July 2023
Beginner to intermediate
270 pages
4h 48m
Chinese
Content preview from 可观测性工程
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处理和调试基于
SLO
的告警
|
129
假设你有一项服务，其
SLO
目标表明
99%
的单元将在移动的
30
天窗口内成功。在一个
典型的月份，该服务会看到
43 800
个单元。在过去的
24
小时内，该服务看到了
1440
个
单元，其中
50
个失败了。在过去的
6
小时内，该服务已看到
50
个单元，其中
25
个失败。
为了实现
99%
的目标，每月只允许
1%
的单元发生故障。基于典型的流量（
43 800
个单
元），仅允许
438
个单元发生故障（你的错误预算）。那么按照这个速度，是否会在接下
来的
24
小时内消耗掉你的错误预算？
线性推断逻辑中，你会计算出过去
6
小时内的
25
次故障意味着接下来的
24
小时内有
100
次故障，总共
105
次故障，远小于
438
次。
使用比例推断法，你会计算出在任何给定的
24
小时内，你预计会看到
43 800
个单元
/
30
天，或
1440
个单元。在过去的
6
小时里，
50
个单元中的
25
个（
50%
）失败了。如
果
50%
的比例故障率在接下来的
24
小时内继续存在，那么你将消耗掉
1440
个单元的
50%
，即
720
个单元，这远超
438
个单元的预算。通过这种比例计算就可以合理地预测，
你的错误预算将在大约半天内用完。此时应该触发一个告警，通知值班工程师立即进行
调查。
上下文感知消耗告警
使用上下文感知或历史消耗告警时，你要保留在
SLO
的整个窗口中发生的好事件和坏事
件的滚动总数，而不仅是基线窗口。本节介绍实现有效的消耗告警所需要进行的各种计
算。但是在实践这些技术时仍需谨慎行事。在每个评估间隔计算这些值的成本也可能会
很昂贵。在
Honeycomb ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.