For the people who are asking the question, why was GIL introduced in the first place or what advantage does it have is that the primary purpose of the GIL is to simplify the memory management and ensure thread safety in python ( cpython ). It is a lock that allows only one thread to execute Python bytecode at a time, even on multi-core systems. This means that even if you have multiple threads in a Python program, they can't truly execute in parallel and take full advantage of multiple CPU cores. The GIL was introduced in python to simplify the memory management of Python objects. The memory management in python is implemented using reference counting, where each object keeps a count of how many references are pointing to it. When the reference count of an object reaches zero, it is deallocated. The GIL ensures that reference counting is thread-safe by allowing only one thread to modify the reference count at a time. While the GIL simplifies memory management and provides thread safety, it also has some drawbacks. Because only one thread can execute Python bytecode at a time, it can limit the performance of multi-threaded Python programs, especially those that involve CPU-bound tasks. However, the GIL is not always a limiting factor for performance, as it can still be beneficial in scenarios where the program is I/O bound or when using extension modules written in languages like C/C++ that release the GIL during their operations. Although you cannot enable / disable or remove GIL, it is a part of python itself, although there is a project that exist where a fork of cpython is being developed without GIL.

Eehh... this video isn't very informative. (WHY is there a GIL? Why don't other languages use it? What are the benefits of having a GIL? etc.) In addition some of the simplifying goes so far as to spread misinformation. Why would you use non interpreted languages when explaining how other languages can use the same interpreter at the same time? I know there are special cases for C++, but the vast majority of the time it's compiled, not interpreted.

I really think if you are going to make a video on the GIL it is worth exploring why it exists and how important it was for the adoption of Python. I would also point out that using IO bound applications as an example isn't a great one. It is very rare for IO issues to be CPU bound. Async and event loops work perfectly well for IO bound applications.

I have extensively used both multi-threading and multi-processing on my codes. The thumb rule is to go for multi-processing if a process needs to perform some processor-intensive task. If it just needs to wait for a response from an API, database, or some I/O device, use multi-threading. GIL doesn't cause problems if you know how to structure your code efficiently. Python is one of the easiest languages to learn, but these are some advanced concepts of Python that needs a thorough understanding of the concepts.

2:05 C++, Go and Rust are not interpreted languages.

Hi Tim, could you make more videos on this topic? Maybe going further into detail with multi-processing and how to handle the separation of tasks and how to recombine the output?

I knew all of this, but wish you went into WHY python implements a GIL.

🎯 Key Takeaways for quick navigation: 00:00 *🎬 Introduction to the Global Interpreter Lock (GIL)* - Explanation of the Global Interpreter Lock (GIL) in Python. - Overview of how traditional CPU processing works with threads. - Understanding software threads and their execution on CPU cores. 02:05 *🔒 Limitations of GIL in Python* - Discussion on the limitations imposed by the Global Interpreter Lock (GIL). - Explanation of how the GIL restricts parallel processing in Python. - Impact of GIL on multithreading and parallel execution in Python programs. 03:07 *🔄 Handling IO Operations with Threading* - Use cases for multithreading in Python, particularly in IO-bound operations. - Explanation of how multithreading enables concurrent execution in IO-bound tasks. - Example scenarios demonstrating the benefits of multithreading for IO operations. 04:10 *⚙️ Introduction to Multiprocessing* - Introduction to Python's multiprocessing module as an alternative to multithreading. - Explanation of how multiprocessing allows parallel execution by spawning separate processes. - Comparison between multithreading and multiprocessing in Python. Made with HARPA AI

I’m kind of a fan of the GIL, it’s a level of complexity removed for new Python people

Very good explanation, some details: 1. All modern CPUs are superscalar, which means that you can, and should perform multiple operations per thread. Modern compilers will attempt to help you out, for example by unrolling a loop for an arithmetic loop. 2. The summation is not sped up by a factor of 4, it was a perfect example to explain why. The speed-up is not that big because you have to add the segments, two times.

Great job on explaining the Global Interpreter Lock (GIL) in Python, but I don't agree with the final conclusion regarding multiprocessing being not as easy to handle as multithreading. Writing safe concurrent code is currently a really hard task. David Baron once said, "You Must be This Tall to Write Multi-Threaded Code," pointing at a post-it note placed at an 8-foot height in Mozilla’s San Francisco office.

SImple in GIL: Multi-Threading(Concurrent): Not really parallel because here is multiple threads with one interpreter Multi-Processing(Parallel): Spawn multiple interpreter for each thread Parallel: Parallelism refers to the simultaneous execution of multiple tasks or processes, where each task is broken down into smaller sub-tasks that can be executed independently and simultaneously on multiple processors or CPU cores. In parallel execution, tasks run at the same time, truly concurrently, and can potentially speed up the overall execution time. Parallelism requires hardware support for multiple processing units. Concurrent: Concurrency, on the other hand, refers to the ability of a system to handle multiple tasks or processes at the same time, even if they are not executing simultaneously. In a concurrent system, tasks may be started, run for a while, paused, and then resumed later, interleaving their execution. Concurrency doesn't necessarily require multiple processing units; it can be achieved through techniques like time-sharing or interleaved execution on a single processor. Because python is simplest language GIL ensure that a beginner mistake not become a problem by limiting threads while multi-processing is more complicated to multi-threading so python give to advanced user and create a awesome balance. If you want to enjoy power of multiple works at a time but not to make it complicated use multi-threading and when actually want parallel execution use multi-procession

"The Guy" form Disturbed(David Draiman best vocalist our generation period!) covers make me smile, interesting way that You use him like that :) Nice explanation Tim, thank You for Your work!

Ah my old arch nemesis and frenemy the GIL. When I was new to it, I hated it and didn't understand why make an OOP language with all this flexibility then constrain it so heavy handed. The GIL is that best friend that will always call you out on your BS and stop you from doing dumb things. If you are running afoul of the GIL, you are either going about solving your problem in the worst way possible, or using Python for the wrong job and something else should be used instead. Working with/around the GIL can be an interesting exercise to deconstruct your problem into proper units of atomic work. You have to go out of your way and know a lot to write thread/memory unsafe code in Py typically.

Nice video, very informative! The style was perfect for this video, great demonstrations.

I've never understood hate for GIL. Python has plenty of problems, GIL wouldn't make my top-100 list. For threads, you can do multiple OS threads, if you need this. Or you can do co-operative multitasking with async, generators, etc. There are ways to do whatever you need threads for, but better. And GIL means the programs now lack one extremely common failure mode entirely, while making Python programs run faster. Seems like almost universally a good idea.

Great video Tim, thank you! Perhaps you could make another video explaining why the GIL even exists in the first place.

I would like to ask is it better to use threading or multiprocessing for writing and saving files. Reecently I have a program to read scan data from sensor, the sensor has a very high sensing rate and produce a lot of data point. I have first tried to use threading to create a one producer-multiple consumer pattern, but I find out that once a consumer start writing to csv (large amount of data), the producer callback cannot be triggered.... so I switched to use multiprocessing and it works out fine. But most people in the Internet saying that writing file is a IO task, and IO task should use threading. Can some one explain the reason? or there is some misunderstanding for me about how to correctly use threading?

I absolutely love the new video style! its focused to the point short and give all the information we need! Great video! Thanks tim!

You seem to be leaving out a huge piece here: async and its predecessors green threads. They have been the typical way get around waiting for non-CPU intensive processes.

When I was a newbie I use gunicorn to support multiprocessing, now I'm also using celery to support async process and multiprocessing in my project. It's additional complexity to setup but still worth the effort.

at 4:05, if one thread execution is I/O blocked, how does the interpreter switch to process another thread execution? how does the interpreter know to release the lock for another thread?

2:56 This is basically Async in python right

I need help I'm studying the freecodecamp course, and when i type the following code for the conditional exercise x = 0 y = 10 if 0 == x: if y == 10: print('YES') the output in the VS code terminal is Syntax Error however when I run it in python itself or an online compiler, it runs successfully and prints YES I need help :(

Thanks Tim. Could you also make a video about the difference between coroutine and thread? Thank you

what is the benefit of multiple-threads vs async?

I started learning python 12 months ago, watching videos like this now no more for loops lol, gonna start applying for jobs in about 3 months, i wanna be an sql developer, wish me luck

Autocomplete not working for OpenCV (cv2) in jupyter lab , please help

Are there any major singular culprits outside of GIL that contribute to Python's slowness?

Your explanations are fantastic!

Hey Tim i abosultely love your videos! Can you make a tutorial on building a captive portal with Django?I really like to make one but im having a hard time. Thank you

This is wrong. C++, Rust and Go programs are compiled and runs on bare metal. They have no interpreter, for comparison. There are mentions about a Go runtime but that is simply additional code added by the Go compiler to take care of garbage collection and other things rather than a virtual machine level abstraction as in JVM.

Hey tim I am really motivated by ur programming journey and skills. I want to ask as a amateur programmer what steps do I need to become professional software programmer.I don't have cs degree.

so what does Polars and Dask do? please explain?

I have a Go background and this GIL in python looks like mutexes in Go. Helps acheiving thread safe programs.

To add twenty numbers on four cores, you have to count up the numbers, 20, count up the cores, 4, divide the 20 by the four, 5 -- and by this time your "limited" opponent is finished adding the twenty. There may be programs where Python is slower -- but this example clearly isn't one of them.

I love the informative videos but i miss your hardcore coding tutorials. I understand you probably don't have the same time that you used to but just know I watch your tutorials all the time!

Hopefully the new kid on the block, Mojo will solve this problem.

Just a Awesome explanation

Yes but why is there a GIL? the video doesn't explain why Python does this when other languages don't.

If there are 4 cores then a CPU can have 4 OR 8 threads. Not between.

That I/O blocking example is not that great. Python still has async which is nice for such operations

I have a qn, are there any advantages of having GIL over not having it?

Absolutely amazing explanation. Still would like to know why the lock is there though.

most controversial feature 💩

I like GIL, can't imagine my l life without it

Hey man, will you be using the Mojo language when it comes out? I want to make a game in the future and many people told me to not use python and that i should go for C# or C++, do you think this Mojo will be a better option for it?

Hey love your content❤ next time could you please make video about JavaScript 😅 thank you 🙏

Why did they decide to call them threads? Why didnt they call them tables? Now threads might sound cooler. But its really not related to sewing as much as it related to sorting. So when these threads come together after processing, are they called strings or ropes or something?Or are they more like a way to pipe programs through rather than sorting? Because you said that python still is able to multiprocess. If thats the case, then why didnt they just call them pipes or ziplines or something? Lol. Jesus Loves You Man.God Bless You.

Excellent.

Awesome

I wanted to say that if you see my msg, I have bought Programming expert course and it is utter waste. You are teaching as if its 10 year old learning programming. I took that course thinking it has shown around 251 problems to solve and I get frustrated seeing True or False question, this or that. Have some standard question You have add practice questions. Not 2nd grade questions. Very disappointed and not getting refund too.

Was just looking this up yesterday.

I clicked because of The Guy Those who know, know🤘

And still, what are the advantages of having a GIL?

Look. If you want speed use a compiled language. Python will never be fast. It's just not designed for it.

second comment. 134th viewer. 16th liker. all just 4 minutes after the video was published

Very educational

Love form India ❤

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U are handsome, but I liked the facecamless teaching way more

second