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Python's Journey to Lazy Imports: How Production Needs Drove a Three-Year Development Process

By

tsenturk

2mo ago· 13 min readenInsight

Summary

The article details Python's journey to implementing lazy imports, a feature that allows modules to be loaded only when actually needed rather than at import time. It explains how major companies like Instagram and Hudson River Trading faced real-world performance issues with slow startup times due to eager imports of heavy libraries like PyTorch and NumPy. The piece covers the three-year development process from PEP 690's rejection to PEP 810's unanimous acceptance, highlighting how production evidence and a fundamental design inversion led to the 'lazy' keyword being added to Python 3.15.

Key quotes

· 4 pulled
This isn't a hypothetical scenario from a conference slide deck. This is what Instagram engineers were dealing with every day in production.
It's the reason Python now has a lazy keyword coming in version 3.15 — though getting there took three years and two attempts.
From PEP 690's rejection to PEP 810's unanimous acceptance — how Python finally got explicit lazy imports after three years of real-world production evidence and a fundamental design inversion.
You run mytool --help and wait. Two seconds. Three. No network requests, no error, no disk thrashing. Just Python dutifully loading PyTorch, NumPy, pandas, and a dozen other heavy libraries it will never touch — all so it can print a usage message and exit.
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From PEP 690's rejection to PEP 810's unanimous acceptance — how Python finally got explicit lazy imports after three years of real-world production evidence and a fundamental design inversion

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