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> At scale, digital becomes analog. — Michael Feathers > Fortunately, as discrete pieces aggregate, they start to look continuous. At scale, we can take advant
by floatrock 7y ago
> At scale, digital becomes analog. — Michael Feathers
> Fortunately, as discrete pieces aggregate, they start to look continuous. At scale, we can take advantage of this, embracing higher-level metrics that can be treated as continuous signals.
He's saying that rather than look at request-level metrics in your Big Data Message Queue (request ID, etc.), your message queue in aggregate can be seen as an industrial process, and there's an entire discipline built around managing those called Statistical Process Control.
Seen in this way, the next NewRelic is going to be all about creating p- and c-charts [1] where the limits algorithmically turn knobs on your kubernetes cluster.
In other words, the next big dev-ops trend is going to be the return of Six Sigma.
[1] https://en.wikipedia.org/wiki/Control_chart https://en.wikipedia.org/wiki/Control_chart
- jacques_chester 7y agoI've occasionally pitched SPC as a source of concepts to mine, starting with XmR charts and the Nelson or Western Electric rules. I think that actually, observability has always been one of the weakest areas in tech -- we reinvent a lot of it ourselves while the wider world abounds in rich concepts of measurement, inference and control. SPC, control theory, inference engines, classifier systems, measurement theory, design of experiments, fuzzy sets, Dempster-Shafer theory, avionics, system dynamics, cybernetics and frankly a bajillion other disciplines that I've never heard of and never will. Sometimes in R&D, the R is much more profitable than the D. Which point, I should say, the article makes reasonably well.