infoverload commented on code in PR #526: URL: https://github.com/apache/flink-web/pull/526#discussion_r848059221
########## _posts/2022-04-11-1.15-announcement.md: ########## @@ -0,0 +1,375 @@ +--- +layout: post +title: "Announcing the Release of Apache Flink 1.15" +subtitle: "" +date: 2022-04-11T08:00:00.000Z +categories: news +authors: +- yungao: + name: "Yun Gao" + twitter: "YunGao16" +- joemoe: + name: "Joe Moser" + twitter: "JoemoeAT" + +--- + +Thanks to our well-organized, kind, and open community, Apache Flink continues +[to grow](https://www.apache.org/foundation/docs/FY2021AnnualReport.pdf) as a +technology. We are and remain one of the most active projects in +the Apache community. With release 1.15, we are proud to announce a number of +exciting changes. + +One of the main concepts that makes Apache Flink stand out is the unification of +batch (aka bounded data) and streaming (aka unbounded data) processing. A lot of +effort went into this in the last releases but we are only getting started there. +Apache Flink is not only growing when it comes to contributions and users, it is +also growing out of the original use cases and personas. Like the whole industry, +it is moving more towards business/analytics use cases that are implemented as +low-/no-code. The feature that represents the most within the Flink space is +Flink SQL. That’s why its popularity continues to grow. + +Apache Flink is considered an essential building block in data architectures. It +is included with other technologies to drive all sorts of use cases. New ideas pop +up, existing technologies establish themselves as standards for solving some aspects +of a problem. In order to be successful, it is important that the experience of +integrating with Apache Flink is as seamless and easy as possible. + +In the 1.15 release the Apache Flink community made significant progress across all +these areas. Still those are not the only things that made it into 1.15. The +contributors improved the experience of operating Apache Flink by making it much +easier and more transparent to handle checkpoints and savepoints and their ownership, +making auto scaling more seamless and complete, by removing side effects of use cases +in which different data sources produce varying amounts of data, and - finally - the +ability to upgrade SQL jobs without losing the state. By continuing on supporting +checkpoints after tasks finished and adding window table valued functions in batch +mode, the experience of unified stream and batch processing was once more improved +making hybrid use cases way easier. In the SQL space, not only the first step in +version upgrades have been added but also JSON functions to make it easier to import +and export structured data in SQL. Both will allow users to better rely on Flink SQL +for production use cases in the long term. To establish Apache Flink as part of the +data processing ecosystem we improved the cloud interoperability and added more sink +connectors and formats. And yes we enabled a Scala-free runtime +([the hype is real](https://flink.apache.org/2022/02/22/scala-free.html)). + + +## Operating Apache Flink with joy + +Even jobs that have been built and tuned by the best engineering teams still need to +be operated. Looking at the lifecycle of Flink based projects most of them are built +to stay, putting long-term burdens on the people operating them. The many deployment +patterns, APIs, tuneable configs, and use cases covered by Apache Flink come at the +high cost of support. Review Comment: ```suggestion ## Operating Apache Flink with ease Even Flink jobs that have been built and tuned by the best engineering teams still need to be operated, usually on a long-term basis. The many deployment patterns, APIs, tuneable configs, and use cases covered by Apache Flink mean that operation support is vital and can be burdensome. In this release, we listened to user feedback and now operating Flink is made much easier. It is now more transparent in terms of handling checkpoints and savepoints and their ownership, which makes auto-scaling more seamless and complete (by removing side effects of use cases where different data sources produce varying amounts of data) and enables the ability to upgrade SQL jobs without losing the state. ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@flink.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
