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.
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.
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.