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https://issues.apache.org/jira/browse/KAFKA-13261?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17415690#comment-17415690
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Matthias J. Sax edited comment on KAFKA-13261 at 9/15/21, 6:56 PM:
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I would prefer to do a KIP and allow user to pass in a custom partitioner.
If we really get follow up requests, we could extend the DSL logic to
auto-forward an upstream partitioner later. Overall, it seems not that there is
no bug in the strong sense, but a missing feature. We never designed FK-join to
support custom partitioning.
Might be worth to update the docs for 3.0 and earlier to point out this
limitation.
was (Author: mjsax):
I would prefer to do a KIP and allow user to pass in a custom partitioner.
If we really get follow up requests, we could extend the DSL logic to
auto-forward an upstream partitioner later.
> KTable to KTable foreign key join loose events when using several partitions
> ----------------------------------------------------------------------------
>
> Key: KAFKA-13261
> URL: https://issues.apache.org/jira/browse/KAFKA-13261
> Project: Kafka
> Issue Type: Bug
> Components: streams
> Affects Versions: 2.8.0, 2.7.1
> Reporter: Tomas Forsman
> Assignee: Victoria Xia
> Priority: Major
> Attachments: KafkaTest.java
>
>
> Two incoming streams A and B.
> Stream A uses a composite key [a, b]
> Stream B has key [b]
> Stream B has 4 partitions and steams A has 1 partition.
> What we try to do is repartition stream A to have 4 partitions too, then put
> both A and B into KTable and do a foreign key join on from A to B
> When doing this, all messages does not end up in the output topic.
> Repartitioning both to only use 1 partition each solve the problem so it seem
> like it has something to do with the foreign key join in combination with
> several partitions.
> One suspicion would be that it is not possible to define what partitioner to
> use for the join.
> Any insight or help is greatly appreciated.
> *Example code of the problem*
> {code:java}
> static Topology createTopoology(){
> var builder = new StreamsBuilder();
> KTable<String, String> tableB = builder.table("B",
> stringMaterialized("table.b"));
> builder
> .stream("A", Consumed.with(Serde.of(KeyA.class),
> Serde.of(EventA.class)))
> .repartition(repartitionTopicA())
> .toTable(Named.as("table.a"), aMaterialized("table.a"))
> .join(tableB, EventA::getKeyB, topicAandBeJoiner(),
> Named.as("join.ab"), joinMaterialized("join.ab"))
> .toStream()
> .to("output", with(...));
> return builder.build();
> }
> private static Materialized<KeyA, EventA> aMaterialized(String name) {
> Materialized<KeyA, EventA, KeyValueStore<Bytes, byte[]>> table =
> Materialized.as(name);
> return
> table.withKeySerde(Serde.of(KeyA.class)).withValueSerde(Serde.of(EventA.class));
> }
> private static Repartitioned<DriverPeriod, DriverCosts> repartitionTopicA() {
> Repartitioned<DriverPeriod, DriverCosts> repartitioned =
> Repartitioned.as("driverperiod");
> return
> repartitioned.withKeySerde(Serde.of(KeyA.class)).withValueSerde(Serde.of(EventA.class))
> .withStreamPartitioner(topicAPartitioner())
> .withNumberOfPartitions(4);
> }
> private static StreamPartitioner<DriverPeriod, DriverCosts>
> topicAPartitioner() {
> return (topic, key, value, numPartitions) ->
> Math.abs(key.getKeyB().hashCode()) % numPartitions;
> }
> private static Materialized<KeyA, EventA, KeyValueStore<Bytes, byte[]>>
> joinMaterialized(String name) {
> Materialized<DriverPeriod, DriverCosts, KeyValueStore<Bytes, byte[]>>
> table = Materialized.as(name);
> return
> table.withKeySerde(Serde.of(KeyA.class)).withValueSerde(Serde.of(EventA.class));
> }
> {code}
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