When your Celery tasks are too slow, and you want them to run faster, you usually want to find and then fix the performance bottleneck. It’s true, you can architect a solution where slow tasks don’t impact faster ones, and you may sometimes need to. But if you can manage to make all your tasks fast, that is ideal.
Source: Finding performance bottlenecks in Celery tasks, an article by Itamar Turner-Trauring.
n this post we’re going to see how we can stitch together a few libraries to make a unit-aware queryable data frame from a CSV using extensible records. By the end of this text, we’ll be able to parse a CSV of data from the periodic table, complete with the correct units, and able to quickly ask questions about our data set using the generated indices.
Source: Unit-aware data frames with composite, dimensional and ixset-typed., an article by Dan Firth.
In my time pretending to be an engineer and working with git at Twitter, I’ve seen an interesting behavior pop up intermittently. People start complaining about git-push being slow. This particular issue becomes hard to diagnose, especially since the pandemic because we can’t be certain of the quality of connection being used, and optimizations to
git-pushhas always taken a back seat to all the other changes we’ve done to git internally. But it has persisted long enough that it needed some deeper diving into, and the intermittent nature always fascinated me. Let’s talk about the problem a little more.
Source: Why Would Git Push a Larger than Necessary Pack, an article by Kiran Paul.