Functional Programming — A Brief Introduction

One wise man once said to me when I asked him as to what functional programming is :

Once you understand it, you lose the ability to explain it to others

In one line it is a way of programming with functions as building blocks rather than objects as is the case with Object oriented programming

What is it?

  • It is another programming paradigm
  • It treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data
  • It is a declarative programming paradigm, which means programming is done with expressions or declarations instead of statements
  • It has origins in lambda calculus created in 1930s by Alonzo Church
  • It is not something new. Even Lisp supported functional programming

Major characteristics (buzzwords) of functional programming

There are a multiple buzzwords associated with functional programming, whose only purpose is to make it sound very cool and difficult to grasp

  • Immutability — The state of a variable never changes
  • Pure Functions — For a valid input there is a valid output
  • No Side effects — Function does not change the input in any way
  • Higher Order Functions — Functions can be assigned to a variable, passed in another function and also used as a return type of a function
  • Types — Type is like a superset of classes in OOP paradigm
  • Composition — functions can be chained.
  • Currying — Extension to composition. One function can take only 1 parameter at a time
  • Lazy evaluation — expressions can be evaluated later from time of declaration
  • Referential transparency — A function can be replaced by evaluated result and program works the same

Seeing them one at a time

Pure functions

Every function takes a valid input and must return a valid output. They only work on their input parameters and nothing else

var z = 10; 
int add(x,y) {
return x + y;
}

This is a pure function. It does not touch any variable outside its parameters. Also check another property of this function. No matter how many times you call this function. It will return same output for same input set of x and y. There is another important advantage of pure functions.

There is consistency

Another pure function can be

int getFive() { return 5; }

This was pure as well. Let’ see another case

int z; 
void addWithNoReturn(x, y) {
z = x + y
}

This is not a pure function as it does not return anything valuable, and also has a side effect as it modified variable z. So another important property of pure function is

Pure functions can have no side effect

Now, you are thinking, what about functions like

addToFile(filename)
updateTable(sql)
writeToSocket(packet)

Yes, they are all impure. And, we still need them. Functional Languages cannot eliminate Side Effects, they can only confine them. So all your side effects should be intentional and easier to debug. The goal is to curtail and also segregate side-effects from rest of the program.

Immutability

There is no variable in functional programming but only constants even though we call them variable for historic reason.

This really is the most difficult concept to grasp for most of us, who are used to imperative style of programming. Most of us are used to writing:

int x = 1;
x++; //or x = x+1;

This is a very common usage in most programming language. However, if you remember algebra x can never be equal to x+1. So functional programming makes it illegal to do x++. As long as x is in scope, its value cannot change. So how do we change a variable here. Well there are two ways

  1. Multi-valued changes (like changing value at one particular index in an array) : It makes a copy of the record with values changed wherever needed. The important things is that it does it efficiently. For ex., certain libraries like Immutable.js treat array as a collection of pointers and copy then is just a matter of changing few pointers.
  2. Single Valued change (like counter variables in loop) : Here too it makes a copy of it with value changed. One more thing to note here is functional programming avoid loops altogether

For ex: In imperative style, if you have to sum an array

sum = 0; 
for (int i = 0 ; i< array.size() ;i++) {
sum += array[i];
}
return sum;

As you see here we are mutating both i and sum. One way to eliminate this is recursion

int sum(int start, int end, List arr, int value) { 
if end <= start { return value; }
return sum(start+1, end, arr, value + arr[start])
}

Also in some function languages like ECMAScript 6 or even in java 8, you have some functions like map and reduce, which can help eliminate looping.

For ex: to sum

sum = list.reduce((a,b) => a+b, 0)

Here reduce takes first parameter as a function which takes 2 params, an accumulator (a) and a array element (b). Second parameter of reduce is the initial value of accumulator. Let's say you wanted to take the sum of square of each number in a list

sum = list.map((i) => i*i).reduce((a,b) => a+b, 0)

Higher Order Functions

This simply means functions can act as other values. Pass it around to different functions or even use it as a return value of another function. Many languages do not support this directly but there has been ways wherein we managed to pass around functions. Like in C/C++, we have function pointers, while in Java we can use a single function interface as a means to same end. However, functional languages provide direct support of using functions as values. Let's see an example in Scala

scala> var changeAndAdd = (x:Int ,y: Int, fn : Int=>Int) => fn(x) + fn(y) 
changeAndAdd: (Int, Int, Int => Int) => Int = $$Lambda$3989/1721733575@1eb71177 
scala> changeAndAdd(2,3, (x:Int) => x*x)

res14: Int = 13
scala> changeAndAdd(2,3, (x:Int) => x*x*x) 
res15: Int = 35

As you can see we are passing a function as 3rd parameter to changeAndAdd. Let's see another example

scala> def sayHi(prefix: String) = (s : String) => prefix+ " " +s
sayHi: (prefix: String)String => String 
scala> var hi = sayHi("Hi!") 
hi: String => String = $$Lambda$3993/1737394043@2f13b8a1 
scala> var hello = sayHi("Hello") 
hello: String => String = $$Lambda$3993/1737394043@5c45930c 
scala> hi("there") 
res19: String = Hi! there 
scala> hello("there")
res20: String = Hello there

Here sayHi returns a function which takes a string and returns a string

Composition

We have already seen some examples of composition in previous section. In Functional Programming, functions are our building blocks which are written to do very specific tasks. One way we can promote reuse is by treating small functions as bricks to build a wall and then use this walls to build our castle. In theory this way of combining smaller functions to achieve bigger functionality is the essence of composition. Let’s see a small example. We have two functions

isEven = value => value%2 = 0 // Returns if a number is even 
isGreaterThan = (value, check) => value > check //return true if value is greater than check 
square = value => value*value

Now let’s see if we have a list and we wanted to get a list which has square of all even numbers in list

newList = list.filter(isEven).map(square).collect()

Now, let’s say we wanted to only have values is are even as well as greater than 10 to be in the list

newList2 = list.filter(isEven).filter(isGreaterThan(10)).map(square).collect()

That’s it. Let’s see another from Javascript

var sum = a b => a+b 
var square = x => x*x

Now let’s say I wanted a function which adds 3 numbers

var s3 = a b c => a + sum(b,c)

Or, let’s say I wanted a function to return (a+b)²

var sumThenSquare = a b => square(sum(a,b))

So rather than creating two new functions we have reused the existing function to create new functions. That is cool.

Currying

Currying in layman’s term is ability for a function which takes more than one parameter to only take one parameter at a time. This makes compositions much cleaner and easier to do. Different languages take different approaches to it. In some languages you have to rewrite the functions to use only one parameter. Like in javascript ECMAScript6

//Two param way 
var add = x y =>x+y
//One param way 
var addCurried = x => y => x+y 
add(2,3) // returns 5 
addCurried(2)(3) // returns 5

In scala there is a way to pass _ as one param to convert any function to a curried function

scala> def multiply(m: Int, n: Int): Int = m * n 
multiply: (m: Int, n: Int)Int 
scala> multiply(2,2) 
res8: Int = 4 
scala> var timesTwo = multiply(2, _:Int) //curried function 
timesTwo: Int => Int = $$Lambda$3979/1419571728@26a23216 
scala> timesTwo(4) 
res9: Int = 8

Referential transparency

It basically means that a function call can easily be replaced by a expression value without impacting the result. This is property of every pure function

Types

A type system is a syntactic method for automatically checking the absence of certain erroneous behaviors by classifying program phrases according to the kinds of values they compute. Type is like a finite or infinite set in which an input and output can belong to. Even functions can be types in this paradigm. Mathematically speaking , for ex:

f: R -> N

this tells us that function “f” maps values from the set of real numbers to values of the set of natural numbers. Given these annotations, the compiler can now statically (at compile time) verify that the program is sound. That is, compilation will fail if values (at runtime) will not comply to the constraints imposed by the program. Let’s see an example function (pseudocode)

float divide(int number, int divisor) { return number/divisor }

This looks fine. Until we call divide(7,0). So now to handle this case

float divide(int number, int divisor) throws Exception { 
if dividor = 0 {
throw SomeException
}
return number/divisor
}

Now this works, but this is not a pure function. Remember, pure functions returns a valid output for a valid input, and in this case the function tells any int is a valid input but it is throwing an exception if we pass 0. So, how to do we handle it. How about we create a new type, which contains all non-zero numbers and use that as a type for divisor. Some languages do provide you such a type, else you can create a class which does not allow to create an object with 0. Then this function remains pure.

Advantages of functional programming

  • Cleaner code (in most instances)
  • Less bolierplate code. As you see most of your looping over a list is removed.
  • Easy to test
  • Easy to handle concurrency (Immutability, i.e, absence of state and use of pure function means less use of mutexes and semaphores)
  • Makes implementation of Lazy evaluation easy
  • It makes you sound smart :)

Disadvantages of functional programming

  • Side effects are compulsory on many occasions and in trying to reduce it sometimes we end up complicating simple things.
  • It is difficult to grasp as unlike objects, this is not very inherently understood.
  • Lazy evaluation can make performance calibration difficult.
  • Debugging can be difficult, as we tend to chain function calls. This makes it difficult to put breakpoints and then second line of investigation is putting logs everywhere
  • Functional languages tend to be CPU/Memory intensive as copying is needed to provide immutability. This makes it unsuitable for High performance software like games or browsers.
  • Lots of hyperbole surrounds functional programming. At times a simple for loop could handle situation better but still people make it unreadable by chains of function calls

Popular functional languages

  • Haskell
  • Scala
  • Elm
  • Erlang
  • Kotlin (More of OO with some functional support)
  • Java 8 (partial support)
  • Javascript (with certain libraries)

You can even do functional programming in Go, Python, PHP or even Java 7 using workarounds here and there.

References/Further Reading


Originally published at gist.github.com.

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