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 ...

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 ...

Cloud computing platforms have become very important in recent years for a wide variety of purposes, from serving websites through to scientific analysis, and the BBC has been an enthusiastic adopter ...

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 ...

Python is powerful, versatile, and programmer-friendly, but it isn’t the fastest programming language around. Some of Python’s speed limitations are due to its default implementation, CPython, being ...

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 ...

Think it's complex to connect your Python program to the UNIX shell? Think again! In past articles, I've looked into concurrency in Python via threads (see "Thinking Concurrently: How Modern Network ...

I have worker thread(s) that use the logger. In the main thread, I occasionally need to ask the user to take some action. Which means I need to suppress the logger from actually printing until the ...