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

Summary


In this chapter, we covered some of the key principles of reactive programming. We looked at the key differences between both reactive programming and typical event-driven programming, and we dived deeper into the specifics of the very popular RxPY Python library. We covered observers and observables, emitting events, and multicasting to numerous subscribers.

Hopefully, by now, you have an appreciation of some of the nuances of reactive programming, and you'll now be able to piece together your own reactive systems. You should also have some insight into how you can construct functional programs using Python, using this newly covered topic of operators in conjunction with PyFunctional.

In the next chapter, we'll discuss how you can improve the performance by leveraging the true power of your graphics card to perform tasks such as data analysis and research into big data. We'll see how to utilize the hundreds to thousands of cores that are all working in parallel and reach new performance...