Course: Kafka Streams 101

Stateful Fault Tolerance

6 min
Sophie Blee-GoldmanSoftware Engineer II (Course Presenter)
Bill BejeckIntegration Architect (Course Author)

Stateful Fault Tolerance

State stores in Kafka Streams are either persistent or in-memory. Both types are backed by changelog topics for durability. When a Kafka Streams application is starting up, it detects a stateful node, and if it determines that data is missing, it will restore from the changelog topic. In-memory stores don't retain records across restarts, so they need to fully restore from the changelog topic after restarts. In contrast, persistent state stores may need little to no restoration.


Changelog topics use compaction, whereby the oldest records for each key are deleted, safely leaving the most recent records. This means that your changelog topics won't endlessly grow in size.

Stateful Fault Tolerance

A full restore of stateful operations can take time. For this reason, Kafka Streams offers stand-by tasks. When you set num.standby.replicas to be greater than the default setting of zero, Kafka Streams designates another application instance as a standby. The standby instance keeps a mirrored store in sync with the original by reading from the changelog. When the primary instance goes down, the standby takes over immediately.

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