https://docs.google.com/document/d/1Lr9UYXEz6s6R_3PWg3bZQLF3upGaNEkc0rQCFSzaYDI/edit   // create the original stream DataStream<String> stream = ...; // apply the async I/O transformation DataStream<Tuple2<String, String>> resultStream =…

1.前言 本文是基于Flink官网上Asynchronous  I/O的介绍结合自己的理解写成的,若有不正确的欢迎大伙留言交流,谢谢! 2.Asynchronous  I/O简介 将Flink用于流计算时,若涉及到和外部系统进行交互,如利用Flink从数据库中读取数据,这种需要获取I/O的场景时,我们需要考虑交互所带来的时延问题. 为分析如何减少时延,我们先来分析一下,Flink以同步的形式方法外部系统(以MapFunction中和数据库交互为例)的过程,若图1虚线左侧所示,请求a发送到data…

如果要考虑易用性和效率,使用rocksDB来替代普通内存的kv是有必要的 有了rocksdb,可以range查询,可以支持columnfamily,可以各种压缩 但是rocksdb本身是一个库,是跑在RocksDBStateBackend中的 所以taskmanager挂掉后,数据还是没了, 所以RocksDBStateBackend仍然需要类似HDFS这样的分布式存储来存储snapshot   kv state需要由rockdb来管理,这是和内存或file backend最大的不同 Abstr…

Flink在流上最大的特点,就是引入全局snapshot,   CheckpointCoordinator 做snapshot的核心组件为, CheckpointCoordinator /** * The checkpoint coordinator coordinates the distributed snapshots of operators and state. * It triggers the checkpoint by sending the messages to the re…

https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals FLIP-1 : Fine Grained Recovery from Task Failures When a task fails during execution, Flink currently resets the entire execution graph and triggers complete re-execution f…

https://cwiki.apache.org/confluence/display/FLINK/Flink+Internals   Memory Management (Batch API) Introduction Memory management in Flink serves the purpose to control how much memory certain runtime operations use. The memory management is used for…

数据流容错机制 该文档翻译自Data Streaming Fault Tolerance,文档描述flink在流式数据流图上的容错机制. ------------------------------------------------------------------------------------------------- 一.介绍 flink提供了可以一致地恢复数据流应用的状态的容错机制,该机制保证即使在错误发生后,反射回数据流记录的程序的状态操作最终仅执行一次.值得注意的是,该保证可…

Flink 的分布式执行过程包含两个重要的角色,master 和 worker,参与 Flink 程序执行的有多个进程,包括 Job Manager,Task Manager 以及 Job Client,下图展示了 Flink 程序的执行过程. Flink 程序首先被提交到 Job Client 上,随后 Job Client 将它提交到 Job Manager 上,Job Manager 负责安排资源的分配和 job 的执行.首先是资源的分配,然后是将 job 划分为若干 task 后提交到对…

This is a guest post from Xiaowei Jiang, Senior Director of Alibaba’s search infrastructure team. The post is adapted from Alibaba’s presentation at Flink Forward 2016, and you can see the original talk from the conference here. Alibaba is the larges…

Motivation I/O access, for the most case, is a time-consuming process, making the TPS for single operator much lower than in-memory computing, particularly for streaming job, when low latency is a big concern for users. Starting multiple threads may…