Apache Flink 1.9.1 Released
18 Oct 2019 Jark Wu (@JarkWu)
The Apache Flink community released the first bugfix version of the Apache Flink 1.9 series.
18 Oct 2019 Jark Wu (@JarkWu)
The Apache Flink community released the first bugfix version of the Apache Flink 1.9 series.
13 Sep 2019 Seth Wiesman (@sjwiesman) & Fabian Hueske (@fhueske)
This post explores the State Processor API, introduced with Flink 1.9.0, why this feature is a big step for Flink, what you can use it for, how to use it and explores some future directions that align the feature with Apache Flink's evolution into a system for unified batch and stream processing.
11 Sep 2019 Jark Wu (@JarkWu)
The Apache Flink community released the second bugfix version of the Apache Flink 1.8 series.
10 Sep 2019 Marta Paes (@morsapaes)
This has been an exciting, fast-paced year for the Apache Flink community. But with over 10k messages across the mailing lists, 3k Jira tickets and 2k pull requests, it is not easy to keep up with the latest state of the project. Plus everything happening around it. With that in mind, we want to bring back regular community updates to the Flink blog.
22 Aug 2019
The Apache Flink community is proud to announce the release of Apache Flink 1.9.0.
23 Jul 2019 Nico Kruber & Piotr Nowojski
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.
02 Jul 2019 Jincheng Sun (@sunjincheng121)
The Apache Flink community released the first bugfix version of the Apache Flink 1.8 series.
26 Jun 2019 Fabian Hueske (@fhueske)
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.
05 Jun 2019 Nico Kruber
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.
19 May 2019 Fabian Hueske (@fhueske) & Andrey Zagrebin
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