Rust: A New Engine for Python Performance Optimization

Introduction

For a long time, Python developers have faced a dilemma: should they write elegant and readable code, or pursue high performance? Traditionally, when performance is critical, developers turn to C extensions. However, this landscape is changing—Rust is becoming a powerful ally for Python performance optimization.

The latest report from JetBrains, “State of Python 2025,” shows that the usage of Rust in Python binary extensions has surged from 27% to 33%, marking a 22% increase in just one year. This data reflects a significant shift in the Python ecosystem towards performance optimization.

Reasons for Rust’s Rise in Python

Why is Rust rapidly emerging in the Python ecosystem? There are several key factors:

1. Exceptional Performance

Rust offers performance comparable to C while maintaining seamless integration with Python. Its zero-cost abstractions and efficient memory management make it an ideal choice for performance-critical components.

2. Memory Safety Guarantees

Unlike C, Rust can prevent common programming errors such as buffer overflows and memory leaks at compile time. This provides higher safety when extending Python, avoiding the introduction of security vulnerabilities or crashes.

3. Superior Development Experience

Rust features a modern toolchain, clear error messages, and a powerful package manager (Cargo), offering a better development experience compared to the painful process of writing and debugging C extensions.

Real-World Use Cases

There are already several successful cases in the Python ecosystem that utilize Rust:

Polars: A Revolutionary Data Science Tool

Polars is a data processing library built with Rust, providing DataFrame operations that are often several orders of magnitude faster than Pandas. It offers Python users a natural and intuitive API while delivering unprecedented data processing speed.

Here is a simple example of using Polars:

import polars as pl

# Create a DataFrame
df = pl.DataFrame({
    "A": [1, 2, 3, 4, 5],
    "B": ["a", "b", "c", "d", "e"],
    "C": [10.0, 20.0, 30.0, 40.0, 50.0]
})

# Perform high-performance filtering operation
filtered_df = df.filter(pl.col("A") > 2)
print(filtered_df)
# Outputs results that are several times faster than Pandas

Pydantic V2: A Leap in Data Validation Performance

Pydantic V2 has rewritten its core validation engine in Rust, bringing significant performance improvements to data validation and serialization, impacting nearly all areas of Python—from web APIs to machine learning pipelines.

from pydantic import BaseModel

class User(BaseModel):
    id: int  # User ID
    name: str  # User name
    active: bool = True  # User status, defaults to active

# Create a user instance
user = User(id=123, name="John Doe")
print(user.model_dump_json())
# Output: {"id":123,"name":"John Doe","active":true}

FastAPI Ecosystem: The Perfect Combination of Asynchronous Architecture and Rust

FastAPI’s usage has risen from 29% to 38% (a 30% increase), partly due to its asynchronous-friendly architecture perfectly pairing with Rust-based server components.

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
async def read_root():
    # Behind this simple endpoint, FastAPI utilizes Rust components for high-performance processing
    return {"message": "Hello, Rust-enhanced Python!"}

The Impact of Rust on Python Infrastructure

Rust’s influence has extended to Python’s core infrastructure. Traditional Web Server Gateway Interface (WSGI) servers are gradually being replaced by alternatives compatible with Asynchronous Server Gateway Interface (ASGI), many of which are built with Rust.

For example, the Rust-based application server Granian is gaining significant attention. While Uvicorn is Python-based, it is increasingly integrating Rust components.

Next-Generation Tools

Two new Python type checkers have emerged, both written in Rust:

Astral’s ty: Described as “a lightning-fast Python type checker and language server”
Meta’s Pyrefly: A high-performance alternative to traditional type checkers like mypy

Both tools provide extremely fast Language Server Protocol (LSP), further proving that “Rust has become Python’s performance co-pilot.”

Business Value for Enterprises

For enterprises, Rust-enhanced Python brings tangible benefits:

Performance improvements can translate into cost savings, including reduced cloud computing costs and memory usage
Faster response times enhance customer satisfaction
More efficient code reduces energy consumption

Advice for Python Developers

If you are a Python developer, here are some tips on how to embrace the Rust wave:

1. Learn to Read Rust Code

You don’t have to fully switch to Rust development, but learning the basics of Rust syntax and understanding the internal implementations of the libraries you use will be a valuable skill.

2. Embrace Rust-Enhanced Libraries

When choosing packages with similar functionalities, consider those with a Rust core—they often provide better performance without sacrificing Python’s usability.

3. Consider Using Rust for Extension Development

If you are building performance-critical Python extensions, you should evaluate using Rust as the implementation language instead of defaulting to C.

Conclusion

Rust is not replacing Python; it is supercharging it. This hybrid approach offers developers the best of both worlds: leveraging Python’s expressiveness and ecosystem for application logic while using Rust for computationally intensive components to achieve outstanding performance.

As more Python projects and libraries adopt Rust to optimize performance-critical parts, this trend will only continue to grow. For Python developers, now is the time to pay attention to this development and consider how to integrate Rust’s advantages into their own projects.

References

JetBrains State of Python 2025 report: https://www.jetbrains.com/lp/python-developers-survey-2025/
Talk Python Blog: https://talkpython.fm/blog/python-rust-integration-2025
FastAPI Official Documentation: https://fastapi.tiangolo.com/
Polars Documentation: https://pola.rs/
Pydantic V2 Release Notes: https://docs.pydantic.dev/latest/blog/pydantic-v2-release/

Book Recommendations

The book “The Rust Programming Language” (2nd Edition) is an authoritative learning resource written by the Rust core development team and translated by members of the Chinese Rust community. It is suitable for all software developers looking to evaluate, get started, improve, and research the Rust language, and is considered essential reading for Rust development work.

This book introduces the fundamental concepts of Rust language and its unique practical tools, covering advanced concepts such as ownership, traits, lifetimes, and safety guarantees, as well as practical tools like pattern matching, error handling, package management, functional features, and concurrency mechanisms. It includes three complete project development case studies, guiding readers from zero to developing practical Rust projects.

Notably, this book has been updated to include content from the Rust 2021 edition, meeting the systematic learning needs of beginners and serving as a reference guide for experienced developers, making it the best entry point for building solid Rust skills.