On what are you basing your statement that multithreading in Python is insanely inefficient? Despite the fact that the GIL prevents multiple threads from running in parallel, using multiple threads can give you a huge boost if IO is your bottleneck.
I think that it's irresponsible to make a blanket statement like this, because there are many use-cases for multiple threads in Python. Sure, one of the obvious ones (parallel processing) doesn't work, but besides that threads can be extremely useful.
I'm also unclear on what you mean by "no such thing as thread-local data" when there is `threading.local()` that does exactly that.
Lastly, I don't think multiprocessing was created as a workaround for threading per-se. Rather it was a workaround for the global interpreter lock.
And in either case my view is concurrency is best done in a language where there is proper support for it, whether the model is threaded or processor based concurrency.
I think that it's irresponsible to make a blanket statement like this, because there are many use-cases for multiple threads in Python. Sure, one of the obvious ones (parallel processing) doesn't work, but besides that threads can be extremely useful.
I'm also unclear on what you mean by "no such thing as thread-local data" when there is `threading.local()` that does exactly that.
Lastly, I don't think multiprocessing was created as a workaround for threading per-se. Rather it was a workaround for the global interpreter lock.