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

Take a practical journey through the core principles, concepts, and applications of asynchronous programming in Python across real-world environments and technologies.

Key Features:

- 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

Book 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.

What You Will Learn:

- Use generators, coroutines and async/await to build scalable Python functions

- Explore event loops to manage concurrency and orchestrate async flow

- Compare concurrency models to choose the right async strategy

- Optimize I/O-intensive programs to improve system throughput and efficiency

- Build async services using real-world APIs and popular Python libraries

- Apply structured concurrency and design patterns for cleaner async design

- Test and debug async Python code to ensure reliability and stability

Who this book is 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.

Table of Contents

- Synchronous and Asynchronous programming paradigms

- Concurrency and Parallelism in Python

- Generators and Coroutines

- Implementing Coroutines with Greenlets, Asyncio and Trio

- Benchmarking: Sync vs Async implementations

- Asynchronous Design Patterns

- Asynchronous programming in Django, FastAPI and Litestar

- Asynchronous data access

- Asynchronous tasks with Airflow

- Asynchronous Jupyter Notebooks