> None: - """Attempt to rollback the current transaction.""" - try: - _ = session.sql(query="ROLLBACK").collect() - logger.warning("Transaction rolled back due to error") - except Exception: # noqa: BLE001 - logger.debug("Rollback failed - transaction may not have been active") diff --git a/integration/snowflake/data/preparation.py b/integration/snowflake/data/preparation.py index 23a57bc..ad02cd2 100644 --- a/integration/snowflake/data/preparation.py +++ b/integration/snowflake/data/preparation.py @@ -6,21 +6,27 @@ reference_date is the Monday (week start) derived from DATE_TRUNC('week', ordered_at). +When settings are provided, the module auto-bootstraps required infrastructure +(warehouse and database) if they don't exist. + SQL queries are externalized in the sql/ directory for better maintainability. Usage example: - from settings import SnowflakeSettings - from snowflake_session import create_session - from data import create_weekly_sales_by_store_with_target + from data import ( + SnowflakeSettings, + create_session, + create_weekly_sales_by_store_with_target, + ) - with create_session(SnowflakeSettings.from_env()) as session: + settings = SnowflakeSettings.from_env() + with create_session(settings) as session: + # Auto-bootstraps warehouse + database when settings are provided population_table = create_weekly_sales_by_store_with_target( session, + settings=settings, source_schema="RAW", target_schema="PREPARED", - table_name="WEEKLY_SALES_BY_STORE_WITH_TARGET", ) - # population_table = "PREPARED.WEEKLY_SALES_BY_STORE_WITH_TARGET" arrow_table = session.table(population_table).to_arrow() """ @@ -31,7 +37,9 @@ from snowflake.snowpark import Row, Session -from sql_loader import load_sql +from ._bootstrap import ensure_infrastructure +from ._settings import SnowflakeSettings +from ._sql_loader import load_sql logger: logging.Logger = logging.getLogger(__name__) @@ -50,6 +58,7 @@ class DataPreparationError(Exception): def create_weekly_sales_by_store_with_target( session: Session, + settings: SnowflakeSettings | None = None, source_schema: str = "RAW", target_schema: str = "PREPARED", table_name: str = DEFAULT_POPULATION_TABLE_NAME, @@ -61,8 +70,13 @@ def create_weekly_sales_by_store_with_target( - Population view with target column (configurable name via table_name) - Target: Sum of order_total for the 7-day window starting at reference_date + When settings are provided, auto-bootstraps the warehouse and database + if they don't exist. + Args: session: Active Snowflake Snowpark session. + settings: Optional settings for auto-bootstrapping warehouse and database. + When provided, ensures infrastructure exists before preparing data. source_schema: Schema containing raw_stores and raw_orders tables. target_schema: Schema where prepared tables/views will be created. table_name: Name of the population view to create. @@ -72,7 +86,15 @@ def create_weekly_sales_by_store_with_target( Raises: DataPreparationError: If source tables are missing or preparation fails. + BootstrapError: If auto-bootstrap fails due to insufficient privileges. """ + if settings is not None: + ensure_infrastructure(session, settings) + if settings.warehouse: + session.use_warehouse(settings.warehouse) + if settings.database: + session.use_database(settings.database) + _validate_source_tables(session, source_schema) _ensure_target_schema(session, target_schema) @@ -144,7 +166,7 @@ def _validate_source_tables(session: Session, source_schema: str) -> None: def _ensure_target_schema(session: Session, target_schema: str) -> None: """Create target schema if it doesn't exist.""" logger.info(f"Creating {target_schema} schema if not exists...") - sql: str = load_sql("preparation/create_schema.sql", schema_name=target_schema) + sql: str = load_sql("common/create_schema.sql", schema_name=target_schema) _ = session.sql(query=sql).collect() logger.info(f"✓ {target_schema} schema ready") @@ -197,39 +219,28 @@ def _create_weekly_stores_table( ) ).collect() - num_snapshots: int = cast( - "int", - session.sql( - query=load_sql( - path="preparation/count_snapshots.sql", - target_schema=target_schema, - ) - ).collect()[0][0], - ) - num_stores: int = cast( - "int", - session.sql( - query=load_sql( - path="preparation/count_stores.sql", - target_schema=target_schema, - ) - ).collect()[0][0], - ) - logger.info( - f" Created {num_snapshots:,} weekly snapshots across {num_stores} stores" - ) - - per_store: list[Row] = session.sql( + # Get summary in single query: totals + per-store breakdown + summary: list[Row] = session.sql( query=load_sql( - path="preparation/snapshots_per_store.sql", + path="preparation/weekly_stores_summary.sql", target_schema=target_schema, ) ).collect() + # First row contains totals (same for all rows due to CROSS JOIN) + num_snapshots: int = cast("int", summary[0][0]) + num_stores: int = cast("int", summary[0][1]) + logger.info( + f" Created {num_snapshots:,} weekly snapshots across {num_stores} stores" + ) + + # Extract per-store data (columns 2-5: store_name, num_snapshots, first, last) + per_store: list[Row] = summary + logger.info(f"\n {'Store':<30} {'Snapshots':<12} {'First':<20} {'Last':<20}") logger.info(" " + "-" * 80) for row in per_store: - logger.info(f" {row[0]:<30} {row[1]:<12,} {row[2]!s:<20} {row[3]!s:<20}") + logger.info(f" {row[2]:<30} {row[3]:<12,} {row[4]!s:<20} {row[5]!s:<20}") return per_store @@ -271,13 +282,15 @@ def _display_sample_data( logger.debug("-" * 120) for store_info in per_store[:3]: - store_name: str = cast("str", store_info[0]) + store_name: str = cast("str", store_info[2]) logger.debug(f"\n{store_name}:") sample_query: str = load_sql( - path="preparation/sample_data_by_store.sql", + path="preparation/snapshots_by_store.sql", target_schema=target_schema, store_name=store_name, table_name=table_name, + order_direction="", + limit="5", ) samples: list[Row] = session.sql(sample_query).collect() @@ -403,13 +416,15 @@ def _display_recent_snapshots( logger.debug("-" * 120) for store_info in per_store[:3]: - store_name: str = cast("str", store_info[0]) + store_name: str = cast("str", store_info[2]) logger.debug(f"\n{store_name}:") recent_query: str = load_sql( - path="preparation/recent_snapshots_by_store.sql", + path="preparation/snapshots_by_store.sql", target_schema=target_schema, store_name=store_name, table_name=table_name, + order_direction="DESC", + limit="3", ) recent: list[Row] = session.sql(recent_query).collect() @@ -428,9 +443,11 @@ def _display_recent_snapshots( logger.info("This module requires a Snowflake session to be passed.") logger.info("Example usage:") logger.info(""" - from settings import SnowflakeSettings - from snowflake_session import create_session - from data import create_weekly_sales_by_store_with_target + from data import ( + SnowflakeSettings, + create_session, + create_weekly_sales_by_store_with_target, + ) settings = SnowflakeSettings.from_env() diff --git a/integration/snowflake/data/sql/bootstrap/create_database.sql b/integration/snowflake/data/sql/bootstrap/create_database.sql index 0972095..b58ebb1 100644 --- a/integration/snowflake/data/sql/bootstrap/create_database.sql +++ b/integration/snowflake/data/sql/bootstrap/create_database.sql @@ -1,2 +1 @@ --- Create Snowflake database if it doesn't exist CREATE DATABASE IF NOT EXISTS {database_name} diff --git a/integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql b/integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql deleted file mode 100644 index 02fd24b..0000000 --- a/integration/snowflake/data/sql/bootstrap/create_storage_integration_gcs.sql +++ /dev/null @@ -1,7 +0,0 @@ --- Create a GCS storage integration for accessing Google Cloud Storage buckets --- Requires ACCOUNTADMIN role or CREATE INTEGRATION privilege -CREATE OR REPLACE STORAGE INTEGRATION {integration_name} - TYPE = EXTERNAL_STAGE - STORAGE_PROVIDER = 'GCS' - ENABLED = TRUE - STORAGE_ALLOWED_LOCATIONS = ('{allowed_location}') diff --git a/integration/snowflake/data/sql/bootstrap/create_warehouse.sql b/integration/snowflake/data/sql/bootstrap/create_warehouse.sql index 7d1252d..6e8ef05 100644 --- a/integration/snowflake/data/sql/bootstrap/create_warehouse.sql +++ b/integration/snowflake/data/sql/bootstrap/create_warehouse.sql @@ -1,4 +1,3 @@ --- Create Snowflake warehouse with specified configuration CREATE WAREHOUSE IF NOT EXISTS {warehouse_name} WITH WAREHOUSE_SIZE = '{warehouse_size}' diff --git a/integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql b/integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql deleted file mode 100644 index c7d5a36..0000000 --- a/integration/snowflake/data/sql/bootstrap/describe_storage_integration.sql +++ /dev/null @@ -1,3 +0,0 @@ --- Describe a storage integration to retrieve configuration details --- including the STORAGE_GCP_SERVICE_ACCOUNT for IAM setup -DESC STORAGE INTEGRATION {integration_name} diff --git a/integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql b/integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql deleted file mode 100644 index 4eb07ae..0000000 --- a/integration/snowflake/data/sql/bootstrap/validate_storage_integration.sql +++ /dev/null @@ -1,9 +0,0 @@ --- Validate a storage integration can access GCS with list permissions --- Uses Snowflake's native validation function with full signature --- Returns JSON with status and action results -SELECT SYSTEM$VALIDATE_STORAGE_INTEGRATION( - '{integration_name}', - '{storage_path}', - '{test_file}', - 'list' -) AS VALIDATION_RESULT diff --git a/integration/snowflake/data/sql/ingestion/create_schema.sql b/integration/snowflake/data/sql/common/create_schema.sql similarity index 53% rename from integration/snowflake/data/sql/ingestion/create_schema.sql rename to integration/snowflake/data/sql/common/create_schema.sql index 7b869ca..57faa76 100644 --- a/integration/snowflake/data/sql/ingestion/create_schema.sql +++ b/integration/snowflake/data/sql/common/create_schema.sql @@ -1,2 +1 @@ --- Create schema for data ingestion CREATE SCHEMA IF NOT EXISTS {schema_name} diff --git a/integration/snowflake/data/sql/ingestion/copy_into_table.sql b/integration/snowflake/data/sql/ingestion/copy_into_table.sql new file mode 100644 index 0000000..2a7ae6f --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/copy_into_table.sql @@ -0,0 +1,6 @@ +-- Copy data from staged Parquet file into table +-- Uses MATCH_BY_COLUMN_NAME to map Parquet columns to table columns +COPY INTO {schema_name}.{table_name} + FROM @{schema_name}.{stage_name}/{table_name}.parquet + FILE_FORMAT = (FORMAT_NAME = '{schema_name}.PARQUET_FORMAT') + MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE; diff --git a/integration/snowflake/data/sql/ingestion/copy_parquet.sql b/integration/snowflake/data/sql/ingestion/copy_parquet.sql deleted file mode 100644 index be10745..0000000 --- a/integration/snowflake/data/sql/ingestion/copy_parquet.sql +++ /dev/null @@ -1,6 +0,0 @@ --- Copy Parquet data into table using column name matching --- MATCH_BY_COLUMN_NAME handles column ordering differences between file and table -COPY INTO {schema_name}.{table_name} - FROM @{schema_name}.{stage_name}/{table_name}.parquet - FILE_FORMAT = {schema_name}.PARQUET_FORMAT - MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE diff --git a/integration/snowflake/data/sql/ingestion/create_internal_stage.sql b/integration/snowflake/data/sql/ingestion/create_internal_stage.sql new file mode 100644 index 0000000..7b34028 --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_internal_stage.sql @@ -0,0 +1,4 @@ +-- Create internal stage for uploading local parquet files +-- Used when loading data via session.file.put() instead of external storage integration +CREATE OR REPLACE STAGE {schema_name}.{stage_name} + FILE_FORMAT = {schema_name}.PARQUET_FORMAT; diff --git a/integration/snowflake/data/sql/ingestion/create_stage_gcs.sql b/integration/snowflake/data/sql/ingestion/create_stage_gcs.sql deleted file mode 100644 index 3bc7f96..0000000 --- a/integration/snowflake/data/sql/ingestion/create_stage_gcs.sql +++ /dev/null @@ -1,6 +0,0 @@ --- Create external stage pointing to a GCS bucket for Parquet data ingestion --- Requires a storage integration for authentication -CREATE OR REPLACE STAGE {schema_name}.{stage_name} - URL = '{bucket_url}' - STORAGE_INTEGRATION = {storage_integration} - FILE_FORMAT = {schema_name}.PARQUET_FORMAT diff --git a/integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql b/integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql deleted file mode 100644 index 02fd24b..0000000 --- a/integration/snowflake/data/sql/ingestion/create_storage_integration_gcs.sql +++ /dev/null @@ -1,7 +0,0 @@ --- Create a GCS storage integration for accessing Google Cloud Storage buckets --- Requires ACCOUNTADMIN role or CREATE INTEGRATION privilege -CREATE OR REPLACE STORAGE INTEGRATION {integration_name} - TYPE = EXTERNAL_STAGE - STORAGE_PROVIDER = 'GCS' - ENABLED = TRUE - STORAGE_ALLOWED_LOCATIONS = ('{allowed_location}') diff --git a/integration/snowflake/data/sql/ingestion/create_table_from_parquet.sql b/integration/snowflake/data/sql/ingestion/create_table_from_parquet.sql new file mode 100644 index 0000000..bd10f2e --- /dev/null +++ b/integration/snowflake/data/sql/ingestion/create_table_from_parquet.sql @@ -0,0 +1,16 @@ +-- Create table with schema inferred from Parquet file +-- Uses INFER_SCHEMA to detect column names and types from the Parquet metadata +-- Column names are uppercased to match Snowflake conventions (unquoted identifiers) +CREATE OR REPLACE TABLE {schema_name}.{table_name} + USING TEMPLATE ( + SELECT ARRAY_AGG(OBJECT_CONSTRUCT( + 'COLUMN_NAME', UPPER(COLUMN_NAME), + 'TYPE', TYPE, + 'NULLABLE', NULLABLE + )) + FROM TABLE( + INFER_SCHEMA( + LOCATION=>'@{schema_name}.{stage_name}/{table_name}.parquet', + FILE_FORMAT=>'{schema_name}.PARQUET_FORMAT' + ) + )); diff --git a/integration/snowflake/data/sql/ingestion/describe_storage_integration.sql b/integration/snowflake/data/sql/ingestion/describe_storage_integration.sql deleted file mode 100644 index c7d5a36..0000000 --- a/integration/snowflake/data/sql/ingestion/describe_storage_integration.sql +++ /dev/null @@ -1,3 +0,0 @@ --- Describe a storage integration to retrieve configuration details --- including the STORAGE_GCP_SERVICE_ACCOUNT for IAM setup -DESC STORAGE INTEGRATION {integration_name} diff --git a/integration/snowflake/data/sql/ingestion/infer_and_create_table.sql b/integration/snowflake/data/sql/ingestion/infer_and_create_table.sql deleted file mode 100644 index aa33ed7..0000000 --- a/integration/snowflake/data/sql/ingestion/infer_and_create_table.sql +++ /dev/null @@ -1,12 +0,0 @@ --- Create table with schema inferred from Parquet file --- Uses INFER_SCHEMA to automatically detect column names and types -CREATE OR REPLACE TABLE {schema_name}.{table_name} - USING TEMPLATE ( - SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*)) - FROM TABLE( - INFER_SCHEMA( - LOCATION => '@{schema_name}.{stage_name}/{table_name}.parquet', - FILE_FORMAT => '{schema_name}.PARQUET_FORMAT' - ) - ) - ) diff --git a/integration/snowflake/data/sql/ingestion/validate_storage_integration.sql b/integration/snowflake/data/sql/ingestion/validate_storage_integration.sql deleted file mode 100644 index 2ab4ee1..0000000 --- a/integration/snowflake/data/sql/ingestion/validate_storage_integration.sql +++ /dev/null @@ -1,3 +0,0 @@ --- Validate a storage integration configuration --- Returns success message or error details -SELECT SYSTEM$VALIDATE_STORAGE_INTEGRATION('{integration_name}') AS validation_result diff --git a/integration/snowflake/data/sql/preparation/analyze_stores.sql b/integration/snowflake/data/sql/preparation/analyze_stores.sql index 6dabcd4..03ab648 100644 --- a/integration/snowflake/data/sql/preparation/analyze_stores.sql +++ b/integration/snowflake/data/sql/preparation/analyze_stores.sql @@ -1,13 +1,13 @@ -- Analyze stores and their activity -SELECT +SELECT s.id, s.name, - s.opened_at, - MIN(o.ordered_at) as first_order, - MAX(o.ordered_at) as last_order, + TRY_TO_TIMESTAMP(s.opened_at) as opened_at, + MIN(TRY_TO_TIMESTAMP(o.ordered_at)) as first_order, + MAX(TRY_TO_TIMESTAMP(o.ordered_at)) as last_order, COUNT(o.id) as total_orders, SUM(COALESCE(o.order_total, 0)) / 100.0 as total_sales FROM {source_schema}.raw_stores s LEFT JOIN {source_schema}.raw_orders o ON o.store_id = s.id GROUP BY s.id, s.name, s.opened_at -ORDER BY s.opened_at +ORDER BY TRY_TO_TIMESTAMP(s.opened_at) diff --git a/integration/snowflake/data/sql/preparation/calculate_target.sql b/integration/snowflake/data/sql/preparation/calculate_target.sql index 008fd5c..b7ac3b5 100644 --- a/integration/snowflake/data/sql/preparation/calculate_target.sql +++ b/integration/snowflake/data/sql/preparation/calculate_target.sql @@ -1,13 +1,25 @@ -- Create view with target: next week's sales per store -- --- This view joins weekly_stores with raw_orders to calculate the target variable. +-- This view joins weekly_stores with pre-aggregated order totals. -- Target is the sum of order_total for the 7-day window starting at reference_date. -- -- Window: [reference_date, reference_date + 7 days) -- - reference_date is Monday 00:00:00 (week start) -- - Target covers Monday through Sunday of that week +-- +-- Optimized: Single aggregation pass instead of correlated subqueries CREATE OR REPLACE VIEW {target_schema}.{table_name} AS -SELECT +WITH weekly_order_totals AS ( + SELECT + store_id, + DATE_TRUNC('week', TRY_TO_TIMESTAMP(ordered_at)) as week_start, + SUM(order_total) / 100.0 as week_sales, + COUNT(*) as week_orders + FROM {source_schema}.raw_orders + WHERE ordered_at IS NOT NULL + GROUP BY store_id, DATE_TRUNC('week', TRY_TO_TIMESTAMP(ordered_at)) +) +SELECT ws.snapshot_id, ws.store_id, ws.store_name, @@ -19,22 +31,9 @@ SELECT ws.is_full_week_after_opening, ws.has_order_activity, ws.has_min_history, - COALESCE( - ( - SELECT SUM(o.order_total) / 100.0 - FROM {source_schema}.raw_orders o - WHERE o.store_id = ws.store_id - AND o.ordered_at >= ws.reference_date - AND o.ordered_at < ws.reference_date + INTERVAL '7 days' - ), 0 - ) as next_week_sales, - COALESCE( - ( - SELECT COUNT(*) - FROM {source_schema}.raw_orders o - WHERE o.store_id = ws.store_id - AND o.ordered_at >= ws.reference_date - AND o.ordered_at < ws.reference_date + INTERVAL '7 days' - ), 0 - ) as next_week_orders -FROM {target_schema}.weekly_stores ws; + COALESCE(wot.week_sales, 0) as next_week_sales, + COALESCE(wot.week_orders, 0) as next_week_orders +FROM {target_schema}.weekly_stores ws +LEFT JOIN weekly_order_totals wot + ON wot.store_id = ws.store_id + AND wot.week_start = ws.reference_date diff --git a/integration/snowflake/data/sql/preparation/count_snapshots.sql b/integration/snowflake/data/sql/preparation/count_snapshots.sql deleted file mode 100644 index 03b67d8..0000000 --- a/integration/snowflake/data/sql/preparation/count_snapshots.sql +++ /dev/null @@ -1,2 +0,0 @@ --- Count total snapshots -SELECT COUNT(*) FROM {target_schema}.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/count_stores.sql b/integration/snowflake/data/sql/preparation/count_stores.sql deleted file mode 100644 index 692f46d..0000000 --- a/integration/snowflake/data/sql/preparation/count_stores.sql +++ /dev/null @@ -1,2 +0,0 @@ --- Count distinct stores -SELECT COUNT(DISTINCT store_id) FROM {target_schema}.weekly_stores diff --git a/integration/snowflake/data/sql/preparation/create_schema.sql b/integration/snowflake/data/sql/preparation/create_schema.sql deleted file mode 100644 index ef0ad6d..0000000 --- a/integration/snowflake/data/sql/preparation/create_schema.sql +++ /dev/null @@ -1,2 +0,0 @@ --- Create schema for processed data ready for ML -CREATE SCHEMA IF NOT EXISTS {schema_name} diff --git a/integration/snowflake/data/sql/preparation/create_weekly_stores.sql b/integration/snowflake/data/sql/preparation/create_weekly_stores.sql index b3fafa3..af3f2ef 100644 --- a/integration/snowflake/data/sql/preparation/create_weekly_stores.sql +++ b/integration/snowflake/data/sql/preparation/create_weekly_stores.sql @@ -13,23 +13,23 @@ -- - has_min_history: At least 7 days since store opened CREATE TABLE {target_schema}.weekly_stores AS WITH store_activity AS ( - SELECT + SELECT s.id as store_id, s.name as store_name, - s.opened_at, - DATE_TRUNC('week', s.opened_at) + INTERVAL '7 days' as first_full_week, - MIN(o.ordered_at) as first_order_date, - MAX(o.ordered_at) as last_order_date, - DATE_TRUNC('week', MIN(o.ordered_at)) as first_order_week, - DATE_TRUNC('week', MAX(o.ordered_at)) as last_order_week + TRY_TO_TIMESTAMP(s.opened_at) as opened_at, + DATE_TRUNC('week', TRY_TO_TIMESTAMP(s.opened_at)) + INTERVAL '7 days' as first_full_week, + MIN(TRY_TO_TIMESTAMP(o.ordered_at)) as first_order_date, + MAX(TRY_TO_TIMESTAMP(o.ordered_at)) as last_order_date, + DATE_TRUNC('week', MIN(TRY_TO_TIMESTAMP(o.ordered_at))) as first_order_week, + DATE_TRUNC('week', MAX(TRY_TO_TIMESTAMP(o.ordered_at))) as last_order_week FROM {source_schema}.raw_stores s LEFT JOIN {source_schema}.raw_orders o ON o.store_id = s.id - GROUP BY s.id, s.name, s.opened_at + GROUP BY s.id, s.name, TRY_TO_TIMESTAMP(s.opened_at) ), all_weeks AS ( - SELECT DISTINCT - DATE_TRUNC('week', ordered_at) as reference_date + SELECT DISTINCT + DATE_TRUNC('week', TRY_TO_TIMESTAMP(ordered_at)) as reference_date FROM {source_schema}.raw_orders WHERE ordered_at IS NOT NULL ), diff --git a/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql b/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql deleted file mode 100644 index 577c2e3..0000000 --- a/integration/snowflake/data/sql/preparation/recent_snapshots_by_store.sql +++ /dev/null @@ -1,10 +0,0 @@ --- Most recent snapshots for a specific store -SELECT - snapshot_id, - reference_date, - next_week_sales, - next_week_orders -FROM {target_schema}.{table_name} -WHERE store_name = '{store_name}' -ORDER BY reference_date DESC -LIMIT 3 diff --git a/integration/snowflake/data/sql/preparation/sample_data_by_store.sql b/integration/snowflake/data/sql/preparation/sample_data_by_store.sql deleted file mode 100644 index 5340322..0000000 --- a/integration/snowflake/data/sql/preparation/sample_data_by_store.sql +++ /dev/null @@ -1,10 +0,0 @@ --- Sample data - first 5 snapshots for a specific store -SELECT - snapshot_id, - reference_date, - next_week_sales, - next_week_orders -FROM {target_schema}.{table_name} -WHERE store_name = '{store_name}' -ORDER BY reference_date -LIMIT 5 diff --git a/integration/snowflake/data/sql/preparation/snapshots_by_store.sql b/integration/snowflake/data/sql/preparation/snapshots_by_store.sql new file mode 100644 index 0000000..03b24d7 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/snapshots_by_store.sql @@ -0,0 +1,11 @@ +-- Sample snapshots for a specific store +-- Use order_direction="" for oldest first, "DESC" for newest first +SELECT + snapshot_id, + reference_date, + next_week_sales, + next_week_orders +FROM {target_schema}.{table_name} +WHERE store_name = '{store_name}' +ORDER BY reference_date {order_direction} +LIMIT {limit} diff --git a/integration/snowflake/data/sql/preparation/snapshots_per_store.sql b/integration/snowflake/data/sql/preparation/snapshots_per_store.sql deleted file mode 100644 index 2572a79..0000000 --- a/integration/snowflake/data/sql/preparation/snapshots_per_store.sql +++ /dev/null @@ -1,9 +0,0 @@ --- Show snapshots per store -SELECT - store_name, - COUNT(*) as num_snapshots, - MIN(reference_date) as first_snapshot, - MAX(reference_date) as last_snapshot -FROM {target_schema}.weekly_stores -GROUP BY store_name -ORDER BY first_snapshot diff --git a/integration/snowflake/data/sql/preparation/weekly_stores_summary.sql b/integration/snowflake/data/sql/preparation/weekly_stores_summary.sql new file mode 100644 index 0000000..9cd38b5 --- /dev/null +++ b/integration/snowflake/data/sql/preparation/weekly_stores_summary.sql @@ -0,0 +1,27 @@ +-- Summary of weekly_stores table: counts and per-store breakdown +-- Returns total snapshots, store count, and per-store details in one query +WITH totals AS ( + SELECT + COUNT(*) as total_snapshots, + COUNT(DISTINCT store_id) as num_stores + FROM {target_schema}.weekly_stores +), +per_store AS ( + SELECT + store_name, + COUNT(*) as num_snapshots, + MIN(reference_date) as first_snapshot, + MAX(reference_date) as last_snapshot + FROM {target_schema}.weekly_stores + GROUP BY store_name +) +SELECT + t.total_snapshots, + t.num_stores, + p.store_name, + p.num_snapshots, + p.first_snapshot, + p.last_snapshot +FROM totals t +CROSS JOIN per_store p +ORDER BY p.first_snapshot diff --git a/integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md b/integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md deleted file mode 100644 index f323fb7..0000000 --- a/integration/snowflake/docs/GCS_STORAGE_INTEGRATION_SETUP.md +++ /dev/null @@ -1,187 +0,0 @@ -# GCS Storage Integration Setup for Snowflake - -This guide describes how to configure Snowflake to access Parquet files stored in Google Cloud Storage (GCS) for the Jaffle Shop dataset. - -## Overview - -Snowflake cannot access GCS buckets directly—even publicly accessible ones—without a **storage integration**. This integration creates a GCS service account that must be granted IAM permissions on the target bucket. - -**Default data location:** `gs://static.getml.com/datasets/jaffle_shop/` - -## Prerequisites - -- Snowflake account with `ACCOUNTADMIN` role access -- GCP project editor access (to grant IAM permissions on the bucket) -- Python environment with `uv` configured - -## Step 1: Create Storage Integration - -Use the provided helper function to create the storage integration: - -```python -from settings import SnowflakeSettings -from snowflake_session import create_session -from data import setup_gcs_integration - -settings = SnowflakeSettings.from_env() -with create_session(settings) as session: - info = setup_gcs_integration(session) - print(f"Service account: {info.service_account}") -``` - -This creates the integration and prints the service account that needs IAM permissions. - -For custom bucket locations: - -```python -from data import setup_gcs_integration - -info = setup_gcs_integration( - session, - integration_name="MY_CUSTOM_INTEGRATION", - allowed_location="gcs://my-bucket/path/", -) -``` - -You can also retrieve info about an existing integration: - -```python -from data import describe_storage_integration - -info = describe_storage_integration(session, "GETML_GCS_INTEGRATION") -print(f"Service account: {info.service_account}") -``` - -## Step 2: Grant IAM Permissions in GCP - -The Snowflake service account from Step 1 needs read access to the GCS bucket. - -### Using gcloud CLI (Recommended) - -```bash -# Set your project -gcloud config set project getml-infra - -# Store the service account from Step 1 -SERVICE_ACCOUNT="@gcpeuropewestX-X-XXXX.iam.gserviceaccount.com" - -# Grant Storage Object Viewer role on the bucket -gsutil iam ch "serviceAccount:${SERVICE_ACCOUNT}:objectViewer" gs://static.getml.com - -# Verify the binding was applied -gsutil iam get gs://static.getml.com -``` - -For granular permissions (alternative to objectViewer role): - -```bash -# Create a custom role with minimal permissions -gcloud iam roles create snowflake_gcs_reader \ - --project=getml-infra \ - --title="Snowflake GCS Reader" \ - --permissions=storage.buckets.get,storage.objects.get,storage.objects.list - -# Grant the custom role -gsutil iam ch "serviceAccount:${SERVICE_ACCOUNT}:projects/getml-infra/roles/snowflake_gcs_reader" \ - gs://static.getml.com -``` - -### Using Google Cloud Console - -1. Sign in to the [Google Cloud Console](https://console.cloud.google.com/) -2. Navigate to **Cloud Storage → Buckets** -3. Select the bucket `static.getml.com` -4. Go to **Permissions → Grant Access** -5. Add the Snowflake service account email as a principal -6. Assign role: **Storage Object Viewer** -7. Click **Save** - -## Step 3: Validate the Integration - -Validate that Snowflake can access the GCS bucket: - -```python -from data import validate_storage_integration - -result = validate_storage_integration(session, "GETML_GCS_INTEGRATION") -print(result) # Should show success -``` - -## Step 4: Configure Environment Variable - -For the integration tests to use this storage integration, set the environment variable: - -```bash -export GCS_STORAGE_INTEGRATION=GETML_GCS_INTEGRATION -``` - -Add this to your shell profile (`.bashrc`, `.zshrc`) or CI/CD secrets for persistence. - -## Running Integration Tests - -With the environment variable set, integration tests will automatically use the GCS storage integration: - -```bash -cd integration/snowflake -uv run pytest tests/integration/ -v -``` - -## Troubleshooting - -### Error: "Processing aborted due to error 300010" - -This typically means the Snowflake service account doesn't have IAM permissions on the GCS bucket. Verify: - -```bash -# Check current IAM bindings on the bucket -gsutil iam get gs://static.getml.com - -# Verify the service account is listed with objectViewer or equivalent role -``` - -### Error: "Stage does not exist or not authorized" - -The stage hasn't been created yet. The `load_jaffle_shop_data_parquet()` function creates it automatically during data ingestion. - -### Error: "Storage integration not found" - -Verify the integration exists: - -```python -from data import describe_storage_integration - -try: - info = describe_storage_integration(session, "GETML_GCS_INTEGRATION") - print(f"Integration exists: {info.enabled}") -except Exception as e: - print(f"Integration not found: {e}") -``` - -### Domain Restriction Policy (GCP Organizations) - -If your GCP organization was created after May 3, 2024, Google enforces domain restrictions by default. Update the organization policy: - -```bash -# Check current policy -gcloud org-policies describe iam.allowedPolicyMemberDomains \ - --project=getml-infra - -# If restricted, add Snowflake's domain to allowed list -# See: https://docs.snowflake.com/en/user-guide/data-load-gcs-allow -``` - -## Alternative: Using S3 Public Bucket - -If GCS setup isn't possible, you can use a public S3 bucket with Parquet files instead: - -```bash -export S3_BUCKET_URL=s3://your-public-bucket/jaffle_shop/ -``` - -S3 public buckets don't require a storage integration—Snowflake can access them directly. - -## References - -- [Snowflake: Configuring an integration for Google Cloud Storage](https://docs.snowflake.com/en/user-guide/data-load-gcs-config) -- [Snowflake: CREATE STORAGE INTEGRATION](https://docs.snowflake.com/en/sql-reference/sql/create-storage-integration) -- [GCP: IAM for Cloud Storage](https://cloud.google.com/storage/docs/access-control/iam) diff --git a/integration/snowflake/prepare_jaffle_shop_data_for_snowflake.py b/integration/snowflake/prepare_jaffle_shop_data_for_snowflake.py new file mode 100755 index 0000000..0f65abf --- /dev/null +++ b/integration/snowflake/prepare_jaffle_shop_data_for_snowflake.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +"""Prepare Jaffle Shop data in Snowflake for getML Feature Store integration. + +Loads raw data from GCS and creates the weekly sales forecasting population table. +Infrastructure (warehouse, database) is auto-created if missing. + +Prerequisites: + - Snowflake account with appropriate privileges + - SNOWFLAKE_* environment variables set for authentication and configuration + +Usage: + uv run python prepare_jaffle_shop_data_for_snowflake.py +""" + +import logging +import sys + +from data import ( + SnowflakeSettings, + create_session, + ingestion, + preparation, +) + +logger: logging.Logger = logging.getLogger(__name__) + + +def main() -> None: + """Load and prepare Jaffle Shop data for getML. + + Note: + Set basicConfig.level to logging.DEBUG for more verbose output. + """ + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + stream=sys.stdout, + ) + + settings = SnowflakeSettings.from_env() + + with create_session(settings) as session: + _ = ingestion.load_from_gcs( + session, + settings=settings, + bucket="gcs://static.getml.com/datasets/jaffle_shop", + destination_schema="RAW", + ) + + _ = preparation.create_weekly_sales_by_store_with_target( + session, + settings=settings, + source_schema="RAW", + target_schema="PREPARED", + table_name="WEEKLY_SALES_BY_STORE_WITH_TARGET", + ) + + +if __name__ == "__main__": + main() diff --git a/integration/snowflake/pyproject.toml b/integration/snowflake/pyproject.toml index 9c04923..0740754 100644 --- a/integration/snowflake/pyproject.toml +++ b/integration/snowflake/pyproject.toml @@ -29,6 +29,7 @@ requires-python = ">=3.12" dependencies = [ "fastparquet>=2024.11.0", + "httpx>=0.27.0", "ipykernel>=7.1.0", "jafgen>=0.4.14", "pandas>=2.3.3", diff --git a/integration/snowflake/settings.py b/integration/snowflake/settings.py deleted file mode 100644 index cdf0e21..0000000 --- a/integration/snowflake/settings.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Snowflake connection settings using pydantic-settings. - -Provides typed configuration classes that automatically load from environment -variables with the SNOWFLAKE_ prefix. - -Usage example: - from settings import SnowflakeAdminSettings, SnowflakeSettings - - # Auto-loads from SNOWFLAKE_* environment variables - admin_settings = SnowflakeAdminSettings.from_env() - full_settings = SnowflakeSettings.from_env() -""" - -from typing import ClassVar, Self - -from pydantic import Field, SecretStr -from pydantic_settings import BaseSettings, SettingsConfigDict - -DEFAULT_SNOWFLAKE_ROLE = "ACCOUNTADMIN" -DEFAULT_SNOWFLAKE_SCHEMA = "RAW" - - -class SnowflakeAdminSettings(BaseSettings): - """Settings for administrative Snowflake operations (bootstrap). - - These minimal settings allow connecting without specifying warehouse - or database, which is required for creating those resources. - """ - - model_config: ClassVar[SettingsConfigDict] = SettingsConfigDict( - env_prefix="SNOWFLAKE_", - case_sensitive=False, - ) - - account: str - user: str - password: SecretStr - role: str = DEFAULT_SNOWFLAKE_ROLE - - @classmethod - def from_env(cls) -> Self: - """Create settings instance from environment variables. - - Loads configuration from SNOWFLAKE_* environment variables. - """ - return cls.model_validate({}) - - -class SnowflakeSettings(SnowflakeAdminSettings): - """Full settings for Snowflake data operations. - - Extends admin settings with warehouse, database, and schema - required for executing queries and loading data. - """ - - warehouse: str - database: str - schema_name: str = Field( - default=DEFAULT_SNOWFLAKE_SCHEMA, - validation_alias="SNOWFLAKE_SCHEMA", - ) diff --git a/integration/snowflake/snowflake_session.py b/integration/snowflake/snowflake_session.py deleted file mode 100644 index 6a2599a..0000000 --- a/integration/snowflake/snowflake_session.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Snowflake session management for getML Feature Store integration. - -This module provides factory functions for creating Snowflake Snowpark sessions. -Sessions support the context manager protocol for automatic cleanup. - -Usage example: - from settings import SnowflakeAdminSettings, SnowflakeSettings - from snowflake_session import create_admin_session, create_session - - # Admin session for bootstrap operations - with create_admin_session(SnowflakeAdminSettings.from_env()) as session: - session.sql("CREATE DATABASE IF NOT EXISTS mydb").collect() - - # Full session for data operations - with create_session(SnowflakeSettings.from_env()) as session: - session.sql("SELECT * FROM mytable").collect() -""" - -import logging - -from snowflake.snowpark import Session - -from settings import SnowflakeAdminSettings, SnowflakeSettings - -logger: logging.Logger = logging.getLogger(__name__) - -# TODO: Evaluate single SnowflakeSettings class with optional fields - - -def create_admin_session(settings: SnowflakeAdminSettings) -> Session: - """Create a Snowflake session for administrative operations. - - This session connects without specifying warehouse or database, - allowing creation of those resources during bootstrap. - - Raises: - SnowflakeSessionError: If connection to Snowflake fails. - """ - connection_params: dict[str, str | int] = { - "account": settings.account, - "user": settings.user, - "password": settings.password.get_secret_value(), - "role": settings.role, - } - - return _create_session(connection_params) - - -def create_session(settings: SnowflakeSettings) -> Session: - """Create a Snowflake session for data operations. - - This session connects with full context including warehouse, - database, and schema for executing queries and loading data. - - Raises: - SnowflakeSessionError: If connection to Snowflake fails. - """ - connection_params: dict[str, str | int] = { - "account": settings.account, - "user": settings.user, - "password": settings.password.get_secret_value(), - "role": settings.role, - "warehouse": settings.warehouse, - "database": settings.database, - "schema": settings.schema_name, - } - - return _create_session(connection_params) - - -class SnowflakeSessionError(Exception): - """Raised when Snowflake session creation fails.""" - - -def _create_session(connection_params: dict[str, str | int]) -> Session: - """Create a Snowpark session with the given parameters.""" - try: - return Session.builder.configs(connection_params).create() - except Exception as e: - raise SnowflakeSessionError( - f"Failed to create Snowflake session. " - f"Account: {connection_params.get('account')}, " - f"User: {connection_params.get('user')}. " - f"Error: {e}" - ) from e diff --git a/integration/snowflake/tests/cleanup_test_resources.py b/integration/snowflake/tests/cleanup_test_resources.py index 10260de..b4e4198 100644 --- a/integration/snowflake/tests/cleanup_test_resources.py +++ b/integration/snowflake/tests/cleanup_test_resources.py @@ -87,8 +87,7 @@ def main() -> None: """Run cleanup as standalone script.""" sys.path.insert(0, str(Path(__file__).parent.parent)) - from settings import SnowflakeSettings - from snowflake_session import create_session + from data import SnowflakeSettings, create_session logging.basicConfig( level=logging.INFO, diff --git a/integration/snowflake/tests/integration/conftest.py b/integration/snowflake/tests/integration/conftest.py index 4aafdfd..22ce225 100644 --- a/integration/snowflake/tests/integration/conftest.py +++ b/integration/snowflake/tests/integration/conftest.py @@ -7,12 +7,10 @@ 4. Clean up test database after the test session completes Storage Configuration: -- GCS: Set GCS_STORAGE_INTEGRATION to the name of a storage integration - with access to gs://static.getml.com/datasets/jaffle_shop/ +- GCS (default): Uses HTTPS to fetch files from gs://static.getml.com/datasets/jaffle_shop/ + No credentials or storage integration required. - S3: Set S3_BUCKET_URL to a public S3 bucket with Parquet files (e.g., s3://your-bucket/jaffle_shop/) - -If neither is set, tests will be skipped. """ # ruff: noqa: G004 @@ -33,9 +31,13 @@ # Add project root to path for imports sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -from data.ingestion import DEFAULT_GCS_BUCKET, load_from_gcs, load_from_s3 -from settings import SnowflakeSettings -from snowflake_session import create_session +from data import ( + DEFAULT_GCS_BUCKET, + SnowflakeSettings, + create_session, + load_from_gcs, + load_from_s3, +) from tests.cleanup_test_resources import cleanup_test_databases logger = logging.getLogger(__name__) @@ -58,7 +60,6 @@ class StorageConfig: provider: CloudProvider bucket_url: str - storage_integration: str | None = None def load_jaffle_shop_data( @@ -70,13 +71,7 @@ def load_jaffle_shop_data( Dispatches to load_from_gcs or load_from_s3 based on the config's provider. """ if config.provider == CloudProvider.GCS: - if config.storage_integration is None: - raise ValueError("GCS provider requires storage_integration") - return load_from_gcs( - session, - storage_integration=config.storage_integration, - bucket=config.bucket_url, - ) + return load_from_gcs(session, bucket=config.bucket_url) return load_from_s3(session, bucket=config.bucket_url) @@ -92,24 +87,16 @@ def _snowflake_credentials_available() -> bool: return all(os.environ.get(var) for var in required_vars) -def _get_storage_config() -> StorageConfig | None: +def _get_storage_config() -> StorageConfig: """Build StorageConfig from environment variables. Priority: - 1. GCS with storage integration (GCS_STORAGE_INTEGRATION) - 2. S3 public bucket (S3_BUCKET_URL) + 1. S3 public bucket if S3_BUCKET_URL is set + 2. GCS (default) - uses HTTPS, no credentials required Returns: - StorageConfig if configured, None otherwise. + StorageConfig with appropriate provider and bucket URL. """ - gcs_integration = os.environ.get("GCS_STORAGE_INTEGRATION") - if gcs_integration: - return StorageConfig( - provider=CloudProvider.GCS, - bucket_url=DEFAULT_GCS_BUCKET, - storage_integration=gcs_integration, - ) - s3_bucket_url = os.environ.get("S3_BUCKET_URL") if s3_bucket_url: return StorageConfig( @@ -117,7 +104,11 @@ def _get_storage_config() -> StorageConfig | None: bucket_url=s3_bucket_url, ) - return None + # Default: GCS via HTTPS (no credentials required) + return StorageConfig( + provider=CloudProvider.GCS, + bucket_url=DEFAULT_GCS_BUCKET, + ) @pytest.fixture(scope="session") @@ -149,22 +140,10 @@ def snowflake_settings() -> SnowflakeSettings: def storage_config() -> StorageConfig: """Create StorageConfig for cloud storage access. - Supports two configuration methods via environment variables: - 1. GCS: Set GCS_STORAGE_INTEGRATION to use gs://static.getml.com/datasets/jaffle_shop/ - 2. S3: Set S3_BUCKET_URL to a public S3 bucket with Parquet files - - Skips tests if neither is configured. + By default, uses GCS which fetches files via HTTPS - no credentials needed. + To use S3 instead, set S3_BUCKET_URL environment variable. """ - config = _get_storage_config() - if config is None: - pytest.skip( - "No storage configuration available. Set one of:\n" - " - GCS_STORAGE_INTEGRATION: Name of Snowflake storage integration " - "for gs://static.getml.com/datasets/jaffle_shop/\n" - " - S3_BUCKET_URL: Public S3 bucket URL with Parquet files " - "(e.g., s3://your-bucket/jaffle_shop/)" - ) - return config + return _get_storage_config() @pytest.fixture(scope="session") diff --git a/integration/snowflake/tests/integration/test_pipeline.py b/integration/snowflake/tests/integration/test_pipeline.py index bba6e08..61d9c1d 100644 --- a/integration/snowflake/tests/integration/test_pipeline.py +++ b/integration/snowflake/tests/integration/test_pipeline.py @@ -90,7 +90,7 @@ def test_prepare_weekly_sales_creates_weekly_stores_table( snowflake_session: Session, ) -> None: """Verify preparation creates weekly_stores table with data.""" - create_weekly_sales_by_store_with_target(snowflake_session) + _ = create_weekly_sales_by_store_with_target(snowflake_session) # Verify table was created with data result: list[Row] = snowflake_session.sql(""" diff --git a/integration/snowflake/tests/test_ingestion.py b/integration/snowflake/tests/test_ingestion.py index 4aada24..96cc30c 100644 --- a/integration/snowflake/tests/test_ingestion.py +++ b/integration/snowflake/tests/test_ingestion.py @@ -1,19 +1,24 @@ """Tests for data ingestion module. -Tests error handling, rollback behavior, storage integration auto-creation, -and successful data loading orchestration logic using mocked Snowflake sessions. +Tests URL conversion, download caching, file uploads, error handling, +and successful data loading orchestration logic using mocked sessions. """ # pyright: reportAny=false -from unittest.mock import MagicMock +from pathlib import Path +from unittest.mock import MagicMock, patch +import httpx import pytest from data.ingestion import ( DEFAULT_GCS_BUCKET, JAFFLE_SHOP_TABLE_NAMES, DataIngestionError, + _download_parquet_file, # pyright: ignore[reportPrivateUsage] + _gcs_to_https_url, # pyright: ignore[reportPrivateUsage] + _get_cache_dir, # pyright: ignore[reportPrivateUsage] get_table_names, load_from_gcs, load_from_s3, @@ -48,7 +53,7 @@ def test_returns_dict_with_all_jaffle_shop_tables(self) -> None: expected_keys = { "customers", "orders", - "order_items", + "items", "products", "stores", "supplies", @@ -77,13 +82,146 @@ def test_preserves_raw_prefix_in_table_name(self) -> None: assert result["stores"] == "MY_SCHEMA.raw_stores" +class TestGcsToHttpsUrl: + """Tests for _gcs_to_https_url URL conversion helper.""" + + def test_converts_gcs_prefix(self) -> None: + """Verify gcs:// prefix is converted to https://storage.googleapis.com/.""" + result = _gcs_to_https_url("gcs://bucket-name/path/to/data") + assert result == "https://storage.googleapis.com/bucket-name/path/to/data" + + def test_converts_gs_prefix(self) -> None: + """Verify gs:// prefix is converted to https://storage.googleapis.com/.""" + result = _gcs_to_https_url("gs://bucket-name/path/to/data") + assert result == "https://storage.googleapis.com/bucket-name/path/to/data" + + def test_handles_uppercase_prefix(self) -> None: + """Verify uppercase GCS:// prefix is handled.""" + result = _gcs_to_https_url("GCS://bucket-name/path") + assert result == "https://storage.googleapis.com/bucket-name/path" + + def test_removes_trailing_slash(self) -> None: + """Verify trailing slash is removed from path.""" + result = _gcs_to_https_url("gcs://bucket-name/path/") + assert result == "https://storage.googleapis.com/bucket-name/path" + + def test_raises_error_for_invalid_prefix(self) -> None: + """Verify error raised for non-GCS URL.""" + with pytest.raises(DataIngestionError) as exc_info: + _ = _gcs_to_https_url("s3://bucket-name/path") + + assert "Invalid GCS URL" in str(exc_info.value) + assert "Must start with gcs:// or gs://" in str(exc_info.value) + + def test_converts_default_bucket(self) -> None: + """Verify default GCS bucket URL is converted correctly.""" + result = _gcs_to_https_url(DEFAULT_GCS_BUCKET) + assert ( + result + == "https://storage.googleapis.com/static.getml.com/datasets/jaffle_shop" + ) + + +class TestGetCacheDir: + """Tests for _get_cache_dir cache directory helper.""" + + def test_returns_path_in_home_directory(self) -> None: + """Verify cache directory is under user's home directory.""" + result = _get_cache_dir() + assert result.is_relative_to(Path.home()) + + def test_returns_getml_jaffle_shop_path(self) -> None: + """Verify cache directory path structure.""" + result = _get_cache_dir() + assert result.parts[-3:] == (".cache", "getml", "jaffle_shop") + + def test_creates_directory_if_not_exists(self, tmp_path: Path) -> None: + """Verify cache directory is created if it doesn't exist.""" + with patch("data.ingestion.Path.home", return_value=tmp_path): + result = _get_cache_dir() + assert result.exists() + assert result.is_dir() + + +class TestDownloadParquetFile: + """Tests for _download_parquet_file download helper.""" + + def test_skips_download_if_file_exists(self, tmp_path: Path) -> None: + """Verify download is skipped when file already exists in cache.""" + dest_path = tmp_path / "test.parquet" + _ = dest_path.write_bytes(b"existing data") + + with patch("data.ingestion.httpx.stream") as mock_stream: + _download_parquet_file("https://example.com/test.parquet", dest_path) + + mock_stream.assert_not_called() + + def test_downloads_file_when_not_cached(self, tmp_path: Path) -> None: + """Verify file is downloaded when not in cache.""" + dest_path = tmp_path / "test.parquet" + test_data = b"parquet file content" + + mock_response = MagicMock() + mock_response.iter_bytes.return_value = [test_data] + mock_response.__enter__ = MagicMock(return_value=mock_response) + mock_response.__exit__ = MagicMock(return_value=False) + + with patch("data.ingestion.httpx.stream", return_value=mock_response): + _download_parquet_file("https://example.com/test.parquet", dest_path) + + assert dest_path.exists() + assert dest_path.read_bytes() == test_data + + def test_raises_error_on_http_failure(self, tmp_path: Path) -> None: + """Verify DataIngestionError raised on HTTP error.""" + dest_path = tmp_path / "test.parquet" + + mock_response = MagicMock() + mock_response.raise_for_status.side_effect = httpx.HTTPStatusError( + "Not Found", request=MagicMock(), response=MagicMock() + ) + mock_response.__enter__ = MagicMock(return_value=mock_response) + mock_response.__exit__ = MagicMock(return_value=False) + + with ( + patch("data.ingestion.httpx.stream", return_value=mock_response), + pytest.raises(DataIngestionError) as exc_info, + ): + _download_parquet_file("https://example.com/missing.parquet", dest_path) + + assert "Failed to download" in str(exc_info.value) + + def test_cleans_up_partial_download_on_failure(self, tmp_path: Path) -> None: + """Verify partial download is deleted on failure.""" + dest_path = tmp_path / "test.parquet" + + # Create a partial file to simulate interrupted download + _ = dest_path.write_bytes(b"partial") + + mock_response = MagicMock() + mock_response.raise_for_status.side_effect = httpx.HTTPStatusError( + "Connection lost", request=MagicMock(), response=MagicMock() + ) + mock_response.__enter__ = MagicMock(return_value=mock_response) + mock_response.__exit__ = MagicMock(return_value=False) + + # First call succeeds (file exists check), then fails on actual download + # Side effect: Not cached initially, then partial file exists after error + with ( + patch("data.ingestion.httpx.stream", return_value=mock_response), + patch.object(Path, "exists", side_effect=[False, True]), + pytest.raises(DataIngestionError), + ): + _download_parquet_file("https://example.com/test.parquet", dest_path) + + class TestLoadFromS3: """Tests for load_from_s3 function.""" def test_raises_error_for_invalid_bucket_url(self, mock_session: MagicMock) -> None: """Verify error raised when bucket URL doesn't start with s3://.""" with pytest.raises(DataIngestionError) as exc_info: - load_from_s3(mock_session, bucket="gcs://wrong-scheme/path") + _ = load_from_s3(mock_session, bucket="gcs://wrong-scheme/path") assert "Invalid S3 bucket URL" in str(exc_info.value) assert "Must start with s3://" in str(exc_info.value) @@ -105,17 +243,15 @@ def test_returns_dict_with_all_table_names_on_success( expected_tables = { "raw_customers", "raw_orders", - "raw_order_items", + "raw_items", "raw_products", "raw_stores", "raw_supplies", } assert set(result.keys()) == expected_tables - def test_executes_begin_and_commit_on_success( - self, mock_session: MagicMock - ) -> None: - """Verify transaction BEGIN and COMMIT are called on successful load.""" + def test_creates_external_stage_for_s3(self, mock_session: MagicMock) -> None: + """Verify external stage is created for S3 bucket.""" mock_session.sql.return_value.collect.return_value = [ {"COUNT": 100, "rows_loaded": 100} ] @@ -123,21 +259,10 @@ def test_executes_begin_and_commit_on_success( _ = load_from_s3(mock_session, bucket="s3://test-bucket/data") sql_calls = _extract_sql_calls(mock_session) - assert "BEGIN" in sql_calls - assert "COMMIT" in sql_calls - - def test_executes_rollback_on_exception(self, mock_session: MagicMock) -> None: - """Verify ROLLBACK is called when an exception occurs during loading.""" - mock_session.sql.return_value.collect.side_effect = [ - [], # BEGIN - Exception("Database error"), # Schema creation fails - ] - - with pytest.raises(DataIngestionError): - _ = load_from_s3(mock_session, bucket="s3://test-bucket/data") - - sql_calls = _extract_sql_calls(mock_session) - assert "ROLLBACK" in sql_calls + stage_calls = [c for c in sql_calls if "CREATE OR REPLACE STAGE" in c] + assert len(stage_calls) > 0 + # S3 stages use URL parameter + assert any("s3://test-bucket/data" in c for c in stage_calls) def test_uses_custom_destination_schema(self, mock_session: MagicMock) -> None: """Verify custom destination schema is used in SQL calls.""" @@ -160,104 +285,122 @@ class TestLoadFromGcs: """Tests for load_from_gcs function.""" def test_uses_default_gcs_bucket_when_not_specified( - self, mock_session: MagicMock + self, mock_session: MagicMock, tmp_path: Path ) -> None: """Verify default GCS bucket URL is used when bucket not specified.""" mock_session.sql.return_value.collect.return_value = [ {"COUNT": 100, "rows_loaded": 100} ] + mock_session.file.put.return_value = [] + + # Create fake cached files + with ( + patch("data.ingestion._get_cache_dir", return_value=tmp_path), + patch("data.ingestion._download_parquet_file") as mock_download, + ): + # Create fake files for all tables + for table_name in JAFFLE_SHOP_TABLE_NAMES: + _ = (tmp_path / f"{table_name}.parquet").write_bytes(b"test") + + _ = load_from_gcs(mock_session) + + # Verify download was called with correct URLs + download_calls = mock_download.call_args_list + for call in download_calls: + url = call.args[0] + assert "storage.googleapis.com/static.getml.com/datasets/jaffle_shop" in url + + def test_downloads_parquet_files_to_cache( + self, mock_session: MagicMock, tmp_path: Path + ) -> None: + """Verify parquet files are downloaded to cache directory.""" + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_loaded": 100} + ] + mock_session.file.put.return_value = [] - _ = load_from_gcs( - mock_session, - storage_integration="TEST_INTEGRATION", - ) + with ( + patch("data.ingestion._get_cache_dir", return_value=tmp_path), + patch("data.ingestion._download_parquet_file") as mock_download, + ): + # Create fake files for all tables + for table_name in JAFFLE_SHOP_TABLE_NAMES: + _ = (tmp_path / f"{table_name}.parquet").write_bytes(b"test") - sql_calls = _extract_sql_calls(mock_session) - stage_calls = [c for c in sql_calls if "CREATE OR REPLACE STAGE" in c] - assert any(DEFAULT_GCS_BUCKET in c for c in stage_calls) + _ = load_from_gcs(mock_session) + + # Verify download was called for each table + assert mock_download.call_count == len(JAFFLE_SHOP_TABLE_NAMES) - def test_verifies_storage_integration_exists(self, mock_session: MagicMock) -> None: - """Verify storage integration is checked before loading.""" + def test_uploads_files_to_internal_stage( + self, mock_session: MagicMock, tmp_path: Path + ) -> None: + """Verify files are uploaded to internal Snowflake stage.""" mock_session.sql.return_value.collect.return_value = [ {"COUNT": 100, "rows_loaded": 100} ] + mock_session.file.put.return_value = [] - _ = load_from_gcs( - mock_session, - storage_integration="MY_INTEGRATION", - ) + with ( + patch("data.ingestion._get_cache_dir", return_value=tmp_path), + patch("data.ingestion._download_parquet_file"), + ): + # Create fake files for all tables + for table_name in JAFFLE_SHOP_TABLE_NAMES: + _ = (tmp_path / f"{table_name}.parquet").write_bytes(b"test") - sql_calls = _extract_sql_calls(mock_session) - describe_calls = [c for c in sql_calls if "DESCRIBE" in c.upper()] - assert len(describe_calls) > 0 + _ = load_from_gcs(mock_session) - def test_raises_error_when_integration_missing_and_no_factory( - self, mock_session: MagicMock - ) -> None: - """Verify error raised when integration missing and no factory provided.""" - mock_session.sql.return_value.collect.side_effect = Exception( - "Integration not found" - ) + # Verify file.put was called for each file + assert mock_session.file.put.call_count == len(JAFFLE_SHOP_TABLE_NAMES) - with pytest.raises(DataIngestionError) as exc_info: - _ = load_from_gcs( - mock_session, - storage_integration="MISSING_INTEGRATION", - ) + def test_creates_internal_stage_not_external( + self, mock_session: MagicMock, tmp_path: Path + ) -> None: + """Verify internal stage is created (no URL parameter).""" + mock_session.sql.return_value.collect.return_value = [ + {"COUNT": 100, "rows_loaded": 100} + ] + mock_session.file.put.return_value = [] - assert "does not exist" in str(exc_info.value) - assert "integration_factory" in str(exc_info.value) + with ( + patch("data.ingestion._get_cache_dir", return_value=tmp_path), + patch("data.ingestion._download_parquet_file"), + ): + # Create fake files for all tables + for table_name in JAFFLE_SHOP_TABLE_NAMES: + _ = (tmp_path / f"{table_name}.parquet").write_bytes(b"test") - def test_attempts_auto_creation_when_factory_provided( - self, mock_session: MagicMock - ) -> None: - """Verify auto-creation is attempted when factory is provided.""" - mock_factory = MagicMock() - call_count = 0 - - def sql_side_effect( - sql: str | None = None, query: str | None = None - ) -> MagicMock: - nonlocal call_count - sql_str = sql or query or "" - result = MagicMock() - if "DESCRIBE" in sql_str.upper(): - call_count += 1 - if call_count == 1: - result.collect.side_effect = Exception("Integration not found") - else: - result.collect.return_value = [] - else: - result.collect.return_value = [{"COUNT": 100, "rows_loaded": 100}] - return result - - mock_session.sql.side_effect = sql_side_effect - - _ = load_from_gcs( - mock_session, - storage_integration="NEW_INTEGRATION", - integration_factory=mock_factory, - ) + _ = load_from_gcs(mock_session) - mock_factory.assert_called_once() + sql_calls = _extract_sql_calls(mock_session) + stage_calls = [c for c in sql_calls if "CREATE OR REPLACE STAGE" in c] + # Internal stages don't have URL parameter + assert len(stage_calls) > 0 + assert not any("URL" in c for c in stage_calls) - def test_uses_storage_integration_in_stage_creation( - self, mock_session: MagicMock + def test_returns_dict_with_all_table_names_on_success( + self, mock_session: MagicMock, tmp_path: Path ) -> None: - """Verify storage integration is included in stage creation SQL.""" + """Verify successful load returns dict mapping table names to row counts.""" mock_session.sql.return_value.collect.return_value = [ {"COUNT": 100, "rows_loaded": 100} ] + mock_session.file.put.return_value = [] - _ = load_from_gcs( - mock_session, - storage_integration="MY_GCS_INTEGRATION", - ) + with ( + patch("data.ingestion._get_cache_dir", return_value=tmp_path), + patch("data.ingestion._download_parquet_file"), + ): + # Create fake files for all tables + for table_name in JAFFLE_SHOP_TABLE_NAMES: + _ = (tmp_path / f"{table_name}.parquet").write_bytes(b"test") - sql_calls = _extract_sql_calls(mock_session) - stage_calls = [c for c in sql_calls if "STORAGE_INTEGRATION" in c] - assert len(stage_calls) > 0 - assert any("MY_GCS_INTEGRATION" in c for c in stage_calls) + result = load_from_gcs(mock_session) + + assert isinstance(result, dict) + expected_tables = set(JAFFLE_SHOP_TABLE_NAMES) + assert set(result.keys()) == expected_tables class TestDataIngestionError: @@ -265,10 +408,7 @@ class TestDataIngestionError: def test_wraps_exception_with_context(self, mock_session: MagicMock) -> None: """Verify exceptions are wrapped in DataIngestionError with context.""" - mock_session.sql.return_value.collect.side_effect = [ - [], # BEGIN - Exception("Connection lost"), - ] + mock_session.sql.return_value.collect.side_effect = Exception("Connection lost") with pytest.raises(DataIngestionError) as exc_info: _ = load_from_s3(mock_session, bucket="s3://test/data") diff --git a/integration/snowflake/tests/test_preparation.py b/integration/snowflake/tests/test_preparation.py index 3f77cfe..9bf9f9d 100644 --- a/integration/snowflake/tests/test_preparation.py +++ b/integration/snowflake/tests/test_preparation.py @@ -6,14 +6,13 @@ # pyright: reportAny=false -from unittest.mock import MagicMock, patch +from unittest.mock import MagicMock import pytest from data.preparation import ( - DEFAULT_POPULATION_TABLE_NAME, DataPreparationError, - _validate_source_tables, + _validate_source_tables, # pyright: ignore[reportPrivateUsage] create_weekly_sales_by_store_with_target, ) @@ -65,85 +64,4 @@ def test_validates_source_tables_first(self, mock_session: MagicMock) -> None: ) with pytest.raises(DataPreparationError, match="Failed to validate"): - create_weekly_sales_by_store_with_target(mock_session) - - @patch("data.preparation._display_recent_snapshots") - @patch("data.preparation._perform_data_quality_check") - @patch("data.preparation._display_overall_statistics") - @patch("data.preparation._display_store_statistics") - @patch("data.preparation._display_sample_data") - @patch("data.preparation._create_target_view") - @patch("data.preparation._create_weekly_stores_table") - @patch("data.preparation._analyze_and_display_stores") - @patch("data.preparation._ensure_target_schema") - @patch("data.preparation._validate_source_tables") - def test_uses_default_schemas( # noqa: PLR0913, PLR0917 - self, - mock_validate: MagicMock, - mock_ensure_schema: MagicMock, - mock_analyze: MagicMock, - mock_create_table: MagicMock, - mock_create_view: MagicMock, - mock_sample: MagicMock, - mock_store_stats: MagicMock, - mock_overall_stats: MagicMock, - mock_quality: MagicMock, - mock_recent: MagicMock, - mock_session: MagicMock, - ) -> None: - """Verify default RAW and PREPARED schemas are used.""" - mock_create_table.return_value = [] - - result = create_weekly_sales_by_store_with_target(mock_session) - - mock_validate.assert_called_once_with(mock_session, "RAW") - mock_ensure_schema.assert_called_once_with(mock_session, "PREPARED") - mock_analyze.assert_called_once_with(mock_session, "RAW") - mock_create_table.assert_called_once_with(mock_session, "RAW", "PREPARED") - mock_create_view.assert_called_once_with( - mock_session, "RAW", "PREPARED", DEFAULT_POPULATION_TABLE_NAME - ) - assert result == f"PREPARED.{DEFAULT_POPULATION_TABLE_NAME}" - - @patch("data.preparation._display_recent_snapshots") - @patch("data.preparation._perform_data_quality_check") - @patch("data.preparation._display_overall_statistics") - @patch("data.preparation._display_store_statistics") - @patch("data.preparation._display_sample_data") - @patch("data.preparation._create_target_view") - @patch("data.preparation._create_weekly_stores_table") - @patch("data.preparation._analyze_and_display_stores") - @patch("data.preparation._ensure_target_schema") - @patch("data.preparation._validate_source_tables") - def test_uses_custom_schemas( # noqa: PLR0913, PLR0917 - self, - mock_validate: MagicMock, - mock_ensure_schema: MagicMock, - mock_analyze: MagicMock, - mock_create_table: MagicMock, - mock_create_view: MagicMock, - mock_sample: MagicMock, - mock_store_stats: MagicMock, - mock_overall_stats: MagicMock, - mock_quality: MagicMock, - mock_recent: MagicMock, - mock_session: MagicMock, - ) -> None: - """Verify custom source and target schemas are used when provided.""" - mock_create_table.return_value = [] - - result = create_weekly_sales_by_store_with_target( - mock_session, - source_schema="MY_RAW", - target_schema="MY_PREPARED", - table_name="CUSTOM_TABLE", - ) - - mock_validate.assert_called_once_with(mock_session, "MY_RAW") - mock_ensure_schema.assert_called_once_with(mock_session, "MY_PREPARED") - mock_analyze.assert_called_once_with(mock_session, "MY_RAW") - mock_create_table.assert_called_once_with(mock_session, "MY_RAW", "MY_PREPARED") - mock_create_view.assert_called_once_with( - mock_session, "MY_RAW", "MY_PREPARED", "CUSTOM_TABLE" - ) - assert result == "MY_PREPARED.CUSTOM_TABLE" + _ = create_weekly_sales_by_store_with_target(mock_session) diff --git a/integration/snowflake/tests/test_sql_loader.py b/integration/snowflake/tests/test_sql_loader.py index baa4ac6..28e644c 100644 --- a/integration/snowflake/tests/test_sql_loader.py +++ b/integration/snowflake/tests/test_sql_loader.py @@ -8,7 +8,7 @@ import pytest -from sql_loader import load_sql +from data import load_sql class TestLoadSql: @@ -16,7 +16,7 @@ class TestLoadSql: def test_loads_existing_sql_file(self) -> None: """Verify load_sql reads content from existing SQL file.""" - content = load_sql("preparation/create_schema.sql", schema_name="PREPARED") + content = load_sql("common/create_schema.sql", schema_name="PREPARED") assert "CREATE SCHEMA" in content assert "PREPARED" in content @@ -24,10 +24,12 @@ def test_loads_existing_sql_file(self) -> None: def test_applies_formatting_when_kwargs_provided(self) -> None: """Verify load_sql applies string formatting with provided kwargs.""" content: str = load_sql( - path="preparation/sample_data_by_store.sql", + path="preparation/snapshots_by_store.sql", target_schema="PREPARED", table_name="WEEKLY_SALES", store_name="Test Store", + order_direction="DESC", + limit="5", ) assert "Test Store" in content @@ -36,7 +38,7 @@ def test_applies_formatting_when_kwargs_provided(self) -> None: def test_returns_raw_content_when_no_kwargs(self) -> None: """Verify load_sql returns unmodified content without kwargs.""" content: str = load_sql( - path="ingestion/create_schema.sql", + path="common/create_schema.sql", schema_name="RAW", ) @@ -52,9 +54,11 @@ def test_raises_file_not_found_for_missing_file(self) -> None: def test_preserves_placeholders_when_no_kwargs(self) -> None: """Verify load_sql preserves {placeholders} when no kwargs provided.""" - # sample_data_by_store.sql has {store_name} and {table_name} placeholders - content: str = load_sql(path="preparation/sample_data_by_store.sql") + # snapshots_by_store.sql has {store_name}, {table_name}, etc. placeholders + content: str = load_sql(path="preparation/snapshots_by_store.sql") # Placeholders should remain in the content assert "{store_name}" in content assert "{table_name}" in content + assert "{order_direction}" in content + assert "{limit}" in content diff --git a/integration/snowflake/uv.lock b/integration/snowflake/uv.lock index 3ef8d12..2c6ec8a 100644 --- a/integration/snowflake/uv.lock +++ 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