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.

Python threading. If you’re familiar with threading in general, ... “CPU-intensive” refers to work happening inside the Python runtime (e.g., the regular expressions in Listing 3).

The GIL is controversial because it only allows one thread at a time to access the Python interpreter. ... the queue.Queue that we used for threading will not work between processes.

Python Thread Support. To quote the Python thread module documentation: "The design of this module is loosely based on Java's threading model. However, where Java makes locks and condition variables ...

Well, Python provides threading. Many people think of Python's threads as fatally flawed, because only one thread actually can execute at a time, thanks to the GIL ... In my next article, I plan to ...

Async programming and threading are both techniques in Python used to run code concurrently. When you're dealing with I/O-bound and high-latency operations, both methods can help improve the ...

Here’s how it works: Each thread has exclusive access to the GIL for a bit of time, does some work, and releases it. Meanwhile, every other thread is on hold, waiting to get a chance to access ...