Streaming microservices with Akka Streams and Kafka Streams

Description

If you’re building streaming data apps, your first inclination might be to reach for Spark Streaming, Flink, Beam, or similar tools, which run as services to which you submit jobs for execution. But sometimes, writing conventional microservices with embedded stream processing is a better fit for your needs.

Kafka Streams and Akka Streams are both libraries that you integrate into your microservices, which means you must manage their lifecycles yourself, but you also get lots of flexibility to do this as you see fit. Kafka Streams is purpose-built for reading data from Kafka topics, processing it, and writing the results to new topics. With powerful stream and table abstractions and an exactly once capability, it supports a variety of common scenarios involving transformation, filtering, and aggregation. Akka Streams, on the other hand, emerged as a dataflow-centric abstraction for the Akka Actors model, designed for general-purpose microservices, especially when per-event low latency is important, such as for complex event processing, where each event requires individual handling. Because of its general-purpose nature, Akka Streams supports a wider class of application problems and third-party integrations, but it’s less focused on Kafka-specific capabilities.

Using an example of model serving, Boris Lublinsky walks you through building streaming apps as microservices using Akka Streams and Kafka Streams. Along the way, Boris discusses the strengths and weaknesses of each tool for particular design needs and contrasts them with Spark Streaming and Flink, so you’ll know when to choose them instead.