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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Apache Flink ML 2.0.0 Release Announcement
The Apache Flink community is excited to announce the release of Flink ML 2.0.0! This release involves a major refactor of the earlier Flink ML library and introduces major features that extend the Flink ML API and the iteration runtime, such as supporting stages with multi-input multi-output, graph-based stage composition, and a new stream-batch unified iteration library.
How We Improved Scheduler Performance for Large-scale Jobs - Part Two
Part one of this blog post briefly introduced the optimizations we’ve made to improve the performance of the scheduler; compared to Flink 1.12, the time cost and memory usage of scheduling large-scale jobs in Flink 1.14 is significantly reduced. In part two, we will elaborate on the details of these optimizations.
How We Improved Scheduler Performance for Large-scale Jobs - Part One
To improve the performance of the scheduler for large-scale jobs, several optimizations were introduced in Flink 1.13 and 1.14. In this blog post we'll take a look at them.
Apache Flink StateFun Log4j emergency release

The Apache Flink community has released an emergency bugfix version of Apache Flink Stateful Function 3.1.1.

Apache Flink Log4j emergency releases

The Apache Flink community has released emergency bugfix versions of Apache Flink for the 1.11, 1.12, 1.13 and 1.14 series.