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. Threads are lightweight and enable multithreading, where we can run multiple tasks that could have idle time. A process is more expensive, enabling us to run multiple CPU-heavy tasks at the same time. Processes do not share memory, while threads do.
  2. Multithreading would not speed up our Fibonacci sequence calculations because calculating Fibonacci numbers is a CPU-heavy task that does not have any idle time; therefore, the threads would run sequentially in Python. However, we did demonstrate that Rust can run multiple threads at the same time, getting a significant speed increase.
  3. Multiprocessing is expensive and the processes do not share memory, making the implementation potentially more complex. A processing pool keeps the multiprocessing part of a program to a minimum. This approach also enables us to easily control the different numbers of workers we need as they're all in one place, and we can also return all the outcomes in the same sequence as they...