This is a self-fulfilling prophecy, as the GIL makes Python's (and Ruby's) concurrency story pretty rough compared to nearly all other widely used languages: C, C++, Java, Go, Rust, and even Javascript (as of late).
As a python dev, pythons multiprocess/multithreading story is one the largest pain points in the language.
Single threaded performance is not that useful while processors have been growing sideways for 10 years.
I often look at elixir with jealousy.
Your anecdote adds little.
Instead of breaking decades of code, maybe use a language like Go or Rust for performance instead.
Turns out they just want a better Python.
That's Go. It gives actual types[1] and structs (so you dont have to wonder about dict, class, class with slots, dataclasses, pydantic, attrs, cattrs, marshmallow, etc). It removes exceptions and monkeypatching. It's async-first (a bit like gevent). It's inherently multicore. And you can package and distribute it without marking a pentagram on the ground and sacrificing an intern to Apep.
You just need to stop being friends with C FFI. Which is fine for web and sysadmin tools. For data science and ML/AI/DL, it's less ok.
[1] And the types are actually checked! I think a lot of people aren't really using python type checking given how slow it is and no one seems to complain. Or maybe everyone is using pyright and pyre and are satisfied with how slow these are.
Going to JS or even TS for performance would be saner, and it has a same-ish object model even.
It's also commonly used language for numerical work in general. Most of the time numpy is enough and then occasionally you'll need something not already implemented and then have to do your own bindings.
Multithreading is not really the reason why things get written in cython etc., you can easily see 100x improvements in single threaded performance (compared to maybe a factor of 2-8x for multithreading). If you care about performance you'd definitely write the performance critical stuff in cython/pythran/c.
The bindings available in Python can also be used from other languages.
Pin your version.
Either include like almost every language from JS, Java, C# to Haskell, or just list C++ and Rust. But Go is in the former category.
I say bring it.
Hmm, how is that so?
As a python dev as well, I don't have much complaint with multiprocessing.
The API is simple, it works OK, the overall paradigm is simple to grok, you can share transparently with pickle, etc.
It takes more careful planning than async in JS?, say, or goroutines.
The ProcessPoolExecutor is nice but shouldn't be necessary.
I'd really appreciate if python included concurrency or parallelism capabilities that didn't disappoint and frustrate me.
If you've tried using the thread module, multiprocessing module, or async function coloring feature, you probably can relate. They can sort of work but are about as appealing as being probed internally in a medical setting.
Occasionally, a technology breaks out of its intended domain. Python is one of these - it plays host to lots of webservers, and even a filesystem (dropbox). Similarly, HTML is a text markup language, but that doesn't stop people from making video games using it.
The developers of the technology now have to make a philosophical decision about what that technology is. They can decide to re-optimize for the current use cases and risk losing the heart of their technology as tradeoffs get made, or they can decide to keep the old vision. All of the design choices flow down from the vision.
They have decided that Python is a scripting language. Personally, I agree (https://specbranch.com/posts/python-and-asm/). In turn, the people using the language for something other than its intended purpose have choices to make - including whether to abandon the technology.
If instead Python moves toward competing with languages like go, it is going to need to make a lot of different tradeoffs. Ditching the GIL comes with tradeoffs of making Python slower for those who just want to write scripts. Adding more rigorous scoping would make it easier to have confidence in a production server, but harder to tinker. Everything comes with tradeoffs, and decisions on those tradeoffs come from the values of the development team.
Right now, they value scripting.
Pandas, numpy, scipy, tensorflow. All of these go way beyond what is possible with a scripting language.
Since when is the runtime performance of a script a serious concern? Why is it a problem if this aspect gets slightly slower if it brings real concurrency support?
All of the libraries you have cited are from the ML and stats communities, and they are not core language features. From what I understand, ML folks like Python because it is fast to play with and get results. In other words, they like it because it is a scripting language.
Personally, I like that Python has kept the GIL so far because I would never run a 24/7 server in Python and I am happy to use it very frequently for single-threaded scripting tasks.
Edit: I didn't decide that Python was a scripting language. The Python maintainers did. The point is that the identity of a project doesn't flow down from its use cases.
Edit 2: I should have said "the identity of a project doesn't flow down from its users."
No, it is not. It's what you can do with a scripting language, which is a fairly straightforward functional categorization. If you are very familiar with it and really want to just ignore the performance issues and don't mind strange workarounds (eg elaborate libraries like Twisted), you probably use python.
> The point is that the identity of a project doesn't flow down from its use cases.
Use cases are what determines the identity of a language. It takes a massive and ongoing marketing campaign to convince people otherwise, with limited success without use-cases. Python is popular because it's a scripting language with a relatively low learning curve (one true way to do things philosophy). That's it. It will improve over time, but it's been slow going...and that's in comparison to the new Java features!
Haskell is a shining example of how the language identity is foisted upon the public until you are convinced enough to try it. It doesn't take long to learn it's a nightmare of incomplete features and overloaded idioms for common tasks, so it isn't used. There aren't good use-cases for a language with haskell's problems, so the developer community and industry-at-large avoids it.
Just as a side-note - my prior gig used Python on both the Server and Data Collection industrial systems. It was very much a 24x7x365 must-never-go-down type of industrial application, and, particularly when we had a lot of data-sources, was very multi-processing. Was not unusual to see 32 processes working together (we used sqlite and kafka as our handoff and output of processes) running on our data collection appliances.
Our core data modelling engine would routinely spin up 500 worker pods to complete the work needed to be done in 1/500th of the time, but we would still see some of the long term historian runs take upwards of a week to complete (many hundreds of machines for multiple years with thousands of tags coming in every 5 seconds is just a lot of data to model).
I say this mostly to demonstrate that people and companies do use python in both large-processing intensive environments as well as industrial-must-never-stop-24x7 mission critical appliances.
I don't ever recall any of our engineers looking at Python as "a scripting language" - it was no different to them than Java, C#, Rust, Go, C++ or any other language.
You just have to isolate the complicated parts, define sensible interfaces for them, and make sure they are followed with type hints and a good type checker.
I don't think anybody expects a recent graduate from computing science, with maybe 4 or 5 classes that used python under their belt (and maybe a co-op or two) to be writing robust code (perhaps in any language).
But, after working with Sr. Engineers who do so, and understanding how to catch your exceptions and respond appropriately, how to fail (and restart) various modules in the face of unexpected issues (memory, disk failures, etc...) - then a python system is just as robust as any other language. I speak from the experience of running them in factories all over the planet and never once (outside of power outages - and even there it was just downtime, not data loss) in 2+ years seeing a single system go down or in any way lose or distort data. And if you want more performance? Make good use of numpy/pandas and throw more cores/processes at the problem.
Just being aware of every exception you can throw (and catching it) and making robust use of type hinting takes you a long way.
Also - and this may be more appropriate to Python than other languages that are a bit more stable, an insane amount of unit and regression testing helps defend quite a bit from underlying libraries like pandas changing the rules from under you. The test code on these project always seemed to outweigh the actual code by 3-5x. "Every line of code is a liability, Every test is a golden asset." was kind of the mantra.
I think that what makes python different from other languages, is that it doesn't enforce guardrails/type checking/etc... As a result, it makes it trivial for anyone who isn't an engineers to start blasting out code that does useful stuff. But, because those guardrails aren't enforced in the language, it's the responsibility of the engineer to add them in to ensure robustness of the developed system.
That's the tradeoff.
Now the fact that you can do some fun metaprogramming shenanigans in Python just speaks to how nice it is to write.
It strikes me as a bit of a waste of resources to keep stapling engineering effort into the Python ML/data ecosystem when it’s basically a crippled language capable of either: mindlessly driving C binaries, or scripting simple tasks.
What other performance, feature and technique advancements are we leaving on the table because the only “viable” ecosystem is built on a fundamentally crippled language?
Also, I hate the idea of re-building all the code when the program starts. Python's JIT can at least ignore the performance-critical code that's written in C++.
The ergonomics are such that it's not difficult to use.
Why can't or shouldn't we have a mechanism comparably fantastic and easy to use in Python?
It is architecturally comparable to async Javascript programming, which imho is a shoehorn solution.
Personally, GIL is more surprising to me when interop with Python.
Sure, but the easy cases are easy (in particular, you can usually do nothing and it will be correct but slow, which is much better than fast but incorrect) and the hard cases are possible.
> Personally, GIL is more surprising to me when interop with Python.
Any GCed language, including Go, will oblige you to integrate with its APIs if you want to handle complex cases correctly.