Book Image

Reactive Programming in Kotlin

By : Rivu Chakraborty
Book Image

Reactive Programming in Kotlin

By: Rivu Chakraborty

Overview of this book

In today's app-driven era, when programs are asynchronous, and responsiveness is so vital, reactive programming can help you write code that's more reliable, easier to scale, and better-performing. Reactive programming is revolutionary. With this practical book, Kotlin developers will first learn how to view problems in the reactive way, and then build programs that leverage the best features of this exciting new programming paradigm. You will begin with the general concepts of Reactive programming and then gradually move on to working with asynchronous data streams. You will dive into advanced techniques such as manipulating time in data-flow, customizing operators and provider and how to use the concurrency model to control asynchronicity of code and process event handlers effectively. You will then be introduced to functional reactive programming and will learn to apply FRP in practical use cases in Kotlin. This book will also take you one step forward by introducing you to Spring 5 and Spring Boot 2 using Kotlin. By the end of the book, you will be able to build real-world applications with reactive user interfaces as well as you'll learn to implement reactive programming paradigms in Android.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Dedication
Preface

Learning Buffer, Throttle, and Window operators


So far, we have learned about backpressure. We slowed down the source, dropped items, or used buffer, which will hold items until the consumer consumes it; however, will all these suffice? While handling backpressure at the downstream is not a good solution always, we cannot always slow down the source as well.

While using Observable.interval/Flowable.interval, you cannot slow down the source. A stop gap could be some operators that would somehow allow us to process the emissions simultaneously.

There are the three operators that could help us in that way:

  • Buffer
  • Throttle
  • Window

The buffer() operator

Unlike the onBackPressureBuffer() operator, which buffers emissions until the consumer consumes, the buffer() operator will gather emissions as a batch and will emit them as a list or any other collection type.

So, let's look at this example:

    fun main(args: Array<String>) { 
      val flowable = Flowable.range(1,111)//(1) 
      flowable.buffer...