Book Image

Hands-On Reactive Programming with Python

By : Romain Picard
Book Image

Hands-On Reactive Programming with Python

By: Romain 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)

Summary

Having read this chapter, you should now be able to test your code, and debug it when issues are found.

This chapter described how testing can be done, in a way that is fully automated so that it can be integrated in continuous integration systems. The Python standard library is a solid foundation on which RxPY and asynchronous code can be tested rather easily. You should also understand why pure functions are easier to test than side-effects: they can be fully tested by using dependency injection, which is easier to use than stubs or mocks.

Logging is a subject in-between testing and debugging. It can be useful during the initial testing phases, or when regressions are detected. One major benefit of ReactiveX here is that, once a logging infrastructure is in place, then it becomes easy to configure it dynamically.

Finally, debugging is the dark side of ReactiveX, with...