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

Answers

  1. We initially must get Python from the GIL. We then must build a PyDict struct in order to store and pass Python variables between Python executions. We then define the Python code as a string literal and pass this into our py.eval function with our PyDict storage.
  2. We must make sure that we get Python from the GIL. We then use this to run the py.eval function with the import line of code passed in as a string literal. We must remember to pass in our PyDict storage to ensure that we can reference the module in the future.
  3. We must remember that Python code returns a PyAny struct, which we can extract using the following code:
    let code = "5 + 6";
    let result = py.eval(code, None, Some(&locals)).unwrap();
    let number = result.extract::<i32>().unwrap();

    We can see that number should be 11.

  4. This is because the Python versions must keep stopping to clean up variables with the garbage collection mechanism.
  5. It would be slightly slower. This is because...