The Apache Flink community is excited to hit the double digits and announce the release of Flink 1.10.0! As a result of the biggest community effort to date, with over 1.2k issues implemented and more than 200 contributors, this release introduces significant improvements to the overall performance and stability of Flink jobs, a preview of native Kubernetes integration and great advances in Python support (PyFlink).
Flink 1.10 also marks the completion of the Blink integration, hardening streaming SQL and bringing mature batch processing to Flink with production-ready Hive integration and TPC-DS coverage. This blog post describes all major new features and improvements, important changes to be aware of and what to expect moving forward.
Read on for the improvements and let me once more point out the validation of Feasel’s Law.