Apache Flink® — Stateful Computations over Data Streams
All streaming use cases
- Event-driven Applications
- Stream & Batch Analytics
- Data Pipelines & ETL
Guaranteed correctness
- Exactly-once state consistency
- Event-time processing
- Sophisticated late data handling
Layered APIs
- SQL on Stream & Batch Data
- DataStream API & DataSet API
- ProcessFunction (Time & State)
Operational Focus
- Flexible deployment
- High-availability setup
- Savepoints
Scales to any use case
- Scale-out architecture
- Support for very large state
- Incremental checkpointing
Excellent Performance
- Low latency
- High throughput
- In-Memory computing
- Optimising the throughput of async sinks using a custom RateLimitingStrategy
- An overview of how to optimise the throughput of async sinks using a custom RateLimitingStrategy
- Apache Flink 1.15.3 Release Announcement
- The Apache Flink Community is pleased to announce a bug fix release for Flink 1.15.
- Announcing the Release of Apache Flink 1.16
Apache Flink continues to grow at a rapid pace and is one of the most active communities in Apache. Flink 1.16 had over 240 contributors enthusiastically participating, with 19 FLIPs and 1100+ issues completed, bringing a lot of exciting features to the community.
- Apache Flink Table Store 0.2.1 Release Announcement
- The Apache Flink Community is pleased to announce a bug fix release for Flink Table Store 0.2.
- Apache Flink Kubernetes Operator 1.2.0 Release Announcement
We are proud to announce the latest stable release of the operator. The 1.2.0 release adds support for the Standalone Kubernetes deployment mode and includes several improvements to the core logic.