Stream processing in Fluent Bit uses SQL to perform record queries.
For more information, see the stream processing README file.
Use the following SQL statements in Fluent Bit.
SELECT results_statement
FROM STREAM:stream_name | TAG:match_rule
[WINDOW TUMBLING (integer SECOND)]
[WHERE condition]
[GROUP BY groupby]Groups keys from records that originate from a specified stream, or from records that match a specific tag pattern.
{% hint style="info" %}
A SELECT statement not associated with stream creation will send the results to the standard output interface, which can be helpful for debugging purposes.
{% endhint %}
You can filter the results of this query by applying a condition by using a WHERE statement. For information about the WINDOW and GROUP BY statements, see Aggregation functions.
Selects all keys from records that originate from a stream called apache:
SELECT * FROM STREAM:apache;Selects the code key from records with tags whose name begins with apache:
SELECT code AS http_status FROM TAG:'apache.*';CREATE STREAM stream_name
[WITH (property_name=value, [...])]
AS select_statementCreates a new stream of data using the results from a SELECT statement. If the Tag property in the WITH statement is set, this new stream can optionally be re-ingested into the Fluent Bit pipeline.
Creates a new stream called hello_ from a stream called apache:
CREATE STREAM hello AS SELECT * FROM STREAM:apache;Creates a new stream called hello for all records whose original tag name begins with apache:
CREATE STREAM hello AS SELECT * FROM TAG:'apache.*';You can use aggregation functions in the results_statement on keys, which lets you perform data calculation on groups of records. These groups are determined by the WINDOW key. If WINDOW is unspecified, aggregation functions are applied to the current buffer of records received, which might have a non-deterministic number of elements. You can also apply aggregation functions to records in a window of a specific time interval.
Fluent Bit uses a tumbling window, which is non-overlapping. For example, a window size of 5 performs aggregation computations on records during a five-second interval, then starts new calculations for the next interval.
Additionally, you can use the GROUP BY statement to group results by one or more keys with matching values.
SELECT AVG(size) FROM STREAM:apache WHERE method = 'POST' ;Calculates the average size of POST requests.
SELECT host, COUNT(*) FROM STREAM:apache WINDOW TUMBLING (X SECOND) GROUP BY host;Counts the number of records in a five-second window, grouped by host IP addresses.
SELECT MIN(key) FROM STREAM:apache;Returns the minimum value of a key in a set of records.
SELECT MAX(key) FROM STREAM:apache;Returns the maximum value of a key in a set of records.
SELECT SUM(key) FROM STREAM:apache;Calculates the sum of all values of a key in a set of records.
Use time functions to add a new key with time data into a record.
SELECT NOW() FROM STREAM:apache;Adds the current system time to a record using the format %Y-%m-%d %H:%M:%S. Output example: 2019-03-09 21:36:05.
SELECT UNIX_TIMESTAMP() FROM STREAM:apache;Adds the current Unix time to a record. Output example: 1552196165.
Use record functions to append new keys to a record using values from the record's context.
SELECT RECORD_TAG() FROM STREAM:apache;Append tag string associated to the record as a new key.
SELECT RECORD_TIME() FROM STREAM:apache;Similar to conventional SQL statements, Fluent Bit supports the WHERE condition. You can use this condition in both keys and subkeys. For example:
SELECT AVG(size) FROM STREAM:apache WHERE method = 'POST' AND status = 200;You can confirm whether a key exists in a record by using the record-specific function @record.contains:
SELECT MAX(key) FROM STREAM:apache WHERE @record.contains(key);To determine if the value of a key is NULL:
SELECT MAX(key) FROM STREAM:apache WHERE key IS NULL;Or similar:
SELECT * FROM STREAM:apache WHERE user IS NOT NULL;