Partition Data to Enable Joins¶
When you use KSQL to join streaming data, you must ensure that your streams and tables are co-partitioned, which means that input records on both sides of the join have the same configuration settings for partitions.
- The input records for the join must have the same keying scheme
- The input records must have the same number of partitions on both sides.
- Both sides of the join must have the same partitioning strategy.
When your inputs are co-partitioned, records with the same key, from both sides of the join, are delivered to the same stream task during processing. If your inputs aren’t co-partitioned, you need to re-key one of the them by using the PARTITION BY clause.
Records Have the Same Keying Scheme¶
The input records for the join must have the same keying scheme, which means that the join must use the same key field on both sides.
For example, you can join a stream of user clicks that’s keyed by a
field with a table of user profiles that’s keyed by a
VARCHAR userId field.
The join won’t match if the key fields don’t have the same name and type.
Records Have the Same Number of Partitions¶
The input records for the join must have the same number of partitions on both sides.
KSQL checks this part of the co-partitioning requirement and throws a runtime exception if the partition count is different.
<path-to-confluent>/bin/kafka-topics CLI tool
--describe option to see the number of partitions for the
Kafka topics that correspond with your streams and tables.
Records Have the Same Partitioning Strategy¶
Records on both sides of the join must have the same partitioning strategy. If you use the default partitioner settings across all applications, you don’t need to worry about the partitioning strategy.
But if the producer applications for your records have custom partitioners specified in configuration, the same custom partitioner logic must be used for records on both sides of the join. The applications that write to the join inputs must have the same partitioning strategy, so that records with the same key are delivered to same partition number.
This means that the input records must be in the same partition on both sides
of the join. For example, in a stream-table join, if a
userId key with the
alice123 is in Partition 1 for the stream, but
alice123 is in
Partition 2 for the table, the join won’t match, even though both sides are
KSQL can’t verify whether the partitioning strategies are the same for both join inputs, so you must ensure this.
The DefaultPartitioner class implements the following partitioning strategy:
- If the producer specifies a partition in the record, use it.
- If the producer specifies a key instead of a partition, choose a partition based on a hash of the key.
- If the producer doesn’t specify a partition or a key, choose a partition in a round-robin fashion.
Custom partitioner classes implement the Partitioner interface
and are assigned in the producer configuration property,
For example implementations of a custom partitioner, see Built for realtime: Big data messaging with Apache Kafka, Part 2 and Apache Kafka Foundation Course - Custom Partitioner.
Ensure Data Co-partitioning¶
If your join inputs aren’t co-partitioned, you must ensure it manually by re-keying the data on one side of the join.
For example, in a stream-table join, if a stream of user clicks is keyed by
pageId, but a table of user profiles is keyed by
userId, one of the
two inputs must be re-keyed (re-partitioned). Which of the two should be re-keyed
depends on the situation.
If the stream has very high volume, you may not want to re-key it, because this would duplicate a large data source. Instead, you may prefer to re-key the smaller table.
To enforce co-partitioning, use the PARTITION BY clause.
For example, if you need to re-partition a stream to be keyed by a
field, and keys need to be distributed over 6 partitions to make a join work,
use the following KSQL statement:
CREATE STREAM products_rekeyed WITH (PARTITIONS=6) AS SELECT * FROM products PARTITION BY product_id;
For more information, see Inspecting and Changing Topic Keys in the Stream Processing Cookbook.