July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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318
｜
第
14
章
及何时重试，等等。
• worker
worker
是通过
Celery
控制的
Python
进程，执行
Python
代码。它们知道你让它们去执行
什么任务，并且尝试执行这些
Python
代码到结束。
•
监控工具［例如
Flower
（
http://flower.readthedocs.org/en/latest/
）］
允许查看
worker
和队列，对于回答类似于“昨晚什么失败了”这样的问题很有用。
Celery
有一个很有用的上手指南（
http://docs.celeryproject.org/en/latest/getting-started/first-
steps-with-celery.html
），但是我们发现最大的问题并不是学习如何使用
Celery
，而是了解什
么类型的任务适合于队列、什么类型不适合于队列。表
14-2
综述了一些有关基于队列自动
化的问题和原则。
表14-2：队列还是非队列？
基于队列的任务管理需求 不需要队列的自动化需求
任务没有一个确定的截止日期 任务可以并且的确有截止日期
不需要知道有多少任务 可以轻松地量化需要完成什么任务
只是大体上知道任务的优先级 清楚地知道任务的优先级
任务不需要总按顺序发生，或者通常不是有顺序的 任务必需按顺序发生
任务有时花费很长的时间，有时花费很短的时间 需要知道任务花费多长时间
任务调用（或排队）基于一个事件或其他的任务结束 任务基于时间或其他可预测的事情
任务失败是可以的；可以重试 必须了解每一次任务的失败
有大量的任务，而且很有可能继续增长
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