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.

Related Posts

Kafka Topic Reuse

Martin Kleppmann walks through the trade-offs of reusing Apache Kafka topics for different event types: The common wisdom (according to several conversations I’ve had, and according to a mailing list thread) seems to be: put all events of the same type in the same topic, and use different topics for different event types. That line of […]

Read More

Set Operations In Spark

Fisseha Berhane compares SparkSQL, DataFrames, and classic RDDs when performing certain set-based operations: In this fourth part, we will see set operators in Spark the RDD way, the DataFrame way and the SparkSQL way. Also, check out my other recent blog posts on Spark on Analyzing the Bible and the Quran using Spark and Spark […]

Read More

Categories

July 2016
MTWTFSS
« Jun Aug »
 123
45678910
11121314151617
18192021222324
25262728293031