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

Building a message bus

For this section, we will be using the Celery and Redis packages to build and run our message bus. Once we have completed this section, our mechanism will take a form that is similar to the following:

Figure 9.6 – A message bus with Flask and Celery

As shown in the preceding diagram, we have two processes running. One is running our Flask application, while the other is running Celery, which handles queuing and processing tasks. To make this work, we are going to perform the following steps:

  1. Build a Celery broker for Flask.
  2. Build a Fibonacci calculation task for Celery.
  3. Update our calculation view with Celery.
  4. Define our Celery service in Docker.

Before we embark on these steps, we have to install the following packages using pip:

  • Celery: This is the message bus broker that we are going to use.
  • Redis: This is the storage system that Celery is going to use.

Now that we have installed...