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
- Flink Backward - The Apache Flink Retrospective
- A look back at the development cycle for Flink 1.14
- Sort-Based Blocking Shuffle Implementation in Flink - Part Two
- Flink has implemented the sort-based blocking shuffle (FLIP-148) for batch data processing. In this blog post, we will take a close look at the design & implementation details and see what we can gain from it.
- Sort-Based Blocking Shuffle Implementation in Flink - Part One
- Flink has implemented the sort-based blocking shuffle (FLIP-148) for batch data processing. In this blog post, we will take a close look at the design & implementation details and see what we can gain from it.
- Apache Flink 1.13.3 Released
The Apache Flink community released the third bugfix version of the Apache Flink 1.13 series.
- Apache Flink 1.14.0 Release Announcement
- The Apache Flink community is excited to announce the release of Flink 1.14.0! More than 200 contributor worked on over 1,000 issues. The release brings exciting new features like a more seamless streaming/batch integration, automatic network memory tuning, a hybrid source to switch data streams between storgage systems (e.g., Kafka/S3), Fine-grained resource management, PyFlink performance and debugging enhancements, and a Pulsar connector.