Profilers are a powerful tool for debugging performance issues in applications and services. An experienced engineer who is running code in production has to know how to use these to be able to analyze performance issues in their systems. This video gives a quick introduction to the world of profilers in Python. Here is what it covers:
What are profilers
Kinds of profilers
Tracing profilers — how do they work, and how to use them in Python
Sampling profilers — how do they work and a demo of py-spy
How to use perf to profile Python
I made this video because tomorrow we are doing a live session on building a bare bones sampling profiler for Python and I think it is topical that I covered profilers before diving deep into their implementation.
If you are someone who is interested in systems programming and enjoy learning how tools like compilers, debuggers, profilers work then do sign-up for the session tomorrow, you can find the details in the below linked post.
Live Session: CPython and ELF Essentials for Building a Basic Remote Profiler
In the previous article we covered everything about the internals of the CPython runtime — its key data structures and how they are initialized. Even though the main role of the runtime state is in Python code execution on the interpreter (which we will cover in the next article), the runtime also enables debugging tools such as remote debuggers and profilers as well.
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