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 1.12.2 Released

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

How to natively deploy Flink on Kubernetes with High-Availability (HA)
Kubernetes provides built-in functionalities that Flink can leverage for JobManager failover. In Flink 1.12 (FLIP-144), the community implemented a Kubernetes High Availability (HA) service as an alternative to ZooKeeper for highly available production setups. In this blogpost, we will have a close look at how to deploy Flink applications natively on Kubernetes cluster with HA.
Apache Flink 1.10.3 Released

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

Apache Flink 1.12.1 Released

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

Using RocksDB State Backend in Apache Flink: When and How
This blog post will guide you through the benefits of using RocksDB to manage your application’s state, explain when and how to use it and also clear up a few common misconceptions.