Apache Flink® — Stateful Computations over Data Streams

Apache Flink® — Stateful Computations over Data Streams #

All streaming use cases
  • Event-driven Applications
  • Stream & Batch Analytics
  • Data Pipelines & ETL
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Guaranteed correctness
  • Exactly-once state consistency
  • Event-time processing
  • Sophisticated late data handling
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Layered APIs
  • SQL on Stream & Batch Data
  • DataStream API & DataSet API
  • ProcessFunction (Time & State)
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Operational Focus
  • Flexible deployment
  • High-availability setup
  • Savepoints
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Scales to any use case
  • Scale-out architecture
  • Support for very large state
  • Incremental checkpointing
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Excellent Performance
  • Low latency
  • High throughput
  • In-Memory computing
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Latest Blog Posts #

Apache Flink 1.16.2 Release Announcement
The Apache Flink Community is pleased to announce the second bug fix release of the Flink 1.16 series. This release includes 104 bug fixes, vulnerability fixes, and minor improvements for Flink 1.16. Below you will find a list of all bugfixes and improvements (excluding improvements to the build infrastructure and build stability). For a complete list of all changes see: JIRA. We highly recommend all users upgrade to Flink 1.16.2.

Apache Flink 1.17.1 Release Announcement
The Apache Flink Community is pleased to announce the first bug fix release of the Flink 1.17 series. This release includes 75 bug fixes, vulnerability fixes, and minor improvements for Flink 1.17. Below you will find a list of all bugfixes and improvements (excluding improvements to the build infrastructure and build stability). For a complete list of all changes see: JIRA. We highly recommend all users upgrade to Flink 1.17.1.

Apache Flink Kubernetes Operator 1.5.0 Release Announcement
The Apache Flink community is excited to announce the release of Flink Kubernetes Operator 1.5.0! The release focuses on improvements to the job autoscaler that was introduced in the previous release and general operational hardening of the operator. We encourage you to download the release and share your feedback with the community through the Flink mailing lists or JIRA! We hope you like the new release and we’d be eager to learn about your experience with it.