A beginner's Guide to Checkpoints in Apache Flink

Every stream processing application, whether this is a streaming data pipeline or a streaming SQL application, can be stateful; meaning that it involves some sort of state.

To persist state in an easy-to-manage way and recover from a failure, Apache Flink implements a mechanism that allows reprocessing only the events from a specific point in time (previously-stored state) instead of replaying the entire history of the application.

Readers of this guide will learn:

• Why checkpoints are necessary for event streaming applications
• How checkpointing in Apache Flink works
• How to configure checkpoints in Apache Flink by choosing an application's state backend and checkpoint storage
• What are the differences between the available state backend options in Apache Flink
• How to set up checkpoint intervals in Flink