Stream where the elements themselves are Streams which continually request records for a single partition. These Streams will have to be processed in parallel, using parJoin or parJoinUnbounded. Note that when using parJoin(n) and n is smaller than the number of currently assigned partitions, then there will be assigned partitions which won't be processed. For that reason, prefer parJoinUnbounded and the actual limit will be the number of assigned partitions.

If you do not want to process all partitions in parallel, then you can use stream instead, where records for all partitions are in a single Stream.

Stream where each element contains a current assignment. The current assignment is the Map, where keys is a TopicPartition, and values are streams with records for a particular TopicPartition.

New assignments will be received on each rebalance. On rebalance, Kafka revoke all previously assigned partitions, and after that assigned new partitions all at once. partitionsMapStream reflects this process in a streaming manner.

Note, that partition streams for revoked partitions will be closed after the new assignment comes.

This is the most generic Stream method. If you don't need such control, consider using partitionedStream or stream methods. They are both based on a partitionsMapStream.

Stops consuming new messages from Kafka. This method could be used to implement a graceful shutdown.

This method has a few effects: 1. After this call no more data will be fetched from Kafka through the poll method. 2. All currently running streams will continue to run until all in-flight messages will be processed. It means that streams will be completed when all fetched messages will be processed.

If some of the stream methods will be called after stopConsuming call, these methods will return empty streams.

More than one call of stopConsuming will have no effect.

Alias for partitionedStream.parJoinUnbounded. See partitionedStream for more information.