You're reading from Asynchronous Programming in Python Apply asyncio in Python to build scalable, high-performance apps across multiple scenarios

Product type Paperback

Published in Nov 2025

Publisher Packt

ISBN-13 9781836646617

Length 202 pages

Edition 1st Edition

Languages

Python

Tools

PyScript

Concepts

Programming Language

Author (1):

Nicolas Bohorquez

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Table of Contents (14) Chapters

Preface

1. Synchronous and Asynchronous Programming Paradigms FREE CHAPTER

2. Identifying Concurrency and Parallelism

3. Generators and Coroutines

4. Implementing Coroutines with Asyncio and Trio

5. Assessing Common Mistakes in Asynchronous Programming

6. Testing and Asynchronous Design Patterns

7. Asynchronous Programming in Django, Flask and Quart

8. Asynchronous Data Access

9. Asynchronous Data Pipelines

10. Asynchronous Computing with Notebooks

11. Unlock Your Exclusive Benefits

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12. Other Books You May Enjoy

13. Index

Summary

In this chapter we have explored three ways to operate over data using asynchronous programming. In many use cases, the management of state in an application requires the inclusion of a database server to persist the data, which is a good fit for asynchronous programming. In Python there are several ways to harness database engines:

As in-process tools that handle direct connections from your source code
As external dependencies in a client/server architecture
As an abstraction provided by ORMs

Finally, remember that databases are not the only way to handle structured data in Python, and that other use cases exist that involve data wrangling and which could benefit from asynchronous programming. In the following chapter we’re going to explore one of the most important of these: data pipelines.