Principes de programmation fonctionnelle en Javascript

Après avoir longtemps appris et travaillé avec la programmation orientée objet, j'ai pris du recul pour réfléchir à la complexité du système.

“Complexity is anything that makes software hard to understand or to modify."- John Outerhout

En faisant quelques recherches, j'ai trouvé des concepts de programmation fonctionnelle comme l'immuabilité et les fonctions pures. Ces concepts vous permettent de créer des fonctions sans effets secondaires, ce qui facilite la maintenance des systèmes, avec d'autres avantages.

Dans cet article, je vais vous en dire plus sur la programmation fonctionnelle et quelques concepts importants, avec de nombreux exemples de code en JavaScript.

Qu'est-ce que la programmation fonctionnelle?

La programmation fonctionnelle est un paradigme de programmation - un style de construction de la structure et des éléments des programmes informatiques - qui traite le calcul comme l'évaluation de fonctions mathématiques et évite les données changeantes et mutables - Wikipedia

Fonctions pures

Le premier concept fondamental que nous apprenons lorsque nous voulons comprendre la programmation fonctionnelle est celui des fonctions pures . Mais qu'est-ce que cela signifie réellement? Qu'est-ce qui rend une fonction pure?

Alors, comment savoir si une fonction l'est pureou non? Voici une définition très stricte de la pureté:

  • Il renvoie le même résultat si on lui donne les mêmes arguments (il est également appelé deterministic)
  • Il ne provoque aucun effet secondaire observable

Il renvoie le même résultat si on lui donne les mêmes arguments

Imaginez que nous voulons implémenter une fonction qui calcule l'aire d'un cercle. Une fonction impure recevrait radiuscomme paramètre, puis calculerait radius * radius * PI:

let PI = 3.14; const calculateArea = (radius) => radius * radius * PI; calculateArea(10); // returns 314.0

Pourquoi est-ce une fonction impure? Tout simplement parce qu'il utilise un objet global qui n'a pas été passé en paramètre à la fonction.

Imaginez maintenant que certains mathématiciens soutiennent que la PIvaleur est en fait 42et modifient la valeur de l'objet global.

Notre fonction impure aboutira maintenant à 10 * 10 * 42= 4200. Pour le même paramètre ( radius = 10), nous avons un résultat différent.

Fixons-le!

let PI = 3.14; const calculateArea = (radius, pi) => radius * radius * pi; calculateArea(10, PI); // returns 314.0

Maintenant, nous allons toujours passer la valeur de en PItant que paramètre à la fonction. Alors maintenant, nous accédons simplement aux paramètres passés à la fonction. Non external object.

  • Pour les paramètres radius = 10et PI = 3.14, nous aurons toujours le même résultat:314.0
  • Pour les paramètres radius = 10et PI = 42, nous aurons toujours le même résultat:4200

Lecture de fichiers

Si notre fonction lit des fichiers externes, ce n'est pas une fonction pure - le contenu du fichier peut changer.

const charactersCounter = (text) => `Character count: ${text.length}`; function analyzeFile(filename) { let fileContent = open(filename); return charactersCounter(fileContent); }

Génération de nombres aléatoires

Toute fonction qui repose sur un générateur de nombres aléatoires ne peut pas être pure.

function yearEndEvaluation() { if (Math.random() > 0.5) { return "You get a raise!"; } else { return "Better luck next year!"; } }

Il ne provoque aucun effet secondaire observable

Des exemples d'effets secondaires observables incluent la modification d'un objet global ou d'un paramètre passé par référence.

Nous voulons maintenant implémenter une fonction pour recevoir une valeur entière et renvoyer la valeur augmentée de 1.

let counter = 1; function increaseCounter(value) { counter = value + 1; } increaseCounter(counter); console.log(counter); // 2

Nous avons la countervaleur. Notre fonction impure reçoit cette valeur et réassigne le compteur avec la valeur augmentée de 1.

let counter = 1; const increaseCounter = (value) => value + 1; increaseCounter(counter); // 2 console.log(counter); // 1

Observation : la mutabilité est déconseillée en programmation fonctionnelle.

Nous modifions l'objet global. Mais comment y arriverions-nous pure? Renvoyez simplement la valeur augmentée de 1.

Voyez que notre fonction pure increaseCounterrenvoie 2, mais la countervaleur est toujours la même. La fonction renvoie la valeur incrémentée sans modifier la valeur de la variable.

Si nous suivons ces deux règles simples, il devient plus facile de comprendre nos programmes. Désormais, chaque fonction est isolée et incapable d'avoir un impact sur d'autres parties de notre système.

Les fonctions pures sont stables, cohérentes et prévisibles. Étant donné les mêmes paramètres, les fonctions pures renverront toujours le même résultat. Nous n'avons pas besoin de penser à des situations où le même paramètre a des résultats différents - car cela ne se produira jamais.

Avantages des fonctions pures

Le code est nettement plus facile à tester. Nous n'avons pas besoin de nous moquer de quoi que ce soit. Nous pouvons donc tester des fonctions pures dans différents contextes:

  • Étant donné un paramètre A→ attendez-vous à ce que la fonction renvoie une valeurB
  • Étant donné un paramètre C→ attendez-vous à ce que la fonction renvoie une valeurD

Un exemple simple serait une fonction pour recevoir une collection de nombres et s'attendre à ce qu'elle incrémente chaque élément de cette collection.

let list = [1, 2, 3, 4, 5]; const incrementNumbers = (list) => list.map(number => number + 1);

Nous recevons le numberstableau, utilisons mappour incrémenter chaque nombre et renvoyons une nouvelle liste de nombres incrémentés.

incrementNumbers(list); // [2, 3, 4, 5, 6]

Pour le input[1, 2, 3, 4, 5], l'attendu outputserait [2, 3, 4, 5, 6].

Immutabilité

Inchangé dans le temps ou impossible à modifier.

Lorsque les données sont immuables, leurl'état ne peut pas changeraprès sa création.Si vous souhaitez modifier un objet immuable, vous ne pouvez pas. Au lieu,vous créez un nouvel objet avec la nouvelle valeur.

En JavaScript, nous utilisons couramment la forboucle. Cette forinstruction suivante a quelques variables mutables.

var values = [1, 2, 3, 4, 5]; var sumOfValues = 0; for (var i = 0; i < values.length; i++) { sumOfValues += values[i]; } sumOfValues // 15

Pour chaque itération, nous modifions le iet l' sumOfValueétat. Mais comment gérer la mutabilité dans l'itération? La récursivité.

 let list = [1, 2, 3, 4, 5]; let accumulator = 0; function sum(list, accumulator) { if (list.length == 0) { return accumulator; } return sum(list.slice(1), accumulator + list[0]); } sum(list, accumulator); // 15 list; // [1, 2, 3, 4, 5] accumulator; // 0

So here we have the sum function that receives a vector of numerical values. The function calls itself until we get the list empty (our recursion base case). For each "iteration" we will add the value to the total accumulator.

With recursion, we keep our variablesimmutable. The list and the accumulator variables are not changed. It keeps the same value.

Observation: We can use reduce to implement this function. We will cover this in the higher order functions topic.

It is also very common to build up the final state of an object. Imagine we have a string, and we want to transform this string into a url slug.

In Object Oriented Programming in Ruby, we would create a class, let’s say, UrlSlugify. And this class will have a slugify method to transform the string input into a url slug.

class UrlSlugify attr_reader :text def initialize(text) @text = text end def slugify! text.downcase! text.strip! text.gsub!(' ', '-') end end UrlSlugify.new(' I will be a url slug ').slugify! # "i-will-be-a-url-slug"

It’s implemented!

Here we have imperative programming saying exactly what we want to do in each slugify process — first lower-case, then remove useless white spaces and, finally, replace remaining white spaces with hyphens.

But we are mutating the input state in this process.

We can handle this mutation by doing function composition, or function chaining. In other words, the result of a function will be used as an input for the next function, without modifying the original input string.

const string = " I will be a url slug "; const slugify = string => string .toLowerCase() .trim() .split(" ") .join("-"); slugify(string); // i-will-be-a-url-slug

Here we have:

  • toLowerCase: converts the string to all lower case
  • trim: removes white-space from both ends of a string
  • split and join: replaces all instances of match with replacement in a given string

We combine all these 4 functions and we can "slugify" our string.

Referential transparency

Let’s implement a square function:

const square = (n) => n * n;

This pure function will always have the same output, given the same input.

square(2); // 4 square(2); // 4 square(2); // 4 // ...

Passing 2 as a parameter of the square function will always returns 4. So now we can replace the square(2) with 4. Our function is referentially transparent.

Basically, if a function consistently yields the same result for the same input, it is referentially transparent.

pure functions + immutable data = referential transparency

With this concept, a cool thing we can do is to memoize the function. Imagine we have this function:

const sum = (a, b) => a + b;

And we call it with these parameters:

sum(3, sum(5, 8));

The sum(5, 8) equals 13. This function will always result in 13. So we can do this:

sum(3, 13);

And this expression will always result in 16. We can replace the entire expression with a numerical constant and memoize it.

Functions as first-class entities

The idea of functions as first-class entities is that functions are also treated as values and used as data.

Functions as first-class entities can:

  • refer to it from constants and variables
  • pass it as a parameter to other functions
  • return it as result from other functions

The idea is to treat functions as values and pass functions like data. This way we can combine different functions to create new functions with new behavior.

Imagine we have a function that sums two values and then doubles the value. Something like this:

const doubleSum = (a, b) => (a + b) * 2;

Now a function that subtracts values and the returns the double:

const doubleSubtraction = (a, b) => (a - b) * 2;

These functions have similar logic, but the difference is the operators functions. If we can treat functions as values and pass these as arguments, we can build a function that receives the operator function and use it inside our function.

const sum = (a, b) => a + b; const subtraction = (a, b) => a - b; const doubleOperator = (f, a, b) => f(a, b) * 2; doubleOperator(sum, 3, 1); // 8 doubleOperator(subtraction, 3, 1); // 4

Now we have an f argument, and use it to process a and b. We passed the sum and subtraction functions to compose with the doubleOperator function and create a new behavior.

Higher-order functions

When we talk about higher-order functions, we mean a function that either:

  • takes one or more functions as arguments, or
  • returns a function as its result

The doubleOperator function we implemented above is a higher-order function because it takes an operator function as an argument and uses it.

You’ve probably already heard about filter, map, and reduce. Let's take a look at these.

Filter

Given a collection, we want to filter by an attribute. The filter function expects a true or false value to determine if the element should or should not be included in the result collection. Basically, if the callback expression is true, the filter function will include the element in the result collection. Otherwise, it will not.

A simple example is when we have a collection of integers and we want only the even numbers.

Imperative approach

An imperative way to do it with JavaScript is to:

  • create an empty array evenNumbers
  • iterate over the numbers array
  • push the even numbers to the evenNumbers array
var numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; var evenNumbers = []; for (var i = 0; i < numbers.length; i++) { if (numbers[i] % 2 == 0) { evenNumbers.push(numbers[i]); } } console.log(evenNumbers); // (6) [0, 2, 4, 6, 8, 10]

We can also use the filter higher order function to receive the even function, and return a list of even numbers:

const even = n => n % 2 == 0; const listOfNumbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; listOfNumbers.filter(even); // [0, 2, 4, 6, 8, 10]

One interesting problem I solved on Hacker Rank FP Path was the Filter Array problem. The problem idea is to filter a given array of integers and output only those values that are less than a specified value X.

An imperative JavaScript solution to this problem is something like:

var filterArray = function(x, coll) { var resultArray = []; for (var i = 0; i < coll.length; i++) { if (coll[i] < x) { resultArray.push(coll[i]); } } return resultArray; } console.log(filterArray(3, [10, 9, 8, 2, 7, 5, 1, 3, 0])); // (3) [2, 1, 0]

We say exactly what our function needs to do — iterate over the collection, compare the collection current item with x, and push this element to the resultArray if it pass the condition.

Declarative approach

But we want a more declarative way to solve this problem, and using the filter higher order function as well.

A declarative JavaScript solution would be something like this:

function smaller(number) { return number < this; } function filterArray(x, listOfNumbers) { return listOfNumbers.filter(smaller, x); } let numbers = [10, 9, 8, 2, 7, 5, 1, 3, 0]; filterArray(3, numbers); // [2, 1, 0]

Using this in the smaller function seems a bit strange in the first place, but is easy to understand.

this will be the second parameter in the filter function. In this case, 3 (the x) is represented by this. That's it.

We can also do this with maps. Imagine we have a map of people with their name and age.

let people = [ { name: "TK", age: 26 }, { name: "Kaio", age: 10 }, { name: "Kazumi", age: 30 } ];

And we want to filter only people over a specified value of age, in this example people who are more than 21 years old.

const olderThan21 = person => person.age > 21; const overAge = people => people.filter(olderThan21); overAge(people); // [{ name: 'TK', age: 26 }, { name: 'Kazumi', age: 30 }]

Summary of code:

  • we have a list of people (with name and age).
  • we have a function olderThan21. In this case, for each person in people array, we want to access the age and see if it is older than 21.
  • we filter all people based on this function.

Map

The idea of map is to transform a collection.

La mapméthode transforme une collection en appliquant une fonction à tous ses éléments et en créant une nouvelle collection à partir des valeurs renvoyées.

Obtenons la même peoplecollection ci-dessus. Nous ne voulons pas filtrer par «sur l'âge» maintenant. Nous voulons juste une liste de chaînes, quelque chose comme TK is 26 years old. Ainsi, la chaîne finale pourrait être :name is :age years old:nameet :agesont les attributs de chaque élément de la peoplecollection.

De manière impérative JavaScript, ce serait:

var people = [ { name: "TK", age: 26 }, { name: "Kaio", age: 10 }, { name: "Kazumi", age: 30 } ]; var peopleSentences = []; for (var i = 0; i < people.length; i++) { var sentence = people[i].name + " is " + people[i].age + " years old"; peopleSentences.push(sentence); } console.log(peopleSentences); // ['TK is 26 years old', 'Kaio is 10 years old', 'Kazumi is 30 years old'] 

De manière déclarative JavaScript, ce serait:

const makeSentence = (person) => `${person.name} is ${person.age} years old`; const peopleSentences = (people) => people.map(makeSentence); peopleSentences(people); // ['TK is 26 years old', 'Kaio is 10 years old', 'Kazumi is 30 years old']

L'idée est de transformer un tableau donné en un nouveau tableau.

Un autre problème intéressant de Hacker Rank était le problème de la liste de mise à jour. Nous voulons simplement mettre à jour les valeurs d'un tableau donné avec leurs valeurs absolues.

For example, the input [1, 2, 3, -4, 5]needs the output to be [1, 2, 3, 4, 5]. The absolute value of -4 is 4.

A simple solution would be an in-place update for each collection value.

var values = [1, 2, 3, -4, 5]; for (var i = 0; i < values.length; i++) { values[i] = Math.abs(values[i]); } console.log(values); // [1, 2, 3, 4, 5]

We use the Math.abs function to transform the value into its absolute value, and do the in-place update.

This is not a functional way to implement this solution.

First, we learned about immutability. We know how immutability is important to make our functions more consistent and predictable. The idea is to build a new collection with all absolute values.

Second, why not use map here to "transform" all data?

My first idea was to test the Math.abs function to handle only one value.

Math.abs(-1); // 1 Math.abs(1); // 1 Math.abs(-2); // 2 Math.abs(2); // 2

We want to transform each value into a positive value (the absolute value).

Now that we know how to do absolute for one value, we can use this function to pass as an argument to the map function. Do you remember that a higher order function can receive a function as an argument and use it? Yes, map can do it!

let values = [1, 2, 3, -4, 5]; const updateListMap = (values) => values.map(Math.abs); updateListMap(values); // [1, 2, 3, 4, 5]

Wow. So beautiful!

Reduce

The idea of reduce is to receive a function and a collection, and return a value created by combining the items.

A common example people talk about is to get the total amount of an order. Imagine you were at a shopping website. You’ve added Product 1, Product 2, Product 3, and Product 4 to your shopping cart (order). Now we want to calculate the total amount of the shopping cart.

In imperative way, we would iterate the order list and sum each product amount to the total amount.

var orders = [ { productTitle: "Product 1", amount: 10 }, { productTitle: "Product 2", amount: 30 }, { productTitle: "Product 3", amount: 20 }, { productTitle: "Product 4", amount: 60 } ]; var totalAmount = 0; for (var i = 0; i < orders.length; i++) { totalAmount += orders[i].amount; } console.log(totalAmount); // 120

Using reduce, we can build a function to handle the amount sum and pass it as an argument to the reduce function.

let shoppingCart = [ { productTitle: "Product 1", amount: 10 }, { productTitle: "Product 2", amount: 30 }, { productTitle: "Product 3", amount: 20 }, { productTitle: "Product 4", amount: 60 } ]; const sumAmount = (currentTotalAmount, order) => currentTotalAmount + order.amount; const getTotalAmount = (shoppingCart) => shoppingCart.reduce(sumAmount, 0); getTotalAmount(shoppingCart); // 120

Here we have shoppingCart, the function sumAmount that receives the current currentTotalAmount , and the order object to sum them.

The getTotalAmount function is used to reduce the shoppingCart by using the sumAmount and starting from 0.

Another way to get the total amount is to compose map and reduce. What do I mean by that? We can use map to transform the shoppingCart into a collection of amount values, and then just use the reduce function with sumAmount function.

const getAmount = (order) => order.amount; const sumAmount = (acc, amount) => acc + amount; function getTotalAmount(shoppingCart) { return shoppingCart .map(getAmount) .reduce(sumAmount, 0); } getTotalAmount(shoppingCart); // 120

The getAmount receives the product object and returns only the amount value. So what we have here is [10, 30, 20, 60]. And then the reduce combines all items by adding up. Beautiful!

We took a look at how each higher order function works. I want to show you an example of how we can compose all three functions in a simple example.

Talking about shopping cart, imagine we have this list of products in our order:

let shoppingCart = [ { productTitle: "Functional Programming", type: "books", amount: 10 }, { productTitle: "Kindle", type: "eletronics", amount: 30 }, { productTitle: "Shoes", type: "fashion", amount: 20 }, { productTitle: "Clean Code", type: "books", amount: 60 } ]

We want the total amount of all books in our shopping cart. Simple as that. The algorithm?

  • filter by book type
  • transform the shopping cart into a collection of amount using map
  • combine all items by adding them up with reduce
let shoppingCart = [ { productTitle: "Functional Programming", type: "books", amount: 10 }, { productTitle: "Kindle", type: "eletronics", amount: 30 }, { productTitle: "Shoes", type: "fashion", amount: 20 }, { productTitle: "Clean Code", type: "books", amount: 60 } ] const byBooks = (order) => order.type == "books"; const getAmount = (order) => order.amount; const sumAmount = (acc, amount) => acc + amount; function getTotalAmount(shoppingCart) { return shoppingCart .filter(byBooks) .map(getAmount) .reduce(sumAmount, 0); } getTotalAmount(shoppingCart); // 70

Done!

Resources

I’ve organised some resources I read and studied. I’m sharing the ones that I found really interesting. For more resources, visit my Functional Programming Github repository

  • EcmaScript 6 course by Wes Bos
  • JavaScript by OneMonth
  • Ruby specific resources
  • Javascript specific resources
  • Clojure specific resources
  • Learn React by building an App

Intros

  • Learning FP in JS
  • Intro do FP with Python
  • Overview of FP
  • A quick intro to functional JS
  • What is FP?
  • Functional Programming Jargon

Pure functions

  • What is a pure function?
  • Pure Functional Programming 1
  • Pure Functional Programming 2

Immutable data

  • Immutable DS for functional programming
  • Why shared mutable state is the root of all evil

Higher-order functions

  • Eloquent JS: Higher Order Functions
  • Fun fun function Filter
  • Fun fun function Map
  • Fun fun function Basic Reduce
  • Fun fun function Advanced Reduce
  • Clojure Higher Order Functions
  • Purely Function Filter
  • Purely Functional Map
  • Purely Functional Reduce

Declarative Programming

  • Declarative Programming vs Imperative

That’s it!

Hey people, I hope you had fun reading this post, and I hope you learned a lot here! This was my attempt to share what I’m learning.

Here is the repository with all codes from this article.

Come learn with me. I’m sharing resources and my code in this Learning Functional Programming repository.

I also wrote an FP post but using mainly Clojure

I hope you saw something useful to you here. And see you next time! :)

My Twitter & Github.

TK.