Thread Carefully: An Introduction To Concurrent Python

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 much any time an application is bottlenecked by a resource.

It’s sadly the case that writing quality concurrent code can be a real headache, but this article aims to demonstrate how easy it is to get started writing threaded programs in Python. Due to the large number of modules available in the standard library which are there to help out with this kind of thing, it’s often the case that simple concurrent tasks are surprisingly quick to implement.

We’ll walk through the difference between threads and processes in a Python context, before reviewing some of the different approaches you can take and what they’re best suited for.

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Stop Using Python 2: What You Need to Know About Python 3

Though Python 3 was released in 2008, many projects are still stuck on Python 2.

It’s understandable that porting large existing codebases to a new version is a prospect which sends a shiver down many a developer’s spine. But code inevitably needs to be maintained, and so when all the shiny new features that would fix everything are in a new version, is it really worth staying rooted in the past?

We’ll take you through some of the features that Python 2 programs are missing out on, not only from 3.0 but up to the current release (3.7).

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