Benefits of Functional Programming


Functional Programming is a paradigm where we generally construct and structure our code using functions. These functions take input which is named as arguments then display the output created on the inputs being taken which, set the same input at all times results in the same output.

Functional programming is a type of pattern in computer science in which programs are created strictly through functions. All is done with the support of functions in Functional Programming and the basic building blocks are functions only.

Functional programming has enlarged quite a bit of power in modern years among the development community. That is typically due to the benefits it makes available. In this post, we will come to be a deep thoughtful of functional programming and its benefits.


Functional programming is an abstraction to solve real-world complex problems in an easy and operative manner. The software industry was completely dependent on functional programming before the object-oriented programming years. This pattern upset the software industry for a couple of decades.

Functional programming doesn’t permit any mutation nor shared state. Under this paradigm, functions should remain pure and true to their expression. It is declarative relatively than imperative or procedural.

Functional Programming was just a group of functions that don’t permit outer scope nor mutation of objects. Somewhat then we mutate arrays by adding or removing inside the scope to attain a solution similar to OOP make sure of. Functional is pure, direct, and doesn’t mutate. It offers an answer to its smart function in an open manner.

Features of Functional Programming

The main features of functional programming are:

Data is immutable:

The data which is existent inside the functions are immutable. We may easily make a new Data structure then can’t modify the existing one in Functional programming. We can at all times insert functions inside a function without any restrictions present.

function executeFunctions(x, y) { 

const add = (x, y) => x + y;  

const subtract = (x, y) => x — y;  

console.log(`sum: ${add(x,y)}`);  

console.log(`difference: ${subtract(x,y)}`);}

Higher-Order Functions

It is a function that becomes a function as an argument. It can or cannot return a function as its resulting output.

function greaterThan(n) {
   return x => x > n;
}let greaterThanTwo = greaterThan(2);console.log(greaterThanTwo(5));


This is one of the most imperative features of functional programming. This assists us to break down a big project into simpler modules. These modules may be tested distinctly which supports us to decrease the time spent on unit testing and debugging.

Function Composition is a performance of composing or creating functions that let us further make simpler and compress our functions by taking functions as an argument and returning an output. It can also return another function as its output other than numerical or string values.

var compose = (f, g) => (x) => f(g(x));

Benefits of Functional Programming

Resolve problems well in a simpler way:

Functional Programming is a Pure Function. This means that it won’t be copying any data outside of its scope. It only cares about what’s on within its scope. It provides an output based on its input and nothing else.

For example, pure functions make sure that the state of the outer side program isn’t changed. They are firmly well to write than impure functions where possible. However, in some cases, an impure function is needed. For example, if we require to make a network call, relate with a database, or print data to the console.

It’s spotless, open, and concise

Functional Programming has at all times been open. Therefore, it’s easy to spot some discrepancies and debug some bugs in the function. Actually, building functions are cleaner and easier to maintain than building a class as in OOP. Ever since we need to think in terms of imperative or procedural patterns. For example, scheming class hierarchies, encapsulation, inheritance, methods, and polymorphism.

It leads to fewer bugs

This is quite a bit easier to debug and write code with pure functions for getting fewer bugs. An easy stack trace or print statement at every level would reveal the problem because each function is simply a mapping of inputs to outputs. We could have shared or mutable state nearly anywhere else in the codebase that’s potentially causing the bug with imperative paradigms.

Function signatures are more reliable

Look at the following Go code:

var radius = 2.0

func areaOfCircle() float64 {

    return 3.14 * radius * radius


On condition that everywhere we calculate the area of a circle we first update the global radius variable and then get the correct output. Theoretically, this code will work.  The difficulty is that inspecting the signature of the function doesn’t provide the whole story. A pure function would have a signature that tells us all we need to know about its usage.

// we don't know which circle's area is being computed, there's no explicit input

func areaOfCircle() float64

We may fix the problem by creation the function pure:

func areaOfCircle(radius float64) float64 {

    return 3.14 * radius * radius


Immutable variables lead to fewer side-effects

The data inner side functional program’s functions are immutable. We may always make new variables and data structures . However, we aren’t permitted to modify the existing values.

It’s easy to read the code and control what the value is at any given point if every value in a program is only assigned once. It may be quite a bit tougher to see what’s going on at diverse lines in the code if as an alternative the named values are permitted to be updated. Immutability creates programs greatly simpler and improves developer speed.