Why Does Python’s GIL Limit True Parallel Execution?

I get why GIL exists, but it’s still frustrating. The initial decision was about keeping it simple so that built-in functions would be thread-safe, but that also means having to deal with all these complications in multi-threading. If you're like me, sometimes you wonder if you really need to dive into operating systems just to grasp these language internals. It can be quite overwhelming!

Actually, there was some confusion about whether the GIL was removed in Python 3.14. It wasn't; you can still disable it, but the GIL is still around. If you’re on Windows or Mac, there's also the overhead of spawning new processes, which isn’t the most efficient approach for parallelism. It's quite a journey trying to work around the GIL, and it would require you to understand how thread safety works in practice.

The GIL is a real hassle when working with threads in Python. It's basically there to prevent issues with memory and thread safety, ensuring that only one thread can run Python bytecode at a time. This design comes from the original implementation of Python, CPython, which was single-threaded by default. The GIL helps avoid deadlocks and unpredictable behavior that could arise if multiple threads were manipulating memory simultaneously. There have been various attempts to create Python interpreters that don’t rely on the GIL, but most have struggled to succeed.