Book Image

Mastering Reactive JavaScript

By : Erich de Souza Oliveira
Book Image

Mastering Reactive JavaScript

By: Erich de Souza Oliveira

Overview of this book

If you’re struggling to handle a large amount of data and don’t know how to improve your code readability, then reactive programming is the right solution for you. It lets you describe how your code behaves when changes happen and makes it easier to deal with real-time data. This book will teach you what reactive programming is, and how you can use it to write better applications. The book starts with the basics of reactive programming, what Reactive Extensions is, and how can you use it in JavaScript along with some reactive code using Bacon. Next, you’ll discover what an Observable and an Observer are and when to use them.You'll also find out how you can query data through operators, and how to use schedulers to react to changes. Moving on, you’ll explore the RxJs API, be introduced to the problem of data traffic (backpressure), and see how you can mitigate it. You’ll also learn about other important operators that can help improve your code readability, and you’ll see how to use transducers to compose operators. At the end of the book, you’ll get hands-on experience of using RxJs, and will create a real-time web chat using RxJs on the client and server, providing you with the complete package to master RxJs.
Table of Contents (11 chapters)

Common strategies to deal with backpressure

In RxJS we have several operators which can be used to mitigate the backpressure problem, they belong to two different family of strategies the lossy and the loss-less strategies. We will see each one in detail in this chapter.

Lossy strategies to deal with backpressure

Lossy strategies to deal with backpressure are the ones we must use when we want to discard some data. As we discussed, you might not be interested in all the movements from your user mouse.


Lossy strategies let you mitigate the problem of backpressure using constant memory, as we don't keep any buffers.

The throttle() operator

The first lossy technique to deal with backpressure is the throttle() operator. This operator lets you propagate the elements emitted by the observable at a certain interval.

This operator is perfect for implementing rate limiting, such as the problem of showing tweets as fast as a human can read.

The throttle() operator has the following signature: