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Asynchronous Programming in Python

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

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Product type Paperback
Published in Nov 2025
Publisher Packt
ISBN-13 9781836646617
Length 202 pages
Edition 1st Edition
Languages
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Author (1):
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Nicolas Bohorquez Nicolas Bohorquez
Author Profile Icon Nicolas Bohorquez
Nicolas Bohorquez
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Table of Contents (14) Chapters Close

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 12. Other Books You May Enjoy
13. Index

Handling exceptions in asynchronous code

The basic rules of Python exception handling in asynchronous code are identical to those which apply to synchronous implementations, but you must decide whether to handle exceptions locally in the coroutine/task or propagate them up to the caller.

In previous implementations you can see that a really generic behavior is implemented in the get_data_nonblocking method: if a aiohttp.ClientResponseError is thrown then the result is a string with the message of the error. This breaks the method contract (a dict is expected as result) and the returned message is uninformative. Similarly, the get_data method handles all possible exceptions by just returning them as a string, which is also not the best way to report the anomaly. The following code (available at Chapter 5/extractor_async4.py) shows an alternative implementation which offers some improvements:

import asyncio
import aiohttp
BASE_URL = https://ponyapi.net/v1/character/
def get_data...
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