Python lets you parallelize workloads using threads, subprocesses, or both. Here's what you need to know about Python's thread and process pools and Python threads after Python 3.13.

The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not possible for threads to take advantage of multi-core systems.

As a software developer, you might often encounter situations where you need to manage multiple tasks simultaneously. In Python, threading and asynchronous (async) programming are two approaches ...

Python knows that I/O can take a long time, and so whenever a Python thread engages in I/O (that is, the screen, disk or network), it gives up control and hands use of the GIL over to a different ...

Learn how to use Python’s async functions, threads, and multiprocessing capabilities to juggle tasks and improve the responsiveness of your applications. If you program in Python, you have most ...

Thread Safety Issues: Multithreading can introduce complex issues such as race conditions, deadlocks, and resource contention, which require careful handling using locks or semaphores. Higher Memory ...

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 ...

Threading in Python involves creating multiple threads within a process to perform tasks concurrently. Each thread runs independently, sharing the same memory space, which can lead to issues like ...