Book Image

Speed Up Your Python with Rust

By : Maxwell Flitton
5 (2)
Book Image

Speed Up Your Python with Rust

5 (2)
By: Maxwell Flitton

Overview of this book

Python has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in. This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust. By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support.
Table of Contents (16 chapters)
1
Section 1: Getting to Understand Rust
5
Section 2: Fusing Rust with Python
11
Section 3: Infusing Rust into a Web Application

Summary

In this chapter, we completed our tool belt when it comes to building Python extensions in Rust by using Python modules in our Rust code. We got a deeper appreciation for modules such as NumPy by exploring matrix mathematics to create a simple mathematical model. This showed us that we use modules such as NumPy for other functionality such as matrix multiplication, as opposed to just using NumPy for speed. This was demonstrated when we manipulated multiple mathematical equations with a few lines of NumPy code and matrix logic.

We then used matrix NumPy multiplication functions in our Rust code to recreate our mathematical model using a flexible functional programming approach. We finished this off by making our interface in a Python class. We also must remember that the NumPy implementation was faster than our Rust code. This is partly down to poor implementation on our part and the C optimization in NumPy. This has shown us that while Rust is a lot faster than Python, solving...