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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Scaling Flink automatically with Reactive Mode
Apache Flink 1.13 introduced Reactive Mode, a big step forward in Flink's ability to dynamically adjust to changing workloads, reducing resource utilization and overall costs. This blog post showcases how to use this new feature on Kubernetes, including some lessons learned.
Apache Flink 1.13.0 Release Announcement
The Apache Flink community is excited to announce the release of Flink 1.13.0! Around 200 contributors worked on over 1,000 issues to bring significant improvements to usability and observability as well as new features that improve the elasticity of Flink's Application-style deployments.
Apache Flink 1.12.3 Released

The Apache Flink community released the next bugfix version of the Apache Flink 1.12 series.

Stateful Functions 3.0.0: Remote Functions Front and Center

The Apache Flink community is happy to announce the release of Stateful Functions (StateFun) 3.0.0! Stateful Functions is a cross-platform stack for building Stateful Serverless applications, making it radically simpler to develop scalable, consistent, and elastic distributed applications.

A Rundown of Batch Execution Mode in the DataStream API
Apache Flink 1.12 introduced batch execution mode through FLIP-140. This blog post discusses when to use the new mode, discusses its benefits and gives examples of programs that can be run in it.