Kafka Streams：Stream 线程与多个主题的分区

Question

Suppose I have 2 topics say xyz1, xyz2, each having 3 partitions. If I have a single Kafka stream application having 3 threads, can the following scenario occur?

Thread                  Partition
    1       xyz1-partition 0, xyz2-partition 2
    2       xyz1-partition 1, xyz2-partition 0
    3       xyz1-partition 2, xyz2-partition 1

as opposed to:

Thread                  Partition
    1       xyz1-partition 0, xyz2-partition 0
    2       xyz1-partition 1, xyz2-partition 1
    3       xyz1-partition 2, xyz2-partition 2

Essentially, a single thread consuming data from a particular partition of 2 different topics and the partition number can be varying? Assuming we use low-level processor API

Answer 1

If the scenario may occur or not depends on your topology.

Actually, stream tasks are assigned to stream threads, not plain partitions. Each task may process a group of partitions. One group contains one or more partitions. If the group contains multiple partitions, it always contains the same partitions (ie, the ones with the same partition number) of different topics. For example, a group may contain xyz1-partition 0, xyz2-partition 0 but not xyz1-partition 0, xyz2-partition 2. This assumes that the different topics use the same partition strategy. Such a co-partitioning of the same partitions of different topics is needed -- for example -- in the case of a join where records with the same key must be processed by the same stream task, similarly as in your second scenario.

If you assume that in your first example each partition is processed by a different stream task, ie, each partition group contains one partition, the scenario may occur.

If you assume that both partitions on each line are processed by the same stream task (ie both partitions are part of the same partition group), the scenario cannot occur, because partition groups cannot contain different partitions.

For more information on the assignment strategy see https://github.com/apache/kafka/blob/e4262471c9aee4a4c04dd04ebbdbdba7e3c5ead1/streams/src/main/java/org/apache/kafka/streams/processor/internals/StreamsPartitionAssignor.java#L297

Said that, actually there is a way to assign different partitions to the same task by implementing the PartitionGrouper interface. However, this interface will be deprecated in the 2.4 and removed in 3.0. See https://cwiki.apache.org/confluence/display/KAFKA/KIP-528%3A+Deprecate+PartitionGrouper+configuration+and+interface .

Answer 2

Its depends

Plain Kafka Consumer:

Kafka Consumer Group consists pool of consumers/instances/processes with the same group.id can either be running on the same machine or distributed machines. Kafka Consumer uses rebalancing to assign partitions on each consumer without overlapping mean one partition can assign at most one consumer process of Consumer Group.

It is also possible for the consumer to manually assign specific partitions (similar to the older "simple" consumer) using assign(Collection). In this case, dynamic partition assignment and consumer group coordination will be disabled

So in case of partition can be assigned to any thread while rebalancing.

Kafka Stream:

Kafka uses stream tasks as a logical unit to assign partition and parallelize process. Kafka Stream creates a number of stream task based on stream partitions and assigns a list of partitions to each task. Once the task assigned to partitions it will stick and manage parallelism on their own topology. As a result stream tasks can be processed independently and in parallel without manual intervention.

Default implementation of the PartitionGrouper interface that groups partitions by the partition id. Join operations requires that topics of the joining entities are partitioned, ie, being partitioned by the same key and having the same number of partitions. Copartitioning is ensured by having the same number of partitions on joined topics, and by using the serialization and Producer's default partitioner. here

So in your case scenario-1 not possible whereas scenario-2 is possible.