Explore the differences between threading and async in Python to optimize your software development projects for better performance.

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.

Python 3.13 introduced the first public, if experimental, “free-threaded” or “no-GIL” builds of the language, which we’ll call “3.13t.” 3.13t allows CPU-bound Python threads to run ...

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 ...

Threads can provide concurrency, even if they're not truly parallel. In my last article, I took a short tour through the ways you can add concurrency to your programs. In this article, I focus on one ...

Learn the key differences between async and multithreading in Python. Discover which concurrency model is best for your specific use case, whether it's I/O-bound or CPU-bound tasks.

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.

Discover the key differences between threading and async in Python and how they impact your software development projects for better concurrency management.