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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Stateful Functions 2.2.0 Release Announcement

The Apache Flink community is happy to announce the release of Stateful Functions (StateFun) 2.2.0! This release introduces major features that extend the SDKs, such as support for asynchronous functions in the Python SDK, new persisted state constructs, and a new SDK that allows embedding StateFun functions within a Flink DataStream job. Moreover, we’ve also included important changes that improve out-of-the-box stability for common workloads, as well as increased observability for operational purposes.

Apache Flink 1.11.2 Released

The Apache Flink community released the second bugfix version of the Apache Flink 1.11 series.

Flink Community Update - August'20
Ah, so much for a quiet August month. This time around, we bring you some new Flink Improvement Proposals (FLIPs), a preview of the upcoming Flink Stateful Functions 2.2 release and a look into how far Flink has come in comparison to 2019.
Memory Management improvements for Flink’s JobManager in Apache Flink 1.11
In a previous blog post, we focused on the memory model of the TaskManagers and its improvements with the Apache Flink 1.10 release. This blog post addresses the same topic but for Flink's JobManager instead.
Apache Flink 1.10.2 Released

The Apache Flink community released the second bugfix version of the Apache Flink 1.10 series.