Kafka Streams provides so-called state stores , which can be used by stream processing applications to store and query data, which is an important capability when implementing stateful operations. The Kafka Streams DSL, for example, automatically creates and manages such state stores when you are calling stateful operators such as join() or aggregate(), or when you are windowing a stream.
Every stream task in a Kafka Streams application may embed one or more local state stores that can be accessed via APIs to store and query data required for processing. Kafka Streams offers fault-tolerance and automatic recovery for such local state stores.
The following diagram shows two stream tasks with their dedicated local state stores.
We are going to fill in the main function to write this pipe program. Note that we will not list the import statements as we go since IDEs can usually add them automatically. However if you are using a text editor you need to manually add the imports, and at the end of this section we’ll show the complete code snippet with import statement for you.
Each topic is split into partitions, which are distributed across Kafka brokers.
Partitions enable parallelism, as multiple consumers can process data from different partitions simultaneously.
Messages in a partition are ordered and identified by an offset, a unique sequence number.
The messaging layer of Kafka partitions data for storing and transporting it. Kafka Streams partitions data for processing it. In both cases, this partitioning is what enables data locality, elasticity, scalability, high performance, and fault tolerance. Kafka Streams uses the concepts of partitions and tasks as logical units of its parallelism model based on Kafka topic partitions. There are close links between Kafka Streams and Kafka in the context of parallelism:
It is important to understand that Kafka Streams is not a resource manager, but a library that “runs” anywhere its stream processing application runs. Multiple instances of the application are executed either on the same machine, or spread across multiple machines and tasks can be distributed automatically by the library to those running application instances. The assignment of partitions to tasks never changes; if an application instance fails, all its assigned tasks will be automatically restarted on other instances and continue to consume from the same stream partitions.
Threading Model
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