Understanding Python's Global Interpreter Lock (Overview)
The Python Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter.
This means that only one thread can be in a state of execution at any point in time. The impact of the GIL isn’t visible to developers who execute single-threaded programs, but it can be a performance bottleneck in CPU-bound and multi-threaded code.
Since the GIL allows only one thread to execute at a time even in a multi-threaded architecture with more than one CPU core, the GIL has gained a reputation as an “infamous” feature of Python.
In this video course you’ll learn how the GIL affects the performance of your Python programs, and how you can mitigate the impact it might have on your code.
00:00 Welcome to Understanding Python’s Global Interpreter Lock. My name is Christopher, and I will be your guide. This course is all about the Global Interpreter Lock, known as the GIL to its frenemies.
00:11 To understand the GIL, you first need to know a little bit about concurrency and then how Python manages memory cleanup. Once you understand those two concepts, you can see why the GIL exists and then better understand how it affects your code.
00:26 The code in this course was tested using Python 3.12. Well, most of it. To show how CPython and the GIL have changed over the years there is also some use of Python 3.9.
00:37 In fact, that’s one of your takeaways from the course. The GIL is an internal mechanism in CPython, and as such, its behavior can change.
00:46 The GIL is often spoken of between clenched teeth by people who want to add concurrency to their code to speed it up, but just what is it and why is it there?
00:55 Concurrency is complicated and adds the possibility of race conditions to your code, and the GIL is there to protect the CPython internals from just these kinds of race conditions.
01:06 This is key to having a stable interpreter and is needed for memory management to work in multi-threaded environments. The result, though, is that it restricts the kind of concurrency that you can have in Python.
01:18 The GIL is actively being worked on in Python. There are multiple efforts underway, some to reduce the effect, and others to get rid of it altogether, although that has been attempted in the past, so who knows what the future will hold?
01:33 To understand the why of the GIL, first you need to understand the varieties of concurrent programming available to you and how Python deals with them.
Become a Member to join the conversation.