What is Kafka Streams?

The Core Problem: You already have Apache Kafka as your event backbone, handling millions of events per second for logs, metrics, and change data. But now you need to process those streams: filter events, join multiple streams, compute aggregations, maintain state. The traditional approach means deploying and operating a separate stream processing cluster like Apache Flink or Spark Streaming. Kafka Streams eliminates that operational burden. It's a library you embed directly into your microservices. Your application becomes the stream processor. How It Actually Works: You define a topology, which is a directed acyclic graph (DAG) describing how data flows through your processing logic. Source processors read from Kafka topics. Stream processors transform, filter, join, or aggregate that data. Sink processors write results back to Kafka or external systems. Here's the key insight: this topology gets split into tasks, and each task is bound to specific Kafka partitions. If you have 96 input partitions, you get 96 tasks. These tasks are your unit of parallelism. Deploy 8 application instances, and Kafka automatically distributes those 96 tasks across your instances using the consumer group protocol. The State Story: Unlike simple consumers that just read and forward, Kafka Streams handles stateful operations like windowed aggregations or stream joins. It maintains local state stores, typically embedded key value stores backed by disk, right on your application nodes. Every state change is also written to a Kafka changelog topic. If an instance crashes, another instance can restore the state by replaying that changelog. This co-location of compute and state is what makes Kafka Streams fast for stateful operations, avoiding the round trip latency to remote databases.