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CPython now ships its own sampling profiler, free-threading is going mainstream, and coding agents have started reading profiles. In this post I look at where Python profiling stands today, compare Tachyon, py-spy, Scalene and Austin, and go through what's new in Austin 4.1.
Coding agents are fast becoming the main generators of new source code, but they are also being used for code review, finding and fixing bugs. How can they be used to the best extent to discover and fix performance issues?
The latest version of Austin comes with a heap size option that can be used in increase the accuracy with which invalid samples are detected. In this post I give a brief description of how this works.
I shall reveal to you how I managed to complete the Hacktoberfest 2021 challenge with just a profiler. So read on if you are interested!
Observability into native call stacks requires some compromise. In this post I explain what this actually means for a Python tool like Austin.
In this short post I will try to convince you of how easy it is to find performance issues in your Python code and how you should develop the habit of profiling your code before you ship it.
If you want to be sure that your applications are working optimally, then sooner or later you will end up turning to profiling techniques to identify and correct potential issues with your code. In this post, I discuss some of the current profiling tools and techniques for Python. The official documentation has a whole section on the subject, but we shall go beyond that and have a look at some alternative solutions, especially in the area of sampling profilers.