What is the global interpreter lock (GIL) in CPython?

Let's first understand what the python GIL provides:

Any operation/instruction is executed in the interpreter. GIL ensures that interpreter is held by a single thread at a particular instant of time. And your python program with multiple threads works in a single interpreter. At any particular instant of time, this interpreter is held by a single thread. It means that only the thread which is holding the interpreter is running at any instant of time.

Now why is that an issue:

Your machine could be having multiple cores/processors. And multiple cores allow multiple threads to execute simultaneously i.e multiple threads could execute at any particular instant of time.. But since the interpreter is held by a single thread, other threads are not doing anything even though they have access to a core. So, you are not getting any advantage provided by multiple cores because at any instant only a single core, which is the core being used by the thread currently holding the interpreter, is being used. So, your program will take as long to execute as if it were a single threaded program.

However, potentially blocking or long-running operations, such as I/O, image processing, and NumPy number crunching, happen outside the GIL. Taken from here. So for such operations, a multithreaded operation will still be faster than a single threaded operation despite the presence of GIL. So, GIL is not always a bottleneck.

Edit: GIL is an implementation detail of CPython. IronPython and Jython don't have GIL, so a truly multithreaded program should be possible in them, thought I have never used PyPy and Jython and not sure of this.

Python 3.7 documentation

I would also like to highlight the following quote from the Python threading documentation:

CPython implementation detail: In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing or concurrent.futures.ProcessPoolExecutor. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.

This links to the Glossary entry for global interpreter lock which explains that the GIL implies that threaded parallelism in Python is unsuitable for CPU bound tasks:

The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.

However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.

Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.

This quote also implies that dicts and thus variable assignment are also thread safe as a CPython implementation detail:

• Is Python variable assignment atomic?
• Thread Safety in Python's dictionary

Next, the docs for the multiprocessing package explain how it overcomes the GIL by spawning process while exposing an interface similar to that of threading:

multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.

And the docs for concurrent.futures.ProcessPoolExecutor explain that it uses multiprocessing as a backend:

The ProcessPoolExecutor class is an Executor subclass that uses a pool of processes to execute calls asynchronously. ProcessPoolExecutor uses the multiprocessing module, which allows it to side-step the Global Interpreter Lock but also means that only picklable objects can be executed and returned.

which should be contrasted to the other base class ThreadPoolExecutor that uses threads instead of processes

ThreadPoolExecutor is an Executor subclass that uses a pool of threads to execute calls asynchronously.

from which we conclude that ThreadPoolExecutor is only suitable for I/O bound tasks, while ProcessPoolExecutor can also handle CPU bound tasks.

Process vs thread experiments

At Multiprocessing vs Threading Python I've done an experimental analysis of process vs threads in Python.

Quick preview of the results:

In other languages

The concept seems to exist outside of Python as well, applying just as well to Ruby for example: https://en.wikipedia.org/wiki/Global_interpreter_lock

It mentions the advantages:

• increased speed of single-threaded programs (no necessity to acquire or release locks on all data structures separately),
• easy integration of C libraries that usually are not thread-safe,
• ease of implementation (having a single GIL is much simpler to implement than a lock-free interpreter or one using fine-grained locks).

but the JVM seems to do just fine without the GIL, so I wonder if it is worth it. The following question asks why the GIL exists in the first place: Why the Global Interpreter Lock?

Python's GIL is intended to serialize access to interpreter internals from different threads. On multi-core systems, it means that multiple threads can't effectively make use of multiple cores. (If the GIL didn't lead to this problem, most people wouldn't care about the GIL - it's only being raised as an issue because of the increasing prevalence of multi-core systems.) If you want to understand it in detail, you can view this video or look at this set of slides. It might be too much information, but then you did ask for details :-)

Note that Python's GIL is only really an issue for CPython, the reference implementation. Jython and IronPython don't have a GIL. As a Python developer, you don't generally come across the GIL unless you're writing a C extension. C extension writers need to release the GIL when their extensions do blocking I/O, so that other threads in the Python process get a chance to run.

Suppose you have multiple threads which don't really touch each other's data. Those should execute as independently as possible. If you have a "global lock" which you need to acquire in order to (say) call a function, that can end up as a bottleneck. You can wind up not getting much benefit from having multiple threads in the first place.

To put it into a real world analogy: imagine 100 developers working at a company with only a single coffee mug. Most of the developers would spend their time waiting for coffee instead of coding.

None of this is Python-specific - I don't know the details of what Python needed a GIL for in the first place. However, hopefully it's given you a better idea of the general concept.