July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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自动化和规模化
｜
327
14.8.4
日志和监控服务
如果一个脚本满足不了需求，或者你想将自动化程序合并到一个大型的组织框架中，你可
能需要研究将日志和监控作为一个服务的技术。有很多公司通过创建工具和系统来跟踪日
志，致力于让数据分析师和开发者的生活更简单。这些工具通常有非常简单的
Python
库，
将日志和监控信息发送到它们的平台。
通过日志服务，你可以将更多的时间投入到研究和脚本中，将更少的时间用
于管理监控和日志。这可以将“我们的脚本在不在工作，工作得怎么样？”
这样的问题留给团队中的非开发者，因为其中的很多服务都有很棒的报表和
内置的警报。
根据自动化程序的大小和布局，你可能同时需要系统监控以及脚本与错误监控。在这一节
中，我们会查看几种能够同时完成这两件事的服务，同时也会查看一些更加专业的服务。
即使你的自动化程序的规模没有大到需要它们，但是了解什么是可用的总是好的。
1.
日志和异常
基于
Python
的日志服务提供了打印日志到一个中心服务的能力，同时让你的脚本在一系列
不同的机器上运行，无论是本地机器还是远程机器。
一个有着很棒的
Python
支持的类似服务是
Sentry
（
https://getsentry.com/welcome/
）。 只
需要每月支付很少的费用，你就可以访问错误的报告板，收到基于异常临界值发送的警
报，监控基于日、周、月的错误和异常类型。
Sentry
的
Python
客户端（
https://github.com/
getsentry/raven-python
）非常容易安装、配置和使用
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