7 Streams and state
This chapter covers
- Adding stateful operations to Kafka Streams
- Using state stores in Kafka Streams
- Enriching event streams with joins
- Learning how 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 purchase activity. Although you created a nontrivial processing topology, it was one-dimensional in that all transformations and operations were stateless. You considered each transaction in isolation, without regard to other events coinciding or within certain time boundaries, either before or after the transaction. Also, you only dealt with individual streams, ignoring any possibility of gaining additional insight by joining streams together.
In this chapter, you’ll extract the maximum amount of information from the Kafka Streams application. To get this level of information, you’ll need to use state. State is nothing more than the ability to recall information you’ve seen before and connect it to current information. You can utilize state in different ways. We’ll look at one example when we explore the stateful operations provided by the Kafka Streams DSL, such as the accumulation of values.