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 ...

The ability to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty ...

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 likely ...

Learn how to use asynchronous programming in Python 3.13 and higher. Get more done in less time, without waiting. Asynchronous programming, or async, is a feature of many modern languages that allows ...

Python already has several ways to run programs concurrently — including asynchronous functions, threads, subinterpreters, and multiprocessing — but all of those options have drawbacks of one kind or ...

We can admit it, marketing is not our strong suit. Our strength is writing the kind of articles that developers, administrators, and free-software supporters depend on to know what is going on in the ...

Python has evolved significantly over the past decade. While Python once had a strong reputation as an "easy-to-write ...

Your Python cache can run the same work twice, even with the GIL. See the check-then-act race, the lru_cache gotcha, and how ...

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 processing ...