Scaling Kafka Streams

Kevin Feasel

2016-07-15

Hadoop

Michael Noll discusses elastic scaling of Kafka Streams:

Third, how many instances can or should you run for your application?  Is there an upper limit for the number of instances and, similarly, for the parallelism of your application?  In a nutshell, the parallelism of a Kafka Streams application — similar to the parallelism of Kafka — is primarily determined by the number of partitions of the input topic(s) from which your application is reading. For example, if your application reads from a single topic that has 10 partitions, then you can run up to 10 instances of your applications (note that you can run further instances but these will be idle).  In summary, the number of topic partitions is the upper limit for the parallelism of your Kafka Streams application and thus for the number of running instances of your application.  Note: A scaling/parallelism caveat here is that the balance of the processing work between application instances depends on how well data messages are balanced between partitions.

Check it out.  Kafka Streams is a potential alternative to Spark Streaming and Storm for real-time (for some definition of “real-time”) distributed computing.

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