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)

Chapter 2

Why is asynchronous programming more efficient at handling I/O concurrency than multiple processes/threads?

Asynchronous programming is a very effective solution when dealing with I/O concurrency because it allows us to multiplex I/O actions without memory or computing overhead. Alternative solutions that are based on multithreading or multiprocesses require either more CPU either more memory, or even both.

Multithreading hits a limit when 1,000 threads are running concurrently. On heavy workloads, this puts some pressure on the OS scheduler and ends up wasting a lot of CPU resources due to contention.

Multiprocess solutions face the same problem, but also require more memory because the address space of the program is allocated for each instance of the program. Some of this memory is shared between these instances (such as the code sections), but a big part has to be...