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Kafka Stream parallelizes via Kafka partitions -- for each partitions
a task is created. If you subscribe to multiple topics, the topics
with the most partitions determine the number of task, and each task
get partitions from all topics assigned.

Furthermore, you can configure the number to thread
(num.stream.threads     , see
http://docs.confluent.io/current/streams/developer-guide.html#optional-c
onfiguration-parameters)
- -- the max useful configuration is the number of created tasks. Keep
in mind, if you start multiple instanced of you Streams app,
partitions are managed in a consumer group fashion, ie, are
distributed over the running instances.

Please see here for more details
http://docs.confluent.io/current/streams/architecture.html#parallelism-m
odel


- -Matthias

On 10/4/16 1:27 PM, Ali Akhtar wrote:
> I need to consume a large number of topics, and handle each topic
> in a different way.
> 
> I was thinking about creating a different KStream for each topic,
> and doing KStream.foreach for each stream, to process incoming
> messages.
> 
> However, its unclear if this will be handled in a parallel way by
> default, or if I need to create a managed ThreadPool and create the
> KStream for each topic within its own thread pool.
> 
> Can anyone shed some light - does KStreamBuilder / KStream handle 
> concurrency for each KStream, or does this need to be managed?
> 
> Thanks.
> 
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