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

Blocking subscribers


Try to remember the code blocks from previous chapters, where we used delay to make the main thread wait whenever we used an Observable or Flowable that operates on a different thread. A perfect example of this scenario is when we used Observable.interval as a factory method or when we used the subscribeOn operator. To get you refreshed, following is such a code example:

    fun main(args: Array<String>) { 
      Observable.range(1,10) 
         .subscribeOn(Schedulers.computation()) 
         .subscribe { 
            item -> println("Received $item") 
          } 
      runBlocking { delay(10) } 
    } 

In this example, we switched to Schedulers.computation for the subscription. Now let's see, how we can test this Observable and check that we received exactly 10 emissions:

    @Test 
    fun `check emissions count` () { 
      val emissionsCount = AtomicInteger()//(1) 
      Observable.range(1,10) 
         .subscribeOn(Schedulers.computation()) 
         .blockingSubscribe...