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Hands-On Reactive Programming with Python

Hands-On Reactive Programming with Python

By : Picard
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Hands-On Reactive Programming with Python

Hands-On Reactive Programming with Python

By: Picard

Overview of this book

Reactive programming is central to many concurrent systems, but it’s famous for its steep learning curve, which makes most developers feel like they're hitting a wall. With this book, you will get to grips with reactive programming by steadily exploring various concepts This hands-on guide gets you started with Reactive Programming (RP) in Python. You will learn abouta the principles and benefits of using RP, which can be leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming (FaRP), observables and observers, and concurrency and parallelism. The book will then take you through the implementation of an audio transcoding server and introduce you to a library that helps in the writing of FaRP code. You will understand how to use third-party services and dynamically reconfigure an application. By the end of the book, you will also have learned how to deploy and scale your applications with Docker and Traefik and explore the significant potential behind the reactive streams concept, and you'll have got to grips with a comprehensive set of best practices.
Table of Contents (16 chapters)
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Concurrency and schedulers

Chapter 2, Asynchronous Programming in Python, explained the principles of concurrency, and its two categories:

  • I/O concurrency
  • CPU concurrency

An asynchronous framework is designed to deal with I/O concurrency by multiplexing all I/O requests on a single process and thread. The AsyncIO framework and ReactiveX are both tools in this category. As such, they are a perfect fit for applications that are I/O bound, such as network-based applications and tasks involving interactions with databases. However, there are situations where a full asynchronous design cannot be applied. This can occur in two cases:

  • When doing CPU-intensive actions
  • When using blocking APIs

Both of them break the behavior of an asynchronous system because they block the event loop for a very long time, and prevent other tasks from executing. On some asynchronous frameworks, handling...

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