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
- How to identify the source of backpressure?
- Apache Flink 1.13 introduced a couple of important changes in the area of backpressure monitoring and performance analysis of Flink Jobs. This blog post aims to introduce those changes and explain how to use them.
- Apache Flink 1.13.1 Released
The Apache Flink community released the first bugfix version of the Apache Flink 1.13 series.
- Apache Flink 1.12.4 Released
The Apache Flink community released the next bugfix version of the Apache Flink 1.12 series.
- Scaling Flink automatically with Reactive Mode
- Apache Flink 1.13 introduced Reactive Mode, a big step forward in Flink's ability to dynamically adjust to changing workloads, reducing resource utilization and overall costs. This blog post showcases how to use this new feature on Kubernetes, including some lessons learned.
- Apache Flink 1.13.0 Release Announcement
- The Apache Flink community is excited to announce the release of Flink 1.13.0! Around 200 contributors worked on over 1,000 issues to bring significant improvements to usability and observability as well as new features that improve the elasticity of Flink's Application-style deployments.