It seems to me there needs to be a better rationale as to the justification of free threading in Python - why would we want that, what use cases are we looking to enable exactly? Is it worth (potentially) upending the ecosystem for these use cases?
Alas I propose the mere fact that it can be done is not a good enough reason.
In my 12+y of practical experience in using Python (previously 18y+ with Java & other languages) for web, full stack and data science use cases within an enterprise context, I have never found any problem with the GIL. For CPU bound parallelism multiprocessing works fine, if more parallel processing power is needed, multi-node is the way to go; for latency bound threading is usually a good option, although multiprocessing works in this case too.
One argument that is often brought up in my discussions with fellow devs is along the lines of “I don’t want to choose between threading or multiprocessing, I just want the code to do what it does already in parallel & faster”. While it seems sensible to want that, of course it is not that easy. With this expectation, the GIL most of the time becomes a point of frustration only because the first thing that gets tried is multi threading. It seems natural to do so: threads are among the top ideas that come up when looking up ways to make computers do things concurrently. Next thing you know, there is a complaint about the GIL limitting performance, when in fact the root cause is the wrong choice of the concurrency model.
I suggest that we should raise awaress of the best practices to parallel and concurrent task execution. We should also highlight that the general concensus (afaik) is that shared-nothing approaches work best and that you should avoid shared-object concurrency if you can. In this view having the GIL is a blessing in disguise.
I’m sorry but this is incredibly ignorant and dismisses not just the use cases that have been vocalized for over a decade by individuals from multiple domains but also implies that organizations are willing to subsidize this effort with millions of dollars for some theoretical benefits that have not been considered.
This PEP poses new challenges for distributing Python. At least for some time, there will be two versions of Python requiring separately compiled C-API extensions. It may take some time for C-API extension authors to build --disable-gil compatible packages and upload them to PyPI. Additionally, some authors may be hesitant to support the --disable-gil mode until it has wide adoption, but adoption will likely depend on the availability of Python’s rich set of extensions.
To mitigate this, the author will work with Anaconda to distribute a --disable-gil version of Python together with compatible packages from conda channels. This centralizes the challenges of building extensions, and the author believes this will enable more people to use Python without the GIL sooner than they would otherwise be able to.
For extension developers, in practice this would mean that both the --disable-gil and GIL versions will need to be maintained for an extended period of time. Given the lifecycle of Linux releases such as ubuntu and debian at least 5 years. During this time there will be limitations on how extensions can be developed because of the dual compatibility. There is not really a choice to only develop for x or y. If you want your extension/prorgram to be seriously considered, it needs to run on both versions of the interpreter. I really don’t want another era where I have to write a limited subset of python, literally getting the worst of both worlds, for compatibility reasons. Python 2’s well deserved rest felt as a liberation to me and going back to a similar scenario fills me with dread.
Thanks for your feedback, I appreciate the candor. However, my question is not ignorant of the use cases and the potential benefits a no-gil version to those use cases would have.
I am merely observing, from my own background, that the “GIL issues” are often raised due to considering a program and data design that are ameanable to shared-all free threading concurrency model, whereas Python (or rather CPython) effectively prefers shared-nothing concurrency. Incidentally, the latter has been known easier to reason about, to be less prone to failures, and in many cases also to be easier to scale.
I propose that this can be seen as a benefit, not a burden and I am asking if the risks inherent in a change of this dimension is warranted, including giving up on a fundamental benefit.
Two things can be true at once. This comment is generally true, and there are also situations where shared-nothing does not work well, as pointed out in many posts on the various topics about removing the GIL.
Even if you have no use for shared memory parallelism, and only want to use the safer share-nothing approach, it is beneficial if the underlying VM can support sharing objects as it can make message passing much more efficient.
I agree that share-nothing is superior in terms of safety, but multiprocessing is an inefficient way to do it.
Erlang processes are conceptually isolated, but generally run in the same OS process (if running on the same physical machine).
The JVM supports a shared memory concurrency model, but nothing prevents you building a share-nothing application on top of it.
Yes, but do we want to? I for one deliberately left Java behind in favor of Python for all the (very often unnecessary) complexity the going powers that were and the common dogma would declare best practice. In particular Java’s multithreading and memory model is not something I miss.
Also it took the JVM years to be free of concurrency issues.
Yes, many people want to have free-threading in Python. It might be a good idea to read this thread, and the two PEP 703 discussion threads (first, second), in full (yes, all 370+ messages). Many people have shown why the existing models are not enough, and have presented real-life examples where the GIL was a bottleneck, and sometimes caused a rewrite in another language.
Free-threading might not be easy to implement at first, but it is certainly worth the effort in the long term. If PEP 703 is rejected, I would (in my personal opinion) consider it as an admission that Python is a toy language, requiring “serious” workflows to be implemented in C.
That’s a bit too strong of a position IMO. If PEP 703 is rejected, I would consider it an admission that free threading is hard, requires difficult decisions in terms of what consequences are acceptable, and will require further planning before it gets implemented.
The general tone from most people has been “this would be great, but what are the costs”. Rejecting one specific proposal for free threading doesn’t mean that nobody wants free threading.
I think this does depend on the form of rejection, though. If it came down in the same tone as some of the posts in this thread [1], I don’t imagine another Sam Gross coming along for the foreseeable future.
not that I think it would, based on the discussions I’ve read ↩︎
It’s already implemented and working, with minimal performance losses compared to previous efforts and at least one mega-org committed to subsidizing the effort for years with engineers that have expertise with the code.
If this doesn’t happen now then I would be shocked if it ever did.
That’s fair, but I agree, I don’t foresee a rejection in the form of “this is a terrible idea at any cost”. Of course, I can’t speak for the SC in any way. I just wanted to clarify the distinction between rejecting PEP 703 (one very specific proposal for free threading) and rejecting the notion of free threading altogether.
That’s probably the point of contention in this thread: can we expect “most code” to add locks in all the right places? If yes, then full-in nogil is fine. If no, nogil is a bad option. I am with the no camp because in my experience people want performance but value safety. That’s only possible in a gil world.
You are talking about code which is pure Python and is supposed to run in a threaded environment. Unless it’s properly protected with locks/queues etc, it’s going to be failing regardless of whether it’s running with free threads or not. Either the (Python) code is threadsafe and then you can run it with threads (GILed CPython, pypy, ironpython or CPython with free threads) or it’s not threadsafe and then just don’t use threads or sooner or later it will fail.
people want performance but value safety. That’s only possible in a gil world.
Other programming languages don’t have GIL and I wouldn’t call them unsafe (because of that). Python world isn’t “special” in a sense that it “can’t handle” free threads. It can. It will take a while to get used to it and there will be bugs, but those that happen frequently will be squashed quickly. If you are running a heart rate monitor or something, stay with 3.11 until you feel it’s safe enough for you to upgrade.
I’m not sure you understand what Brett wrote. The requirements for thread safety are no different in the GIL and no-GIL scenarios. I believe the original reason for including the GIL was to simplify Python’s C internals. It also simplifies extension module implementations. It has no effect on the correctness of Python code.
Thanks, that is a relief. But from your answer I take it will be an issue for C-extension code? If all objects need to be made actively thread-safe, that is going to take some work.
That is not necessarily true. Threaded code in nogil will likely have side effects that need additional explicit locking that are not needed with the GIL. This means the same Python code running just fine, implicated by the GIL, may fail in nogil for reasons that are not obvious.
This describes the Python-2-3 migration - which took a whole decade - and nobody wants to go back to that. So let’s not do that please.
I get that many people want free threading, yet isn’t it also true that many times more people do not need it, and might suffer from having to live through the process to get there, or because they rely on the implicit guarantees given by the GIL?
Btw it’s not that I wouldn’t want free threading in Python, I just happen to think that the risks to Python as a language, and to the ecosystem while getting there are not worth taking. I would much rather see efforts spent on making use of nogil extensions (e.g. using Cython) both more known and less cumbersome.