Asynchronous Programming in Python: Apply asyncio in Python to build scalable, high-performance apps across multiple scenarios

Nicolas Bohorquez

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eBook Nov 2025 202 pages 1st Edition

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Nicolas Bohorquez

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Asynchronous Programming in Python

Summary

In this chapter we have informally introduced several key terms and concepts. In the next chapter we will go deep into actual Python constructs for multiprocessing and multithreading, including problems in which those techniques bring more value.We learned an intuitive way about how to distinguish synchronous and asynchronous solutions to well-defined problems. Then we translated those ideas into standard computer science terminology. This will allow us to go deeper into the particulars of each concept as we focus on specific coding solutions. In Chapter 2 we will embark on the practical approach by coding and comparing the multiprocessing and multithreading solutions for a vanilla implementation of a CPU-intensive problem.

Technical requirements

In this chapter we will show different solutions to a common problem. To run the code, which is available in the Chapter02 folder of the Github repo at https://github.com/PacktPublishing/Asynchronous-Programming-in-Python, we recommend creating a new virtual environment inside the folder and then running each Python file to check its output. No external packages are required but this way you can isolate your execution from the common Python environment of your computer:

$ python3 -m venv .env
$ source .env/bin/activate

Detecting embarrassingly parallel workloads

Applying a simple function over a loop is a good example of a so-called embarrassingly parallel workload, which means that an easy optimization that can result in improved performance is to use multithreading or multiprocessing techniques to speed up the calculations. More broadly, the basic criterion you should check before trying to implement a parallelization optimization is that the sub-units of work making up your task are completely independent. In the previous example, the distance calculation for cities A and B is completely independent of the B to C case. You can find a simple example of an embarrassingly parallel workload in the repository file Chapter 2/embarrassingly.py, the code being a simple math operation:

import time
import multiprocessing
def square_number(n):
    time.sleep(0.001)
    return n * n
def serial_square_list(numbers):
    return [square_number(n) for n in numbers]
def parallel_square_list(numbers, num_processes...

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Key benefits

Understand core principles and theory behind async programming in Python
Measure the impact of async techniques in practical, real-world use cases
Apply async patterns in software design and data-oriented architectures

Description

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.

Who is this book for?

This book will help Python developers who want to understand and apply the asynchronous programming model in application development, data analysis, and orchestration scenarios. Junior developers, data engineers,, and tech leads will also benefit from the application design examples.