及时安排火花作业

本文介绍了及时安排火花作业的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

推荐使用哪种工具来每天/每周调度Spark作业. 1)Oozie 2)路易吉 3)阿兹卡班 4)计时 5)气流

Which is the recommended tool for scheduling Spark Jobs on a daily/weekly basis. 1) Oozie 2) Luigi 3) Azkaban 4) Chronos 5) Airflow

谢谢.

推荐答案

从此处更新我以前的答案:建议使用调度工具来构建基于hadoop的数据管道

Updating my previous answer from here: Suggestion for scheduling tool(s) for building hadoop based data pipelines

• 气流:先尝试一下.体面的用户界面，类似于Python的作业定义，对于非程序员来说半可访问，依赖声明语法很奇怪.
  • Airflow内置了对事实的支持，即计划的作业通常需要重新运行和/或回填.确保您建立了支持此功能的管道.
  • Airflow: Try this first. Decent UI, Python-ish job definition, semi-accessible for non-programmers, dependency declaration syntax is weird.
    • Airflow has built in support for the fact that jobs scheduled jobs often need to be rerun and/or backfilled. Make sure you build your pipelines to support this.
    • Azkaban要求简单(不能使用不存在的功能)，而其他则巧妙地鼓励复杂性.
    • 签出Azkaban CLI项目以编程方式创建作业. github/mtth/azkaban (示例 github/joeharris76/azkaban_examples )
    • Azkaban enforces simplicity (can’t use features that don’t exist) and the others subtly encourage complexity.
    • Check out the Azkaban CLI project for programmatic job creation. github/mtth/azkaban (examples github/joeharris76/azkaban_examples)

    哲学:

    简单的管道比复杂的管道要好:易于创建，易于理解(尤其是在未创建时)，并且易于调试/修复.

    Simpler pipelines are better than complex pipelines: Easier to create, easier to understand (especially when you didn’t create) and easier to debug/fix.

    当需要复杂的操作时，您希望以完全成功或完全失败的方式封装它们.

    When complex actions are needed you want to encapsulate them in a way that either completely succeeds or completely fails.

    如果您可以使其幂等(再次运行它可以产生相同的结果)，那就更好了.

    If you can make it idempotent (running it again creates identical results) then that’s even better.