Book Image

Learning Concurrency in Python

By : Elliot Forbes
Book Image

Learning Concurrency in Python

By: Elliot Forbes

Overview of this book

Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Multithreading models


In Chapter 1, Speed It Up!, the first section provide a brief introduction to concurrency, where we talked about the two distinct types of threads that we have on a single machine. These were user threads and kernel threads, and it's useful to know how these map to each other, and the different ways that they can be mapped together. In total, there are these three different styles of mapping:

  • One user thread to one kernel thread
  • Many user-level threads to one kernel thread
  • Many user threads to many kernel threads

Within Python, we typically go with the one user thread to one kernel thread mapping, and as such, every thread you create within your multithreaded applications will take up a non-trivial amount of resources on your machine.

However, there do exist some modules within the Python ecosystem that enable you to implement multithreaded-esque functionality to your program while remaining on a single thread. One of the biggest and best examples of this is the asyncio...