Apache Flink® - Stateful Computations over Data Streams

All streaming use cases
  • Event-driven Applications
  • Stream & Batch Analytics
  • Data Pipelines & ETL
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Guaranteed correctness
  • Exactly-once state consistency
  • Event-time processing
  • Sophisticated late data handling
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Layered APIs
  • SQL on Stream & Batch Data
  • DataStream API & DataSet API
  • ProcessFunction (Time & State)
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Operational Focus
  • Flexible deployment
  • High-availability setup
  • Savepoints
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Scales to any use case
  • Scale-out architecture
  • Support for very large state
  • Incremental checkpointing
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Excellent Performance
  • Low latency
  • High throughput
  • In-Memory computing
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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.
State TTL in Flink 1.8.0: How to Automatically Cleanup Application State in Apache Flink
A common requirement for many stateful streaming applications is to automatically cleanup application state for effective management of your state size, or to control how long the application state can be accessed. State TTL enables application state cleanup and efficient state size management in Apache Flink
Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL
Apache Flink natively supports temporal table joins since the 1.7 release for straightforward temporal data handling. In this blog post, we provide an overview of how this new concept can be leveraged for effective point-in-time analysis in streaming scenarios.