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)

Chapter 6. Too Many Sources - Combining Observables

In the last chapter, we learned how we can use different operators to deal with the backpressure problem, learned two different strategies to deal with this problem:

  • Lossy strategy
  • Loss-less strategy

For each strategy, we learned different operators implement a lossy strategy to deal with the back pressure problem. learned the following operators:

  • throttle()
  • sample()
  • debounce()
  • pause()

To implement a loss-less strategy to deal with the back pressure problem we learned the following operators:

  • bufferWithCount()
  • bufferWithTime()
  • bufferWithTimeOrCount()
  • pause() using buffering
  • controlled()

We also learned when to use each strategy, based on the amount of memory we have available and if we can afford losing any data or not.

For the last, we learned new operators to filter data, along with the filter() operator. This operator lets us avoid computation of unnecessary events on an observable. We learned the following operators to filter data:

  • first()
  • take()
  • takeLast...