In this blog post we describe how to tell a Kafka Streams application to reprocess its input data from scratch. This is actually a very common situation when you are implementing stream processing applications in practice, and it might be required for a number of reasons, including but not limited to: during development and testing, when addressing bugs in production, when doing A/B testing of algorithms and campaigns, when giving demos to customers or internal stakeholders, and so on.
The quick answer is you can do this either manually (cumbersome and error-prone) or you can use the new application reset tool for Kafka Streams, which is an easy-to-use solution for the problem. The application reset tool is available starting with upcoming Confluent Platform 3.0.1 and Apache Kafka 0.10.0.1.
In the first part of this post we explain how to use the new application reset tool. In the second part we discuss what is required for a proper (manual) reset of a Kafka Streams application. This parts includes a deep dive into relevant Kafka Streams internals, namely internal topics, operator state, and offset commits. As you will see, these details make manually resetting an application a bit complex, hence the motivation to create an easy to use application reset tool.
Being able to reprocess streams is a critical part of the Kappa architecture, and this article is a nice overview of how to do that if you’re using Kafka Streams.