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
- Apache Flink 1.14.5 Release Announcement
- The Apache Flink Community is pleased to announce another bug fix release for Flink 1.14.
- Adaptive Batch Scheduler: Automatically Decide Parallelism of Flink Batch Jobs
- To automatically decide parallelism of Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
- Apache Flink Kubernetes Operator 1.0.0 Release Announcement
In the last two months since our initial preview release the community has been hard at work to stabilize and improve the core Flink Kubernetes Operator logic. We are now proud to announce the first production ready release of the operator project.
- Improving speed and stability of checkpointing with generic log-based incremental checkpoints
- This post describes the mechanism introduced in Flink 1.15 that continuously uploads state changes to a durable storage while performing materialization in the background
- Getting into Low-Latency Gears with Apache Flink - Part Two
- This multi-part series of blog post presents a collection of low-latency techniques in Flink. Following with part one, Part two continues with a few more techniques that optimize latency directly.