How to Resolve Memory Increase Issues After Upgrading setuptools

Описание: Discover effective methods to handle memory usage increases after upgrading `setuptools` in Python environments.
---
This video is based on the question https://stackoverflow.com/q/76575080/ asked by the user 'Dinesh Reddy' ( https://stackoverflow.com/u/875977/ ) and on the answer https://stackoverflow.com/a/76618060/ provided by the user 'Dinesh Reddy' ( https://stackoverflow.com/u/875977/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: memory increase after upgrading setuptools

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Memory Increase After Upgrading setuptools

Recently, a common issue has emerged among Python developers who upgraded their setuptools package to version 65.3.0. Many users have reported a concerning increase in memory and performance bottlenecks, particularly when using the distutils module. This guide will explore the underlying problem and present a straightforward solution.

The Problem

When you upgrade setuptools, you may encounter significant memory consumption and slowdowns in your Python scripts. For instance, one user observed that following the upgrade, their system's memory usage drastically increased due to the loading of several dynamic libraries. In their simple test script, they noted that:

Memory usage on upgraded setuptools: Increased considerably, with a test script resulting in numerous loaded libraries, leading to longer loading times and elevated memory consumption.

Memory usage on older setuptools version (57.0.0): Was significantly less, demonstrating that the newer version had introduced performance issues.

Example Scenarios

With the memory increase problem identified, it was important to understand where the issue stemmed from. For example:

On Python 3.9 with setuptools 65.3.0:

[[See Video to Reveal this Text or Code Snippet]]

The process consumes high memory with many dynamic libraries loaded.

On Python 3.9 with setuptools 57.0.0:

[[See Video to Reveal this Text or Code Snippet]]

The memory consumption is significantly lower.

This comparison highlights the clear impact of this specific upgrade on system performance.

The Solution

Fortunately, addressing this memory issue after upgrading setuptools is relatively easy. The solution involves setting an environment variable that directs Python to use the distutils module from the standard library instead of the one bundled with setuptools.

Steps to Fix the Memory Increase

Set the Environment Variable:
You need to export the SETUPTOOLS_USE_DISTUTILS environment variable:

[[See Video to Reveal this Text or Code Snippet]]

This will ensure that Python utilizes the standard distutils distribution instead of the one packaged with setuptools, which is known to cause the increased memory footprint.

Confirmation of the Change:
After exporting the variable, confirm which version of distutils is being used:

[[See Video to Reveal this Text or Code Snippet]]

Previously, with setuptools upgraded, it would lead to:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In conclusion, if you find yourself dealing with increased memory consumption upon upgrading setuptools, remember that configuring the SETUPTOOLS_USE_DISTUTILS variable to stdlib may resolve the issue effectively. While upgrading packages can bring enhancements and features, they can also bring unintended consequences, as seen in this situation.

By addressing these changes proactively, you can maintain optimal performance in your Python applications while leveraging the latest package improvements.

For further questions or assistance, feel free to reach out or share your experiences with upgrading packages in the comments below!