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A beginner's Guide to Checkpoints in Apache Flink

Checkpoints are one of the fundamental characteristics in Apache Flink that allow Flink to recover both its state and the corresponding stream positions in the case of failure.

This is a beginner's guide to checkpoints in Apache Flink® and provides all the necessary information about how to use Flink's checkpointing mechanism for distributed, stream processing applications.

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
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Figure 1: Internal State in Apache Flink®

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