Cracking the Code: How Python Import Costs Can Impact Your Startup Time

The Python programming language is renowned for its simplicity and readability. Yet, as more developers flock to Python for their projects, there is an unseen cost that looms over its usability—import costs. Recent research by Trinath Sai Subhash Reddy Pittala sheds light on the often-ignored "import tax" that developers face each time they execute Python scripts. In this article, we break down the implications of these findings for Python users and the ecosystem at large.

Understanding the Import Tax

When Python programs are executed, they incur costs related to importing libraries—a charge that isn’t captured in standard benchmarking practices. The research reveals that this import cost can significantly affect various Python applications, especially in command-line tools, serverless applications, and test environments. In fact, for commonly used Python packages, the import time can vary dramatically, with half of the import processes taking less than 6 milliseconds, while the slowest can take over 354 milliseconds.

New Insights from Longitudinal Data

Pittala's team analyzed the 500 most downloaded packages on PyPI over five years, across several versions of CPython. They found that while the median growth of import cost was modest, the mean growth was surprisingly higher, at approximately 11-13% per year. This skew in data indicates that while many packages grow slowly, the minority that grow quickly contributes disproportionately to overall import costs—sometimes inflating them significantly.

The Impact of CPython Versions

Interestingly, the study leveraged two platforms—Apple’s M1 and Intel Xeon Linux—to compare import times. On macOS, newer CPython versions were found to import code 1.16 times slower than older versions. However, this trend was not uniform across platforms, showcasing the necessity for context in performance assessments. Import times and efficiency can fluctuate between environments and even minor updates to Python’s versioning.

Introducing Lazy Imports: A Game Changer

The research examines Python 3.15's implementation of explicit lazy imports, a feature intended to streamline import costs. While the expectation is that lazy imports will significantly mitigate startup time costs, they may not be universally compatible, as eight out of the top 414 packages broke under global lazy mode. This highlights the need for developers to adapt their codebases for the paradigm shift that lazy imports represent.

What This Means for Developers

Python developers must factor in the "import tax" when budgeting for startup times. The staggering cold import costs—3 to 22 times higher than warm imports—indicate that real-world usage scenarios often diverge from performance benchmarks. The longitudinal dataset created through this research serves as an essential tool for developers aiming to optimize their applications and manage import costs effectively.

As Python continues to evolve, understanding and addressing these hidden costs will be pivotal for developers looking to enhance the performance and efficiency of their applications. With insights from Pittala's research, developers are now equipped with the knowledge to make informed decisions about package usage and implementation strategies.

By acknowledging the dynamics of import costs and leveraging lazy imports wisely, the Python community can move towards a more efficient and responsive future.

Authors: Trinath Sai Subhash Reddy Pittala