Python provides two ways to work around this issue: threading and multiprocessing. Each approach allows you to break a long-running job into parallel batches, which you can work on side-by-side.

In this article, I focus on one of those forms that has a reputation for being particularly frustrating for many developers: threading. I explore the ways you can use threads in Python and the ...

a version of Python that allows full concurrency, or multi-threading, by removing the Global Interpreter Lock. Whether you call it “free-threaded” or “no-GIL” Python, the result is the ...

This means that the GIL is not an issue and threading is an ideal solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely be ...

Guided by this logic, Python inventor Guido van Rossum architected all of Python’s multithreading capabilities around the fatally flawed assumption that computers of the future would have only a ...

Ruby and Python's standard implementations make use of a Global Interpreter Lock. Justin James explains the major advantages and downsides of the GIL mechanism. Multithreading and parallel ...

Python 3.13 has been released with a slight ... In addition, the global interpreter lock can now be deactivated to allow multithreaded applications to run more efficiently. Finally, the new ...

Many people, when they start to work with Python, are excited to hear that the language supports threading. And, as I've discussed in previous articles, Python does indeed support native-level threads ...

Here's a typical exchange: Critic: Python's not as fast as Java. Fan: Yes, it is. Look at this benchmark. Critic: That doesn't count because it's single-threaded. Fan: OK, look at this multithreaded ...