Plurrrr

Most people understand that async Python has a higher level of concurrency. It would make some sense for that to imply higher performance for common tasks like serving dynamic web sites or web APIs.

Sadly async is not go-faster-stripes for the Python interpreter.

Under realistic conditions (see below) asynchronous web frameworks are slightly worse throughput (requests/second) and much worse latency variance.

Earlier this week, as part of my work doing a more complete review of the Raspberry Pi 4 (coming soon!), I decided I'd go all-in and spend one entire day working entirely (or at least as much as possible) from a Raspberry Pi.

And not just doing some remote coding sessions or writing a blog post—that's easy to do on a Chromebook, a tablet, or any cheap old laptop—but trying to do all the things I do in a given day, like:

• Browse Twitter using a dedicated app
• Use Slack (you laugh, but Slack uses more memory than most of the other apps I'm running at any given time—combined!)
• Record and edit clips of audio and video
• Work on some infrastructure automation with Docker, Ansible, and Kubernetes

In this post, I’ll show you five ways to flatten a list in Python, and then show you the test results. I consider these results to be a myth-buster, because they are not at all what I expected based on what we consider best practice in the Python community.