安全是产品的底座，是体验的基础，也是企业的一项核心竞争力。安全生产是一项系统性的工作，同时也是一件比较琐碎的事，需要做方方面面的考虑尽一切可能保障系统安全稳定运行。个人之前一直负责商品的稳定性工作，在这方面有比较多的经历和实践。
Part one of this tutorial will teach you how to build and run a custom source connector to be used with Table API and SQL, two high-level abstractions in Flink. The tutorial comes with a bundled docker-compose setup that lets you easily run the connector. You can then try it out with Flink’s SQL client.
In part two of this blog post, we will give you insight into some core design considerations and implementation details of the sort-based blocking shuffle in Flink and list several ideas for future improvement.
Part one of this blog post will explain the motivation behind introducing sort-based blocking shuffle, present benchmark results, and provide guidelines on how to use this new feature.
Part one of this blog post briefly introduced the optimizations we’ve made to improve the performance of the scheduler; compared to Flink 1.12, the time cost and memory usage of scheduling large-scale jobs in Flink 1.14 is significantly reduced. In part two, we will elaborate on the details of these optimizations.
When scheduling large-scale jobs in Flink 1.12, a lot of time is required to initialize jobs and deploy tasks. The scheduler also requires a large amount of heap memory in order to store the execution topology and host temporary deployment descriptors. For example, for a job with a topology that contains two vertices connected with an all-to-all edge and a parallelism of 10k (which means there are 10k source tasks and 10k sink tasks and every source task is connected to all sink tasks)
Flink has become a well established data streaming engine and a mature project requires some shifting of priorities from thinking purely about new features towards improving stability and operational simplicity. In the last couple of releases, the Flink community has tried to address some known friction points, which includes improvements to the snapshotting process. Snapshotting takes a global, consistent image of the state of a Flink job and is integral to fault-tolerance and exacty-once proce
PyFlink was introduced in Flink 1.9 which purpose is to bring the power of Flink to Python users and allow Python users to develop Flink jobs in Python language. The functionality becomes more and more mature through the development in the past releases.
Flink sinks share a lot of similar behavior. Most sinks batch records according to user-defined buffering hints, sign requests, write them to the destination, retry unsuccessful or throttled requests, and participate in checkpointing. This is why for Flink 1.15 we have decided to create the AsyncSinkBase (FLIP-171), an abstract sink with a number of common functionalities extracted.
