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

Utilizing and testing our package

We have started building out our solution in a Python package coded in Rust. However, we need to justify to our team and ourselves that all this effort was worth it. We can test to see whether we should continue with our efforts in a single isolated Python script. In this Python script, we can test by following these steps:

  1. Build a Python construct model using pandas.
  2. Build random event ID generator functions.
  3. Time our Python and Rust implementations with a series of different data sizes.

Once we have carried out all the aforementioned steps, we will know whether we should progress further with our module.

In our testing script, before we start coding anything, we must import all of what we need with the following code:

import random
import time
import matplotlib.pyplot as plt
import pandas as pd
from flitton_oasis_risk_modelling import construct_model

Here, we are using the random module to generate random event IDs...