So, functional programming wants you to distribute your programming logic into small pieces of reusable declarative small and pure functions. Distributing your logic into small pieces of code will make the code modular and non-complex, thus you will be able to refactor/change any module/part of the code at any given point without any effects to other modules.

Functional programming requires some interfaces and support from the language, thus we can't say any language is functional unless it gives some sort of support to implement functional programming. However, functional programming isn't something new; it is actually quite an old concept and has several languages supporting it. We call those languages functional programming languages, and the following is a list of some of the most popular functional programming languages:

• Lisp
...

Functional programming consists of few new concepts such as lambdas, pure functions, high-order functions, function types, and inline functions, which we will be learning. Quite interesting, isn't it?

Lambda or lambda expressions generally mean anonymous functions, that is, functions without names. You can also say a lambda expression is a function, but not every function is a lambda expression. Not every programming language provides support for lambda expressions, for instance, Java...

So, now, after trying to understand the ReactiveCalculator class from the previous chapter, we will try to optimize the code as well. Let's first take a look at the init block of the ReactiveCalculator class:

    init{ 
      nums = Pair(a,b) 
 
      subjectAdd.map({ it.first+it.second }).subscribe({println
      ("Add = $it")} )//1 
      subjectSub.map({ it.first-it.second }).subscribe({println
      ("Substract = $it")} ) 
      subjectMult.map({ it.first*it.second }).subscribe
      ({println("Multiply = $it")} ) 
      subjectDiv.map({ it.first/(it.second*1.0) }).subscribe
      ({println("Divide = $it")} ) 
 
      subjectCalc.subscribe({ 
        with(it) { 
          calculateAddition() 
          calculateSubstraction() 
          calculateMultiplication() 
     ...

Path breaking and, probably, the most exciting feature in Kotlin are coroutines. They are a new way to write asynchronous, non-blocking code somewhere like the threads, but way more simple, efficient, and lightweight. Coroutines were added in Kotlin 1.1 and are still experimental, so think before using it in production.

In the later chapters of this book, you'll learn about Schedulers in RxKotlin, which encapsulates the complexities of threading, but you can use it only in RxKotlin chain, while you can use coroutines anywhere and everywhere. That is indeed a path-breaking feature of Kotlin. They provide a great abstraction on threads, making context changes and concurrency easier.

Keep in mind that RxKotlin does not use coroutines yet; the reason is quite simple–both coroutines and Schedulers in RxKotlin share nearly the same internal architecture; while...

Functional programming is incomplete without monads. If you are into functional programming, then you know it very well; otherwise, you are hearing it for the first time. So, what is a monad? Let's learn about it. The concept of monad is quite abstract; the definition says monad is a structure that creates a new type by encapsulating a value and adding some extra functionalities to it. So, let's start by using a monad; take a look at the following program:

    fun main(args: Array<String>) { 
      val maybeValue: Maybe<Int> = Maybe.just(14)//1 
      maybeValue.subscribeBy(//2 
        onComplete = {println("Completed Empty")}, 
        onError = {println("Error $it")}, 
        onSuccess = { println("Completed with value $it")} 
      ) 
      val maybeEmpty:Maybe<Int> = Maybe.empty...