Connecting CPython's GC Internals to Real-World Performance

Learn how the knowledge of CPython internals translate into performance insights for your code

Although I have written a very detailed article on the implementation of CPython’s garbage collector, I realized it misses out on explaining the motivation and design behind it. Many of my readers are not that interested in hacking on CPython, but they are interested in leveraging the knowledge of these internals to boost the performance of their Python code.

So, in this article, I’ll give you a more accessible (yet thorough) overview of how garbage collection works in CPython. More importantly, I'll show you how understanding these internals can offer actionable insights for optimizing the performance of your Python applications.

By the end of this read, you'll see how the inner workings of CPython's garbage collection directly impact your application's performance and get some practical tips on how to use this knowledge to make your code run faster. Let's get into it!

This article was triggered due to a performance regression found in CPython’s incremental GC implementation in the 3.13 release candidate. As I posted an explainer about it on Twitter, I realized there is a need for a simpler article on the design of GC which makes it easy to deduce performance insights around memory management in Python applications.

I am making this article available to the paid subscribers as part of early access. I will open this for everyone in a few days. Here’s what’s covered:

Table of Contents

1. Introduction

2. Reference Counting and Role of the GC in CPython

   1. CPython’s Object Allocator

3. Conditions for Triggering the GC

   1. GC Generations

4. Entering the GC

5. The Full Heap Scan

6. The Incremental GC

7. A Worst Case Example to Understand Cost of Full Heap Scan

   1. Assumptions

   2. Generational GC in Action

   3. Performance Analysis

   4. Performance Impact

8. Applying GC Internals to Optimize Performance

   1. Allocate Smaller Objects

   2. Use Memory Efficient Data Structures

   3. Optimize Object Life Times

   4. Minimize Cyclic References

   5. Tune the GC Threshold

   6. Profile and Monitor

9. Conclusion

Photo by Reid Naaykens on Unsplash

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