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Reactive Programming in Kotlin

You're reading from   Reactive Programming in Kotlin Design and build non-blocking, asynchronous Kotlin applications with RXKotlin, Reactor-Kotlin, Android, and Spring

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788473026
Length 322 pages
Edition 1st Edition
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Author (1):
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Rivu Chakraborty Rivu Chakraborty
Author Profile Icon Rivu Chakraborty
Rivu Chakraborty
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Table of Contents (13) Chapters Close

Preface 1. A Short Introduction to Reactive Programming FREE CHAPTER 2. Functional Programming with Kotlin and RxKotlin 3. Observables, Observers, and Subjects 4. Introduction to Backpressure and Flowables 5. Asynchronous Data Operators and Transformations 6. More on Operators and Error Handling 7. Concurrency and Parallel Processing in RxKotlin with Schedulers 8. Testing RxKotlin Applications 9. Resource Management and Extending RxKotlin 10. Introduction to Web Programming with Spring for Kotlin Developers 11. REST APIs with Spring JPA and Hibernate 12. Reactive Kotlin and Android

Understanding backpressure

The only problem with Observable is when an Observer cannot cope with the pace of an Observable. An Observable, by default, chains work by pushing items synchronously to the Observer, one at a time. However, if the observer has to perform some time-consuming computations, this may take longer than the interval of each item emission of Observable. Confused? Let's consider this example:

    fun main(args: Array<String>) { 
      val observable = Observable.just(1,2,3,4,5,6,7,8,9)//(1) 
      val subject = BehaviorSubject.create<Int>() 
      subject.observeOn(Schedulers.computation())//(2) 
            .subscribe({//(3) 
                println("Subs 1 Received $it") 
                runBlocking { delay(200) }//(4) 
            }) 
 
            subject.observeOn(Schedulers.computation())//(5) 
            .subscribe({//(6) ...
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