Načela funkcionalnog programiranja u Javascriptu

Nakon dugog učenja i rada s objektno orijentiranim programiranjem, vratio sam se korak unatrag razmišljajući o složenosti sustava.

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

Istražujući, otkrio sam koncepte funkcionalnog programiranja poput nepromjenjivosti i čistih funkcija. Ti koncepti omogućuju vam izgradnju funkcija bez nuspojava, pa je lakše održavati sustave - uz neke druge prednosti.

U ovom postu reći ću vam više o funkcionalnom programiranju i nekim važnim konceptima, s puno primjera koda u JavaScript-u.

Što je funkcionalno programiranje?

Funkcionalno programiranje je programska paradigma - stil izgradnje strukture i elemenata računalnih programa - koji računanje tretira kao procjenu matematičkih funkcija i izbjegava promjene stanja i promjenjive podatke - Wikipedia

Čiste funkcije

Prvi temeljni koncept koji učimo kada želimo razumjeti funkcionalno programiranje su čiste funkcije . Ali što to zapravo znači? Što čini funkciju čistom?

Pa kako znati da li funkcija jest pureili nije? Evo vrlo stroge definicije čistoće:

  • Vraća isti rezultat ako se daju isti argumenti (naziva se i deterministic)
  • Ne uzrokuje uočljive nuspojave

Vraća isti rezultat ako se daju isti argumenti

Zamislite da želimo implementirati funkciju koja izračunava površinu kruga. Nečista funkcija primila bi radiuskao parametar, a zatim izračunala radius * radius * PI:

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

Zašto je ovo nečista funkcija? Jednostavno zato što koristi globalni objekt koji funkciji nije proslijeđen kao parametar.

Sad zamislite da neki matematičari tvrde da je PIvrijednost zapravo 42i mijenjaju vrijednost globalnog objekta.

Naša nečista funkcija sada će rezultirati 10 * 10 * 42= 4200. Za isti parametar ( radius = 10) imamo drugačiji rezultat.

Popravimo!

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

Sada ćemo PIfunkciju uvijek prosljeđivati kao parametar. Dakle, sada samo pristupamo parametrima prosljeđenim funkciji. Ne external object.

  • Za parametre radius = 10i PI = 3.14uvijek ćemo imati isti rezultat:314.0
  • Za parametre radius = 10i PI = 42uvijek ćemo imati isti rezultat:4200

Čitanje datoteka

Ako naša funkcija čita vanjske datoteke, to nije čista funkcija - sadržaj datoteke se može promijeniti.

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

Generiranje slučajnih brojeva

Bilo koja funkcija koja se oslanja na generator slučajnih brojeva ne može biti čista.

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

Ne uzrokuje uočljive nuspojave

Primjeri uočljivih nuspojava uključuju izmjenu globalnog objekta ili parametra koji se prosljeđuje referencom.

Sada želimo implementirati funkciju za primanje cijele vrijednosti i vraćanje vrijednosti povećane za 1.

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

Mi imamo countervrijednost. Naša nečista funkcija prima tu vrijednost i ponovno dodjeljuje brojač s vrijednosti povećanom za 1.

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

Promatranje : u funkcionalnom programiranju ne mijenja se promjenjivost.

Modificiramo globalni objekt. Ali kako bismo uspjeli pure? Samo vratite vrijednost povećanu za 1.

Pogledajte da naša čista funkcija increaseCountervraća 2, ali countervrijednost je i dalje ista. Funkcija vraća uvećanu vrijednost bez promjene vrijednosti varijable.

Ako slijedimo ova dva jednostavna pravila, postaje lakše razumjeti naše programe. Sada je svaka funkcija izolirana i ne može utjecati na druge dijelove našeg sustava.

Čiste funkcije su stabilne, dosljedne i predvidljive. S obzirom na iste parametre, čiste funkcije uvijek će vratiti isti rezultat. Ne trebamo razmišljati o situacijama kada isti parametar ima različite rezultate - jer se to nikada neće dogoditi.

Prednosti čiste funkcije

Kod je definitivno lakše testirati. Ne trebamo se ničemu rugati. Tako možemo jedinstveno testirati čiste funkcije u različitim kontekstima:

  • S obzirom na parametar A→ očekujte da funkcija vrati vrijednostB
  • S obzirom na parametar C→ očekujte da funkcija vrati vrijednostD

Jednostavan primjer bila bi funkcija koja prima zbirku brojeva i očekuje da će povećati svaki element ove zbirke.

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

Primamo numbersniz koji koristimo mapza povećanje svakog broja i vraćamo novi popis uvećanih brojeva.

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

Za input[1, 2, 3, 4, 5], očekivano outputbi bilo [2, 3, 4, 5, 6].

Nepromjenljivost

Nepromjenjiva tijekom vremena ili se ne može promijeniti.

Kada su podaci nepromjenjivi, njihovistanje se ne može promijenitinakon što je stvoren.Ako želite promijeniti nepromjenjivi objekt, ne možete. Umjesto toga,izradite novi objekt s novom vrijednošću.

U JavaScriptu obično koristimo forpetlju. Sljedeća forizjava sadrži neke promjenjive varijable.

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

Za svaku iteraciju mijenjamo stanje ii sumOfValuestanje. Ali kako se nositi s promjenjivošću u iteraciji? Rekurzija.

 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.

mapMetoda pretvara kolekciju primjenom funkciju svim svojim elementima i izgradnju nove kolekcije s vraćene vrijednosti.

Uzmimo istu peoplekolekciju gore. Ne želimo sada filtrirati prema "starijima". Samo želimo popis nizova, otprilike TK is 26 years old. Tako bi konačni niz mogao biti :name is :age years oldgdje su :namei :ageatributi svakog elementa u peoplezbirci.

Na imperativni JavaScript način, to bi bilo:

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'] 

Na deklarativni JavaScript način, to bi bilo:

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']

Cijela je ideja transformirati zadani niz u novi niz.

Još jedan zanimljiv problem Hacker Ranka bio je problem s popisom ažuriranja. Samo želimo ažurirati vrijednosti datog niza njihovim apsolutnim vrijednostima.

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.