July 2023
Beginner to intermediate
270 pages
4h 48m
Chinese
Content preview from 可观测性工程
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通过事件分析实现可观测性
|
85
让计算机做它们最擅长的事情：翻阅大量数据以识别可能有趣的模式。让人类做他们最
擅长的事情：将认知性的上下文添加到那些潜在有趣的模式中，这些模式可以可靠地从
不重要的噪声中筛选出重要的信号。
可以这样想：任何计算机都可以处理数字并检测到一个峰值，但只有人类才能为这个峰
值附加意义。是好还是坏？有意还是无意？借助当今的技术，
AIOps
无法可靠地为你分
配这些价值判断。
仅靠人类无法解决当今最复杂的软件性能问题。但是计算机或供应商也不能将
AIOps
吹
捧为“神奇的银弹”。以互相结合的方式利用人类和机器的优势，是当今世界最好、最
实用的解决方案。自动化核心分析循环是关于如何做到这一点的一个主要例子。
8.4 结论
收集正确的遥测数据只是实现可观测性之旅的第一步。必须根据第一性原理分析这些数
据，以便客观、正确地识别复杂环境中的应用程序问题。核心分析循环是一种快速故障
定位的有效技术。然而，随着系统变得越来越复杂，人类有条不紊地进行这项工作可能
会很耗时。
在寻找异常来源时，你可以利用计算资源快速筛选非常大的数据集以识别有趣的模式。
将这些模式呈现给人类操作员，他们可以将其置于必要的环境中，然后进一步指导调
查，达到有效平衡，最好地利用机器和人类的优势来快速推动系统调试。可观测性系统
旨在将这种类型的分析模式应用于你在前几章才学会如何收集的事件数据。
既然你了解了所需的数据类型以及分析该数据以通过可观测性获得快速答案的实践，那
么下一章将回过头来看看可观测性实践和监控实践如何共存。
可观测性工程 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.