A weekly source of software testing news
ISSUE #27 - August 7, 2017
Tester’s Digest has had half a year’s worth of weekly newsletters! Thanks for sticking with me this long, and please keep sending pointers to good materials. Today’s issue looks at observability: How to know if your service/application is doing well, through metrics and alerts based on them, and how to debug it when it’s not doing well, through logs.
Twitter’s 2013 observability stack (metric collection, storage, querying, dashboards, monitoring / alerts):
https://blog.twitter.com/2013/observability-at-twitter
On designing better alarms, so you get timely alerts on actionable events: “The trouble with alarm design is that it seems obvious. It’s not.”
When you are debugging an issue using timeline charts of several metrics and find them to align visually, are they indeed correlated?
https://www.vividcortex.com/blog/correlating-metrics
Or maybe your debugging is based on logs. If so, these things about logs are good to know.
https://honeycomb.io/blog/2017/04/lies-my-parents-told-me-about-logs/
Solving the Goldilocks problem in logging not too much, not too little, but the most interesting events.
https://honeycomb.io/blog/2017/06/build-observable-systems/
What to monitor about your app, take 1: Google’s 4 golden signals which I combined into a mnemonic LETS = Latency, Errors, Traffic, Saturation
What to monitor about your app, take 2 and 3, for good measure: RED = Request rate, Error rate, Duration of requests; USE = Utilisation, Saturation, Error metrics
https://twitter.com/i/web/status/852341680946544640
“Logs and metrics are complementary” and what each is good for, a great overview if you ignore the “observability > unit tests” intro.
https://medium.com/@cindysridharan/logs-and-metrics-6d34d3026e38?__s=b2sexxzpefs371hbo9ts
Things worth learning: Hill’s guidelines for evaluating whether a causal relationship exists, presented via xkcd comics:
http://livefreeordichotomize.com/2016/12/15/hill-for-the-data-scientist-an-xkcd-story/
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