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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

Composing operators with transformer

So, you have learned how to create custom operators, but think of a situation when you want to create a new operator by combining multiple operators. For instance, I often wanted to combine the functionality of the subscribeOn and observeOn operators so that all the computations can be pushed to computation threads, and, when the results are ready, we can receive them on the main thread.

Yes, it's possible to get the benefits of both operators by adding both operators one after the other to the chain, as shown here:

    fun main(args: Array<String>) { 
      Observable.range(1,10) 
        .map { 
           println("map - ${Thread.currentThread().name} $it") 
           it 
         } 
         .subscribeOn(Schedulers.computation()) 
         .observeOn(Schedulers.io()) 
         .subscribe { 
            println(&quot...
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