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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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Index

Understanding data pipelines

A data pipeline is a mechanism to handle three types of operation over data: extraction, transformation, and load. Extraction is the process of obtaining raw data from a source, i.e. data that hasn’t been processed previously. Imagine extracting images from a camera, recordings from sensors in the wild, or text from comments on a website.

The transformation process takes data points and applies functions to clean (filter), enrich, validate, change or project the original raw data in order to make it processable in business applications. The transformations can be handled sequentially or in parallel, and might involve long and complex computational operations or small incremental steps. Most importantly, transformations must handle edge cases and exceptions in a consistent manner, because raw data is usually messy.

Load operations, the third type of operation common in data pipelines, involve a series of steps to connect and store data to...

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