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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
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Index

Working with data pipelines

To work with asynchronous data pipelines, we are going to use one of the many open-source libraries that handles steps and orchestration behind the scenes. Our main goal is not to provide custom implementations of the filter-pipe architecture but rather to use it to demonstrate how asynchronous programming can be applied to some steps even though the whole business case might require a combination of both synchronous and asynchronous solutions.

A suitable use case for asynchronous data pipelines

In our case, we want to find the word that has the largest number of related words in any language; to do so we must process a Parquet file that contains a structured, comprehensive, multilingual public etymology dataset, etymology being the study of the origin and historical development of words. For example, the word algorithm is derived from words in four languages (Middle English, Anglo-Norman, Medieval Latin and Arabic). The following graph shows you...

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Asynchronous Programming in Python
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