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Asynchronous Programming in Python

Asynchronous Programming in Python

By : Nicolas Bohorquez
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Asynchronous Programming in Python

Asynchronous Programming in Python

By: Nicolas Bohorquez

Overview of this book

Asynchronous programming is one of the most effective but often misunderstood techniques for building fast, scalable, and responsive systems in Python. While it can significantly improve performance, efficiency, and sustainability, using async without a clear understanding of its trade-offs can lead to fragile designs and hard-to-debug issues. This book offers a structured approach to applying asynchronous programming in Python. It begins with a conceptual framework to help you distinguish between synchronous and asynchronous execution models, and shows how async relates to other concurrency strategies such as multithreading and multiprocessing. From there, you will explore the core tools available for building async applications in Python. You will also learn how to measure the impact of async programming in practical scenarios, profile and debug asynchronous code, and evaluate performance improvements using real-world metrics. The final chapters focus on applying async techniques to common cloud-based systems, such as web frameworks, database interactions, and data-pipelines tools. Designed for developers looking to apply async programming with confidence, this book blends real-world examples with core concepts to help you write efficient, maintainable Python code.
Table of Contents (14 chapters)
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12
Other Books You May Enjoy
13
Index

Handling exceptions in asynchronous code

The basic rules of Python exception handling in synchronous code are identical in asynchronous 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, which 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 shows an alternative implementation which offers some improvements:

import asyncio
import aiohttp
BASE_URL = https://ponyapi.net/v1/character/
def get_data(person):
try: p = person["data"][0]
        return f'{p[...
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Asynchronous Programming in Python
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