Monad

7 minute read

In this blog we will talk about Monad which is the core concept of FP. We won’t touch any category theory which belong to mathematics. We will try to find out the motivation of Monad from code level.

Question

Let’s start from a very simple question, how do you implement the following formula in Scala?

Obviously we need four functions here

def sqrt(v:Double):Double = 
  if (v <0) throw new Exception("sqrt should not less than 0") else Math.sqrt(v)

def div(numerator:Double, denominator:Double):Double =
  if (denominator == 0) throw new Exception("denominator should not be zero") else numerator/denominator

def sum(left:Double, right:Double):Double = left + right

def sub(left:Double, right:Double):Double = left - right

Then we can implement the main function like this

def calculation:Unit = {
    val x1 = readDouble
    val x2 = readDouble
    val x3 = readDouble
    val x4 = readDouble
    val x5 = readDouble

    val divResult1: Double = div(x1, x2)

    val divResult2: Double = div(divResult1, x3)

    val sumResult: Double = sum(divResult2, x4)

    val sqrtResult: Double = sqrt(sumResult)

    val subResult: Double = sub(sqrtResult, x5)
    
    println(subResult)
}

Let’s do some test

@ calculation
1
1
2
3
4
-2.1291713066130296

@ calculation
1
1
0
2
3
java.lang.Exception: denominator should not be zero
  ammonite.$sess.cmd11$.div(cmd11.sc:2)
  ammonite.$sess.cmd14$.calculation(cmd14.sc:7)
  ammonite.$sess.cmd16$.<init>(cmd16.sc:1)
  ammonite.$sess.cmd16$.<clinit>(cmd16.sc)

@ calculation
1
1
2
-4
5
java.lang.Exception: sqrt should not less than 0
  ammonite.$sess.cmd10$.sqrt(cmd10.sc:2)
  ammonite.$sess.cmd14$.calculation(cmd14.sc:7)
  ammonite.$sess.cmd17$.<init>(cmd17.sc:1)
  ammonite.$sess.cmd17$.<clinit>(cmd17.sc)

The last two test cases throw execptions, they are caused by div and sqrt which are not pure functions.

Then we get anther question, how do we make these functions pure?

Solution

In Algebraic Data Type, we already talked about how to make a function pure.

Let’s follow that steps to make div and sqrt pure.

  1. Find out what effect the function send to outside

    These two functions send two effects to outside

    • The result of calculation
    • Exception when the input parameter is invalid

  2. For each effect, create a corresponding Product Type for it

     case class Normal(v: Double)
 case class InvalidInput(error: String)

  3. To unify the type of different effects, create Sum Type to give these effects a common parent type

     sealed trait Data
 case class Normal(v: Double) extends Data
 case class InvalidInput(error: String) extends Data

  4. Refactor the function to use the Algebraic Data Type as return type

     def sqrt(v:Double):Data = 
   if (v <0) InvalidInput("sqrt should not less than 0") else Normal(Math.sqrt(v))

 def div(numerator:Double, denominator:Double):Data =
   if (denominator == 0) InvalidInput("denominator should not be zero") else Normal(numerator/denominator)

Then we can refactor the main function using pure div and sqrt like this

def calculation:Unit = {
    val x1 = readDouble
    val x2 = readDouble
    val x3 = readDouble
    val x4 = readDouble
    val x5 = readDouble
    
    val divResult1: Data = div(x1,x2)

    val divResult2: Data = divResult1 match {
      case Normal(x) => div(x, x3)
      case InvalidInput(e) => InvalidInput(e)
    }

    val sumResult: Data = divResult2 match {
      case Normal(x) => Normal(sum(x, x4))
      case InvalidInput(e) => InvalidInput(e)
    }

    val sqrtResult: Data = sumResult match {
      case Normal(x) => sqrt(x)
      case InvalidInput(e) => InvalidInput(e)
    }

    val subResult: Data = sqrtResult match {
      case Normal(x) => Normal(sub(x, x5))
      case InvalidInput(e) => InvalidInput(e)
    }

    subResult match {
      case Normal(x) => println(x)
      case InvalidInput(e) => throw new Exception(e)
    }
}

Please recall what we said in What is Functional Programming?

Functional Programming means construct most parts of programs using only pure function and centralize the parts with Side-Effect(usually we will put them in main function)

So except the last expression and readDouble in calculation, our program is pure, we answered the question.

Now let’s see if we can make our code more clean and easy to be maintained.

Refactor

To be honest, comparing with the original code, the FP code is ugly for me. There are so many pattern-matching, seems I need the following code everywhere

case Normal(x) => ???
case InvalidInput(e) => InvalidInput(e)

Let’s see if we can remove the duplication.

Except the expression with Side-Effect, there are 4 expressions use it and the only different part is the branch of Normal

//divResult2
case Normal(x) => div(x, x3)

//sumResult
case Normal(x) => Normal(sum(x, x4))

//sqrtResult
case Normal(x) => sqrt(x)

//subResult
case Normal(x) => Normal(sub(x, x5))

sumResult and subResult have similar structure, they all need a function Double => Double. We can pass sum/sub by parameter and extract a common function like this

def computeIfNormal(data:Data)(f:Double=>Double):Data = data match {
    case Normal(v) => Normal(f(v))
    case InvalidInput(e) => InvalidInput(e)
}

divResult2 and sqrtResult have similar structure, they all need a function Double => Data. We can pass div/sqrt by parameter and extract a common function like this

def computeIfNormalForComplexFunction(data:Data)(f:Double=>Data):Data = data match {
    case Normal(v) => f(v)
    case InvalidInput(e) => InvalidInput(e)
}

Then the calculation can be refactored like this

def calculation:Unit = {
    val x1 = readDouble
    val x2 = readDouble
    val x3 = readDouble
    val x4 = readDouble
    
    val divResult1: Data = div(x1,x2)

    val divResult2: Data = computeIfNormalForComplexFunction(divResult1)(x:Double => div(x, x3))

    val sumResult: Data = computeIfNormal(divResult2)(x:Double => sum(x, x4))

    val sqrtResult: Data = computeIfNormalForComplexFunction(sumResult)(sqrt)

    val subResult: Data = computeIfNormal(sqrtResult)(x: Double => sub(x, x4))

    subResult match {
      case Normal(x) => println(x)
      case InvalidInput(e) => throw new Exception(e)
    }
}

Looks better now, but the name of these two common functions are too long, let’s give them a shorter name

def map(data: Data)(f: Double => Double): Data = data match {
    case Normal(v) => Normal(f(v))
    case InvalidInput(e) => InvalidInput(e)
}

def flatMap(data: Data)(f: Double => Data): Data = data match {
    case Normal(v) => f(v)
    case InvalidInput(e) => InvalidInput(e)
}

Then the calculation can be refactored like this

def calculation:Unit = {
    val x1 = readDouble
    val x2 = readDouble
    val x3 = readDouble
    val x4 = readDouble
    
    val divResult1: Data = div(x1,x2)

    val divResult2: Data = flatMap(divResult1)(x:Double => div(x, x3))

    val sumResult: Data = map(divResult2)(x:Double => sum(x, x4))

    val sqrtResult: Data = flatMap(sumResult)(sqrt)

    val subResult: Data = map(sqrtResult)(x: Double => sub(x, x4))

    subResult match {
      case Normal(x) => println(x)
      case InvalidInput(e) => throw new Exception(e)
    }
}

Hmm, there is another minor problem for me.

When we want to invoke map/flatMap, the idea in our mind is to find a way to apply the given function to the target data, we will first get a target data then choose map/flatMap to apply given function.

But our current code use wrong order, it choose map/flatMap first, then pass target data and given function. The order make me uncomfortable, could we change it?

Thanks Implicit, we can do it!!

object Data {
  def map(data: Data)(f: Double=>Double): Data = data match {
      case Normal(v) => Normal(f(v))
      case InvalidInput(e) => InvalidInput(e)
  }

  def flatMap(data: Data)(f: Double=>Data): Data = data match {
      case Normal(v) => f(v)
      case InvalidInput(e) => InvalidInput(e)
  }

  implicit DataOps(v: Data){
      def map(f: Double => Double): Data = Data.map(v)(f)
      def flatMap(f: Double => Data): Data = Data.flatMap(v)(f)
  }
}

Then the calculation become

def calculation:Unit = {
    val x1 = readDouble
    val x2 = readDouble
    val x3 = readDouble
    val x4 = readDouble
    
    val divResult1: Data = div(x1,x2)

    val divResult2: Data = divResult1.flatMap(x:Double => div(x, x3))

    val sumResult: Data = divResult2.map(x:Double => sum(x, x4))

    val sqrtResult: Data = sumResult.flatMap(sqrt)

    val subResult: Data = sqrtResult.map(x: Double => sub(x, x4))

    subResult match {
      case Normal(x) => println(x)
      case InvalidInput(e) => throw new Exception(e)
    }
}

Perfect! With minor changes, our program is pure now and looks similar to the original code and easy to understand.

Summary

Let’s review what happened in the last section.

  1. We make a function pure by returning algebraic data type, such as div, sqrt
  2. We can not compose the refactored function with other functions directly(including refactored one and the unchanged one). For example, we can’t compose div with sqrt, sum and sub directly, because it returns Data but other functions accept Double
  3. To do the function compose, we do lots of pattern match on the algebraic data type and we got lots of duplicated code.
  4. These duplicated code have the similar pattern, we extract some common functions to do it, such as computeIfNormal and computeIfNormalForComplexFunction.
  5. To make the code clean, We give these common functions shorter names map and flatMap
  6. To follow the thinking mode of human, we use Implicit to change the syntax of map and flatMap.

So we can give a summary

  1. We use map and flatMap to remove duplication
  2. We use map to compose the unchanged function which doesn’t return the algebraic data type, such as sum, sub.
  3. We use flatMap to compose the refactored function which return the algebraic data type, such as div, sqrt.

Actually we can make the map and flatMap more generic to support the generic algebraic data type, we call the generic version as Monad.

trait Monad[F[_]] {
  def map[A, B](fa: F[A])(f: A => B): F[B]
  def flatMap[A, B](fa: F[A])(f: A => F[B]): F[B]
}

It’s a type class, you can review Type Classes to see why we need a type class for Monad.

Comments

You May Also Enjoy

How to do bug analysis?

4 minute read

What is the purpose of Bug analysis? Some people think it is to classify Bugs, others think it is to prevent Bugs from reoccurring, and some think it is to i...