And while the Table API/SQL already has unified operators, using lower-level abstractions still requires you to choose between two semantically different APIs for batch (DataSet API) and streaming (DataStream API). New Features and Improvements Batch Execution Mode in the DataStream APIįlink’s core APIs have developed organically over the lifetime of the project, and were initially designed with specific use cases in mind.
#Steven vimr download#
We encourage you to download the release and share your feedback with the community through the Flink mailing lists or JIRA.
Please review the release notes carefully, and check the complete release changelog and updated documentation for more details. The binary distribution and source artifacts are now available on the updated Downloads page of the Flink website, and the most recent distribution of PyFlink is available on PyPI. Other Improvements to the Table API/SQL.Table API/SQL: Support for Temporal Table Joins in SQL.Table API/SQL: Metadata Handling in SQL Connectors.Kubernetes High Availability (HA) Service.Batch Execution Mode in the DataStream API.This blog post describes all major new features and improvements, important changes to be aware of and what to expect moving forward. Support for the DataStream API in PyFlink expands its usage to more complex scenarios that require fine-grained control over state and time, and it’s now possible to deploy PyFlink jobs natively on Kubernetes. Temporal table joins can now also be fully expressed in SQL, no longer depending on the Table API. The Kafka SQL connector has been extended to work in upsert mode, supported by the ability to handle connector metadata in SQL DDL. Kubernetes-based High Availability (HA) was implemented as an alternative to ZooKeeper for highly available production setups. This is the next major milestone towards achieving a truly unified runtime for both batch and stream processing. The community has added support for efficient batch execution in the DataStream API. Marta Paes ( & Aljoscha Krettek ( Apache Flink community is excited to announce the release of Flink 1.12.0! Close to 300 contributors worked on over 1k threads to bring significant improvements to usability as well as new features that simplify (and unify) Flink handling across the API stack. Flink Table Store Master (Latest Snapshot).Flink Table Store 0.1 (Latest stable release).Flink Kubernetes Operator Main (Latest Snapshot).Flink Kubernetes Operator 1.1 (Latest stable release).Flink Stateful Functions Master (Latest Snapshot).Flink Stateful Functions 3.2 (Latest stable release).
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