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Comment by simias | Hacker News Reader

I suspect that the amount of people and especially companies willing to spend time and money optimizing Python are fairly low.

Think about it: if you have some Python application that's having performance issues you can either dig into a foreign codebase to see if you can find something to optimize (with no guarantee of result) and if you do get something done you'll have to get the patch upstream. And all that "only" for a 25% speedup.

Or you could rewrite your application in part or in full in Go, Rust, C++ or some other faster language to get a (probably) vastly bigger speedup without having to deal with third parties.

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> you can either dig into a foreign codebase ... > Or you could rewrite your application

Programmers love to save an hour in the library by spending a week in the lab

Everyone likes to shirk away from their jobs; engineers and programmers have ways of making their fun (I'm teaching myself how to write parsers by giving each project a DSL) look like work. Lingerie designers or eyebrow barbers have nothing of the sort, they just blow off work on TikTok or something.

TikTok’s got some fun coding content, if you can get the algorithm to surface it to you.

> Or you could rewrite your application in part or in full in Go, Rust, C++

Or you can just throw more hardware at it, or use existing native libraries like NumPy. I don't think there are a ton of real-world use cases where Python's mediocre performance is a genuine deal-breaker.

If Python is even on the table, it's probably good enough.

You are right, that's usually not how it works.

Instead, there are big companies, who are running let's say the majority of their workloads in python. It's working well, it doesn't need to be very performant, but together all of the workloads are representing a considerable portion of your compute spend.

At a certain scale it makes sense to employ experts who can for example optimize Python itself, or the Linux kernel, or your DBMS. Not because you need the performance improvement for any specific workload, but to shave off 2% of your total compute spend.

This isn't applicable to small or medium companies usually, but it can work out for bigger ones.

There was some guarantee of result. It has been a long process but there was mostly one person who had identified a number of ways to make it faster but wanted financing to actually do the job. Seems Microsoft is doing the financing, but this has been going on for quite a while.

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