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

Keeping data-parallelism simple with Rayon

In Chapter 3, Understanding Concurrency we processed our Fibonacci numbers in parallel. While it was interesting to look into concurrency, when we are building our own applications, we should lean on other crates to reduce the complexity of our application. This is where the rayon crate comes in. This will enable us to loop through numbers to be calculated and process them in parallel. In order to do this, we initially have to define the crate in the Cargo.toml file as seen here:

[dependencies]
rayon = "1.5.1"
With this, we import this crate in our main.rs file with the 
following code:
extern crate rayon;
use rayon::prelude::*;

Then, if we do not import the macros with use rayon::prelude::*; our compiler will refuse to compile when we try and turn a standard vector into a parallel iterator. With these macros, we can execute parallel Fibonacci calculations with the following code:

pub fn fibonacci_reccursive(n: i32) -&gt...