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
- Getting into Low-Latency Gears with Apache Flink - Part One
- This multi-part series of blog post presents a collection of low-latency techniques in Flink. Part one starts with types of latency in Flink and the way we measure the end-to-end latency, followed by a few techniques that optimize latency directly.
- Apache Flink Table Store 0.1.0 Release Announcement
The Apache Flink community is pleased to announce the preview release of the Apache Flink Table Store (0.1.0).
- The Generic Asynchronous Base Sink
- An overview of the new AsyncBaseSink and how to use it for building your own concrete sink
- Exploring the thread mode in PyFlink
- Flink 1.15 introduced a new Runtime Execution Mode named 'thread' mode in PyFlink. This post explains how it works and when to use it.
- Improvements to Flink operations: Snapshots Ownership and Savepoint Formats
- This post will outline the journey of improving snapshotting in past releases and the upcoming improvements in Flink 1.15, which includes making it possible to take savepoints in the native state backend specific format as well as clarifying snapshots ownership.