Fluent Bit stream processor uses common SQL to perform record queries. The following section describe the features available and examples of it.
You can find the detailed query language syntax in BNF form here. The following section will be a brief introduction on how to write SQL queries for Fluent Bit stream processing.
Select keys from records coming from a stream or records matching a specific Tag pattern. Note that a simple SELECT
statement not associated from a stream creation will send the results to the standard output interface (stdout), useful for debugging purposes.
The query allows filtering the results by applying a condition using WHERE
statement. We will explain WINDOW
and GROUP BY
statements later in aggregation functions section.
Select all keys from records coming from a stream called apache:
Select code key from records which Tag starts with apache.:
Since the TAG selector allows the use of wildcards, we put the value between single quotes.
Create a new stream of data using the results from the SELECT
statement. New stream created can be optionally re-ingested back into Fluent Bit pipeline if the property Tag is set in the WITH statement.
Create a new stream called hello from stream called apache:
Create a new stream called hello for all records which original Tag starts with apache:
Aggregation functions are used in results_statement
on the keys, allowing to perform data calculation on groups of records. Group of records that aggregation functions apply on are determined by WINDOW
keyword. When WINDOW
is not specified, aggregation functions apply on the current buffer of records received, which may have non-deterministic number of elements. Aggregation functions can be applied on records in a window of a specific time interval (see the syntax of WINDOW
in select statement).
Fluent Bit streaming currently supports tumbling window, which is non-overlapping window type. That means, a window of size 5 seconds performs aggregation computations on records over a 5-second interval, and then starts new calculations for the next interval.
In addition, the syntax support GROUP BY
statement, which groups the results by the one or more keys, when they have the same values.
Calculates the average of request sizes in POST requests.
Count the number of records in 5 second windows group by host IP addresses.
Gets the minimum value of a key in a set of records.
Gets the maximum value of a key in a set of records.
Calculates the sum of all values of key in a set of records.
Time functions adds a new key into the record with timing data
Add system time using format: %Y-%m-%d %H:%M:%S. Output example: 2019-03-09 21:36:05.
Add current Unix timestamp to the record. Output example: 1552196165 .
Record functions append new keys to the record using values from the record context.
Append Tag string associated to the record as a new key.
Similar to conventional SQL statements, WHERE
condition is supported in Fluent Bit query language. The language supports conditions over keys and subkeys, for instance:
It is possible to check the existence of a key in the record using record-specific function @record.contains
:
And to check if the value of a key is/is not NULL
:
Append a new key with the record Timestamp in double format: seconds.nanoseconds. Output example: 1552196165.705683 .