The more I work with performance, the less I like generated queries (incl. ORM-driven generators).

Like this other team came to me complaining that some query takes >3minutes to execute (an OLTP qry) and the HTTP timeout is 60 seconds, so.... there's a problem.

Sure, a simple explain analyze suggests that some UIDPK index is queried repeatedly for ~1M times (the qry plan was generated for 300k expected invocations), each Index Scan lasts for 0.15ms. So there you go.. Ofc I'd really like to see more decimal zeroes, rather than just 0.15, but still..

Rewriting the query with a CTE cut down the execution time to pathetic 0.04sec (40ms) w/o any loops in the plan.

I suggest that change to the team and I am responded a big fat NO - they cannot make any query changes since they don't have any control on their queries

....
*sigh*
....
*sigh*
but down to 0.04sec from 3+ minutes....
*sigh*

alright, let's try to VACUUM ANALYZE, although I doubt this will be of any help. IDK what I'll do if that doesn't change the execution plan :/ Prolly suggest finding a DBA (which they won't, as the client has no € for a DBA).

All this because developers, the very people sho should have COMPLETE control over the product's code, have no control over the SQLs.

This sucks!

!rant

Ever find something that's just faster than something else, but when you try to break it down and analyze it, you can't find out why?

PyPy.
I decided I'd test it with a typical discord bot-style workload (decoding a JSON theoretically from an API, checking if it contains stuff, format and then returning it). It was... 1.73x the speed of python.

(Though, granted, this code is more network dependent than anything else.)

Mean +- std dev: [kitsu-python] 62.4 us +- 2.7 us -> [kitsu-pypy] 36.1 us +- 9.2 us: 1.73x faster (-42%)

Me: Whoa, how?!
So, I proceed to write microbenches for every step. Except the JSON decoding, (1.7x faster was at least twice as slow (in one case, one hundred times slower) when tested individually.
The combination of them was faster. Huh.

By this point, I was all "sign me up!", but... asyncpg (the only sane PostgreSQL driver for python IMO, using prepared statements by default and such) has some of it's functionality written in C, for performance reasons. Not Cython, actual C that links to CPython. That means no PyPy support.

Okay then.

Which is the best book or resource to study analysis of algorithms ??need help guys 🙄

The dev tools of browsers

I was reviewing an Angular (remember this) project where I work to find any possibilities to optimize the performance of app. For a moment an idea came to me to look and analyze package.json and see if there is any package listed there but it's not being used in the application.

...aaaandd there were fucking 32 unused packages. 32 packages that have been installed but are not used anywhere in the application. 32!!!!!

And you know what the best part is. 2 of them were react packages. I mean, literally, their name was react-bllabllablla- component, and when I visited npmjs website, their description was react component that does bllabllablla. It's fucking react....... It's in the name, it's in the description. Is my company giving jobs to fucking blind developers or what? I'm going crazy!