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Comment by vlovich123 | Hacker News Reader

> blazing fast, shall we say, to make me believe anyone even wanted to pay attention to tiny performance regressions

Important to distinguish frontend vs backend performance of course. This is about backend performance where they care about this stuff a lot because it multiplies at scale & starts costing them real money. Frontend performance has less of a direct impact on their numbers with the only data I know on that is the oft-cited Google stuff trying to claim that there's a direct correlation between lost revenue and latency (which I haven't seen anyone else bother to try to replicate & see if it holds up).

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> Important to distinguish frontend vs backend performance of course.

I'm not sure what you're calling frontend in this context (client side? or client-facing "front-end" servers?), but I'm talking about their server side specifically. I've looked at their API calls and understand when slowness is coming from the client or the server. The client side is even slower for sure, but the server side also never felt like it was optimized to the point where deviations this small mattered.

I think the confusion arises because of the difference between optimization and control, which are superficially similar.

Having control lets you see if things changed. Optimization is changing things.

This team seems to be focused on control. I assume optimization is left to the service teams.

I think by control you mean observability?

I get that they're different, but the whole point is optimization here. They're not gathering performance metrics just to hang them up on a wall and marvel at the number of decimal points, right? They presumably invested all the effort into this infrastructure because they think this much precision has significant ROI on the optimization side.

I'm using "control" in the statistical process control sense, where it means "we can tell if variation is ordinary or extraordinary".

To me it seemed clear that the paper is about detecting regressions, which is control under my definition above. I still think of that as distinct from optimization.

Right, but what is the point of optimizing?

It often isn't to make things go faster for an individual user (often times the driving factor of latency is not computation, but inter-system rpc latency, etc.). The value is to bin-pack processing more requests into the same bucket of CPU.

That can have latency wins, but it may not in a lot of contexts.

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