Oh wow, a programming language that is not supposed to be used for every single software in the world. Unlike Javascript for example which should absolutely be used for making everything (horrible). Nodejs was a mistake.
this post was submitted on 26 Mar 2025
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Nodejs was a mistake.
More choice is always better
And some of those choices are mistakes.
I like Typescript >:3
I appreciate Typescript for addressing the sins of its predecessor.
Citations Needed: Episode 95: The Hollow Vanity of Libertarian "Choice" Rhetoric
Episode webpage: https://dts.podtrac.com/redirect.mp3/traffic.libsyn.com/secure/citationsneeded/CN95_20191205_choice_Stites_v2.mp3
Fucking Citations Needed, every time I finish an episode, someone comment something related to it.
Oooooh this is really cool, thanks for sharing. How could I install it on Linux (Ubuntu)? I assume I would have to compile CPython. Also, would the source of the programs I run need any modifications?
In this case, it's a feature of the language that enables developers to implement greater amounts of parallelism. So, the developers of the Python-based application will need to refactor to take advantage of it.
From memory I can only answer one of those: The way I understand it (and I could be wrong), your programs theoretically should only need modifications if they have a concurrency related bug. The global interlock is designed to take a sledgehammer at "fixing" a concurrency data race. If you have a bug that the GIL fixed, you'll need to solve that data race using a different control structure once free threading is enabled.
I know it's kind of a vague answer, but every program that supports true concurrency will do it slightly differently. Your average script with just a few libraries may not benefit, unless a library itself uses threads. Some libraries that use native compiled components may already be able to utilize the full power of you computer even on standard Python builds because threads spawned directly in the native code are less beholden to the GIL (depending on how often they'd need to communicate with native python code)
Thanks for the answer, I really hope Synapse will be able to work with concurrency enabled.
let's be honest here, he actually means 0.01 core performance
don't worry it'll use all the RAM anyway
I paid for all the memory. I'll use all the memory.
JG Memoryworth
No RAM gets wasted!
I tough this was about excel and was like yeah haha!
But is about Python, so I'm officially offended.
It only took us how many years?
all programs are single threaded unless otherwise specified.
I initially read this as “all programmers are single-threaded” and thought to myself, “yeah, that tracks”
It’s safe to assume that any non-trivial program written in Go is multithreaded
And yet: You’ll still be limited to two simultaneous calls to your REST API because the default HTTP client was built in the dumbest way possible.
Really? Huh, TIL. I guess I've just never run into a situation where that was the bottleneck.
I absolutely love how easy multi threading and communication between threads is made in Go. Easily one of the biggest selling points.
Key point: they're not threads, at least not in the traditional sense. That makes a huge difference under the hood.
Well, they're userspace threads. That's still concurrency just like kernel threads.
Also, it still uses kernel threads, just not for every single goroutine.
What I mean is, from the perspective of performance they are very different. In a language like C where (p)threads are kernel threads, creating a new thread is only marginally less expensive than creating a new process (in Linux, not sure about Windows). In comparison creating a new 'user thread' in Go is exceedingly cheap. Creating 10s of thousands of goroutines is feasible. Creating 10s of thousands of threads is a problem.
Also, it still uses kernel threads, just not for every single goroutine.
This touches on the other major difference. There is zero connection between the number of goroutines a program spawns and the number of kernel threads it spawns. A program using kernel threads is relying on the kernel's scheduler which adds a lot of complexity and non-determinism. But a Go program uses the same number of kernel threads (assuming the same hardware and you don't mess with GOMAXPROCS) regardless of the number of goroutines it uses, and the goroutines are cooperatively scheduled by the runtime instead of preemptively scheduled by the kernel.
Great details! I know the difference personally, but this is a really nice explanation for other readers.
About the last point though: I'm not sure Go always uses the maximum amount of kernel threads it is allowed to use. I read it spawns one on blocking syscalls, but I can't confirm that. I could imagine it would make sense for it to spawn them lazily and then keep around to lessen the overhead of creating it in case it's needed later again, but that is speculation.
Edit: I dove a bit deeper. It seems that nowadays it spawns as many kernel threads as CPU cores available plus additional ones for blocking syscalls. https://go.dev/doc/go1.5 https://docs.google.com/document/u/0/d/1At2Ls5_fhJQ59kDK2DFVhFu3g5mATSXqqV5QrxinasI/mobilebasic
But it's still not a guarantee
Definitely not a guarantee, bad devs will still write bad code (and junior devs might want to let their seniors handle concurrency).
Does Python have the ability to specify loops that should be executed in parallel, as e.g. Matlab uses parfor instead of for?
python has way too many ways to do that. asyncio, future, thread, multiprocessing...
Of the ways you listed the only one that will actually take advantage of a multi core CPU is multiprocessing
yup, that's true. most meaningful tasks are io-bound so "parallel" basically qualifies as "whatever allows multiple threads of execution to keep going". if you're doing numbercrunching in pythen without a proper library like pandas, that can parallelize your calculations, you're doing it wrong.
I’ve used multiprocessing to squeeze more performance out of numpy and scipy. But yeah, resorting to multiprocessing is a sign that you should be dropping into something like Rust or a C variant.
Most numpy array functions already utilize multiple cores, because they're optimized and written in C
I've always hated object oriented multi threading. Goroutines (green threads) are just the best way 90% of the time. If I need to control where threads go I'll write it in rust.
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Do you mean Synapse the Matrix server? In my experience, Conduit is much more efficient.
i wish they would switch the reference implementation to conduit
there is core components on the client side in rust so maybe that's the way for the future
Yep, I mean as in matrix. There is currently no was to migrate to conduit/conduwuit. Btw from what I've seen conduwuit is more full-featured.
I prefer this default. Im sick of having to rein in Numba cores or OpenBlas threads or other out of control software that immediately tries to bottleneck my stack.
CGroups (Docker/LXC) is the obvious solution, but it shouldn't have to be
Python
..so.. so you made it single threaded?
I'll be honest, this only matters when running single services that are very expensive. it's fine if your program can't be pararlelized if the OS does its job and spreads the love around the cpus