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
- Batch as a Special Case of Streaming and Alibaba's contribution of Blink
- A few weeks ago, Alibaba contributed its Flink-fork 'Blink' back to Apache Flink. In this blog post we discuss how Blink's features will help the Flink community to make a big step towards its vision to build a truly unified system for stream and batch processing.
- Apache Flink 1.5.6 Released
The Apache Flink community released the sixth and last bugfix version of the Apache Flink 1.5 series.
- Apache Flink 1.6.3 Released
The Apache Flink community released the third bugfix version of the Apache Flink 1.6 series.
- Apache Flink 1.7.1 Released
The Apache Flink community released the first bugfix version of the Apache Flink 1.7 series.
- Apache Flink 1.7.0 Release Announcement
The Apache Flink community is pleased to announce Apache Flink 1.7.0. The latest release includes more than 420 resolved issues and some exciting additions to Flink that we describe in the following sections of this post. Please check the complete changelog for more details.