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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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5
Concurrency and Parallelism in RxPY
In Progress | 0 / 5 sections completed | 0%
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10
Testing and Debugging
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15
Other Books You May Enjoy
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Summary

This chapter described how to deal with two issues that can happen when writing an asynchronous application: dealing with CPU-intensive tasks and dealing with blocking tasks. Solutions to both problems can be handled via schedulers and two operators: subscribe_on and observe_on. Schedulers are objects that allow us to control on which execution context the ReactiveX operators will run. A chain of operators can use as many different execution contexts as needed.

Using schedulers allows us to keep a synchronous-like code style. With their API, it is possible to execute each operator of a chain on different threads. ReactiveX and RxPY provide a very developer-friendly syntax that makes multithreading easier to use than with most other libraries and frameworks.

The three schedulers that have been detailed in the second part of this chapter are the only ones that should be...

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