|GoKa Stream Processing For Kafka|
|Written by Kay Ewbank|
|Friday, 26 May 2017|
There's a new library for writing distributed stream processing applications in Go backing them with data in Kafka. GoKa aims to reduce the complexity of building highly scalable and highly available microservices.
Kafka is an Apache project that can be used for building real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant, fast, and runs in production in thousands of companies. Goka uses Kafka for message passing, fault-tolerant state storage and workload partitioning
Goka works by binding a state table to Kafka consumer groups, and persisting them in Kafka. The Goka group table represents the state of the group. A microservice modifies and serves the content of a table employing two complementary object types: processors and views.
A processor is a set of callback functions that modify the group table when messages arrive. Processors can also emit messages into other topics. Goka distributes the partitions of the input topics across all processor instances in a processor group to enable scaling and fault-tolerance. If a processor instance fails, its partitions and state are reassigned to the remaining healthy members of the processor group.
A view is a persistent cache of a group table that subscribes for the updates of all partitions of a group table and keeps local disk storage in sync with the group topic. Views provide read-only access to the group tables and can be used to provide external services.
Emitters deliver key-value messages into Kafka. The example used in the documentation is of a database handler emitting the state changes into Kafka for other interested applications to consume.
Goka handles all the message input and output. You provide one or more callback functions that handle messages from any of the Kafka topics you are interested in, and then can work with only the deserialized messages.
Goka is on GitHub complete with samples and good documentation.
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|Last Updated ( Friday, 26 May 2017 )|