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 1.9.0 Release Announcement
The Apache Flink community is proud to announce the release of Apache Flink 1.9.0.
- Flink Network Stack Vol. 2: Monitoring, Metrics, and that Backpressure Thing
- In a previous blog post, we presented how Flink’s network stack works from the high-level abstractions to the low-level details. This second post discusses monitoring network-related metrics to identify backpressure or bottlenecks in throughput and latency.
- Apache Flink 1.8.1 Released
The Apache Flink community released the first bugfix version of the Apache Flink 1.8 series.
- A Practical Guide to Broadcast State in Apache Flink
- Apache Flink has multiple types of operator state, one of which is called Broadcast State. In this post, we explain what Broadcast State is, and show an example of how it can be applied to an application that evaluates dynamic patterns on an event stream.
- A Deep-Dive into Flink's Network Stack
- Flink’s network stack is one of the core components that make up Apache Flink's runtime module sitting at the core of every Flink job. In this post, which is the first in a series of posts about the network stack, we look at the abstractions exposed to the stream operators and detail their physical implementation and various optimisations in Apache Flink.