Apache Flink® — Stateful Computations over Data Streams



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

Pravega Flink Connector 101
A brief introduction to the Pravega Flink Connector
Apache Flink 1.14.3 Release Announcement
The Apache Flink community released the second bugfix version of the Apache Flink 1.14 series.
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.