Kafka Streams in Action

Chapter 4. Streams and state

This chapter covers

• Applying stateful operations to Kafka Streams
• Using state stores for lookups and remembering previously seen data
• Joining streams for added insight
• How time and timestamps drive Kafka Streams

In the last chapter, we dove headfirst into the Kafka Streams DSL and built a processing topology to handle streaming requirements from purchases at ZMart locations. Although you built a nontrivial processing topology, it was one dimensional in that all transformations and operations were stateless. You considered each transaction in isolation, without any regard to other events occurring at the same time or within certain time boundaries, either before or after the transaction. Also, you only dealt with ...