Kafka is no longer a new thing especially to people who may have worked on huge amounts of data before. It is a distributed, fault-tolerant system designed for fast processing of huge volumes of data. Meanwhile, stream processing is the real-time processing of data concurrently, in a continuous and record-by-record way. Now, what are Kafka Streams and, how these help stream processing? That’s the focus of this article, so relax and read further.
Kafka Streams Explained
In simple terms, Kafka Streams is a client framework used for building streaming applications. It is used to analyse and process data stored in Kafka.
Kafka Streams rely on crucial stream processing concepts like windowing support, real-time querying of application state, and distinguishing between processing time and event time. Also, Kafka streams have a very low restriction of entry, meaning a proof-of-concept can be quickly written and run on a single machine. Kafka Streams may have some local state on disk, however, this is just a cache that can be recreated anytime even if lost or if the instance of the app is moved to another place.
Essential features you need to know about Kafka Streams:
- Kafka Streams do not have any external dependencies on systems except on Apache Kafka itself which serves as the internal messaging layer.
- It can be easily embedded in any Java application. Also, it can be integrated with existing deployment, packaging, and operational tools being used for streaming applications by users. Kafka Streams have this capability because it is a lightweight and simple client library. Also, there is no downtime rolling deployments.
- Kafka Streams offer exactly-once processing semantics which ensures that each record is processed once even if there is a failure on either Kafka brokers or stream clients during processing.
- In combination with a low-level Processor API and a high-level streams DSL, Kafka Streams offer necessary stream processing primitives.
- It supports fault-tolerant local state to enable fast and efficient stateful processing including distributed aggregations and joins. Kafka streams also balance the processing load as existing instances of your app crash or as new ones are added.
- To achieve millisecond processing latency, Kafka streams use one-record-at-a-time processing.
- It has reprocessing capabilities such that you can re-calculate output anytime your code changes.
Having read this article to this point, you will agree that Kafka stream is leading the race while other stream processing frameworks follow. It’s awesome and that’s why we have taken time to explain some of its capabilities in this piece. Meanwhile, if you need any support to use Kafka streams, remember that we are just a click away. Axual has integrated Kafka Streams into our Client libraries, making it highly available.
We can help your business gain a competitive advantage today. If you are ready, let’s go!
Download our whitepaper
Want to know how we have built a platform based on Apache Kafka, including the learnings? Fill in the form below and we send you our whitepaper.