oooohhh goody... @maplebed takes you to school in scaling production db loads, and shows you ✨exactly✨ how much headroom we were able to buy ourselves using @honeycombio. Not bad for an hour's work. https://www.honeycomb.io/blog/stop-your-database-from-hating-you-with-this-one-weird-trick/ …
key takeaway: when should you as startup developers switch from writing code to paying down some db perf debt? a good rule of thumb is, when your cpu hits 40% utilization. https://www.honeycomb.io/blog/stop-your-database-from-hating-you-with-this-one-weird-trick/ …
this shit takes me way back. i first fell in love with this tooling when i was working on db perf, using mongodb at parse.
that was... five years ago?
it changed my life. being able to sum up and break down things like lock time held, raw and normalized queries, etc ... just look at the crisp, clear handful of graphs in this post, and the easy way they tell a story.
when you can see wtf is going on, everything gets easier.
when you know what to fix, fixing is easy.
the hard part isn't fixing the problem, it's finding what problem to fix.
and if you can't even clearly inspect the problem, you're just stabbing and guessing (and praying you hit a vital organ, that doesn't belong to an ally).
when you don't have tooling that engineers can use to inspect the problem and see the impact of their code, then you're reduced to peering at the tea leaves of time series aggregates and leaning on the intuition of a specialized ops priesthood to explain mysterious behaviors.
how many times do i have to say this? ✨the way you're doing things now is the hard way.✨
how many hours of work would it take your team to do what ben just did in an hour? would you have the same level of confidence?
You can follow @mipsytipsy.
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