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Learning Concurrency in Python

Learning Concurrency in Python

By : Forbes
3.3 (3)
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Learning Concurrency in Python

Learning Concurrency in Python

3.3 (3)
By: 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 (13 chapters)
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Summary


In this chapter, we took a comprehensive look at some of the techniques that you can utilize in order to ensure your concurrent Python systems are as free as practically possible from bugs before they plague your production environment. We covered testing strategies that help to ensure the soundness of your code's logic, and provide you with that extra peace of mind when bug fixing.

We then looked at the various ways that you can debug your Python codebase, touching upon the inbuilt Pdb, and how you can interactively use that in the command-line.

Finally, we looked at the various techniques that you can employ in order to benchmark and profile your Python applications, and ensure that they are as efficient as possible.

In the next chapter, we are going to look at Python's Asyncio library, and explain how we can utilize executors and pools in order to improve the performance of our Python applications.

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Learning Concurrency in Python
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