Work allocation in Kafka Streams


Here are some high-level notes about work allocation in a Kafka Streams application. Posted by Thomas Sutton on November 15, 2018

This is mostly an exercise in writing things to help remembering them myself. You’re better off referring to Confluent’s Kafka Streams documentation or this blog post by Andy Bryant.

Kafka

Apache Kafka is a “distributed streaming platform”. Messages with keys and values are written to topics (“queues”, if that helps to think about them). Each topic is divided (when it’s created) into a number of partitions. It’s topic partitions are the unit of work in a Kafka cluster: at any given time a single cluster node is responsible for processing the messages for a given topic.

Applications which read from a Kafka topic can also be distributed - each partition can be consumed (“read”) by a different worker. The collection of workers cooperating to process a topic form a consumer group. Kafka’s consumer group API helps to assign the work (i.e. the partitions) to the available workers.

Kafka Streams

Kafka Streams is a library for building streaming data processing applications on top of Kafka. Streams applications are just normal Java programs which can be deployed, monitored, and managed just like any other Java program: as many instances as you start will self-organise and cooperate to share the available work between them. This makes scaling Streams applications very straightforward: just start or kill some instances (assuming there are work units that can be re-/allocated).

A Kafka Streams application is described by a topology – essentially a directed acyclic graph with nodes representing each source, sink, and processing step. Each topology can be split into subtopologies with nodes which interact only with other nodes in the same subtopology. Because the nodes in a subtopology only interact with each other, the subtopologies can be executed in parallel without any coordination required. The collection of subtopologies together with the collection of partitions in the input topics for each subtopology will define the collection of stream tasks that can be distributed across the workers in the Streams applications.

The first phase in executing a topology analyses it and the Kafka cluster and determines the units of work that must be scheduled:

  1. Partition the topology into subtopologies.

  2. For each subtopology, check that the input topics have the same key configuration and the same number of partitions. This ensures that corresponding records from each of the input topics will be processed by the same stream task, allowing them to be joined, etc.

  3. For each subtopology, generate one stream task to read from each set of corresponding partitions in the input topics. If subtopology 1 reads from topics A, B, and C and they are configured with 3 partitions then this will result in three stream tasks “1_0”, “1_1”, and “1_2”.

Note that the collection of stream tasks generated from a topology is static: both the graph in a topology and the number of partitions in a Kafka topic are fixed at creation. The next phase allocates the stream tasks to be executed by application instances.

  1. Each instance executes a number of stream threads determined by its configuration. Each stream thread is a more or less independent worker able to process one or more stream tasks.

  2. Each stream thread will connect to the Kafka cluster using the consumer group API. The Kafka cluster and the Streams application instances will cooperate to allocate the available work to the available workers. From the application’s perspective this means allocating stream tasks to stream threads and from the Kafka cluster’s perspective this is topic partitions to consumers (and it just happens to be the case that we’ll co-allocate partitions of certain topics to the same workers).

With all this done, the Kafka Streams application is able to start processing messages.

This post was published on November 15, 2018 and last modified on December 4, 2018. It is tagged with: distributed systems, kafka, streaming, data.