Kafka Streams

Kafka Streams, a client library, we use it to process and analyze data stored in Kafka.

It relied on important streams processing concepts like properly distinguishing between event time and processing time, windowing support, and simple yet efficient management and real-time querying of application state.

In addition, Kafka Streams has a low barrier to entry that means we can quickly write and run a small-scale proof-of-concept on a single machine. For that, we only need to run additional instances of our application on multiple machines to scale up to high-volume production workloads.

Moreover, by leveraging Kafka’s parallelism model, it transparently handles the load balancing of multiple instances of the same application.

Some key points related to Kafka Streams:

• Kafka Stream can be easily embedded in any Java application and integrated with any existing packaging, deployment and operational tools that users have for their streaming applications because it is a simple and lightweight client library.
• There are no external dependencies on systems other than Apache Kafka itself as the internal messaging layer.
• In order to enable very fast and efficient stateful operations (windowed joins and aggregations), it supports the fault-tolerant local state.
• To guarantee that each record will be processed once and only once even when there is a failure on either Streams clients or Kafka brokers in the middle of processing, it offers exactly-once processing semantics.
• In order to achieve millisecond processing latency, employs one-record-at-a-time processing. Also, with the late arrival of records, it supports event-time based windowing operations.
• Along with a high-level Streams DSL and a low-level Processor API, it offers necessary stream processing primitives.