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

Fusing Rust with data access

In web applications, accessing a database is a big part of the process. We could import the dal object that we created in the src/data_access.py file and pass it into our Rust function, executing database operations through Python. While this will technically work, it is not ideal as we will have to waste time and effort extracting objects from the database queries, inspecting them, and converting them into Rust structs. We would then have to convert the Rust structs into Python objects before inserting them into the database. This is a lot of excess code that has a lot of interaction with Python, reducing its speed gain.

Because a database is external from the Python web application, and it contains information about its schema, we can completely bypass Python's implementations by using the diesel Rust crate to automatically write our schema and database models in Rust based on the live database. We can also use diesel to manage the connection...