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
- Pravega Flink Connector 101
- A brief introduction to the Pravega Flink Connector
- Apache Flink 1.14.3 Release Announcement
- The Apache Flink community released the second bugfix version of the Apache Flink 1.14 series.
- Apache Flink ML 2.0.0 Release Announcement
- The Apache Flink community is excited to announce the release of Flink ML 2.0.0! This release involves a major refactor of the earlier Flink ML library and introduces major features that extend the Flink ML API and the iteration runtime, such as supporting stages with multi-input multi-output, graph-based stage composition, and a new stream-batch unified iteration library.
- How We Improved Scheduler Performance for Large-scale Jobs - Part Two
- Part one of this blog post briefly introduced the optimizations we’ve made to improve the performance of the scheduler; compared to Flink 1.12, the time cost and memory usage of scheduling large-scale jobs in Flink 1.14 is significantly reduced. In part two, we will elaborate on the details of these optimizations.
- How We Improved Scheduler Performance for Large-scale Jobs - Part One
- To improve the performance of the scheduler for large-scale jobs, several optimizations were introduced in Flink 1.13 and 1.14. In this blog post we'll take a look at them.