Funktionelle programmeringsprincipper i Javascript

Efter lang tid at lære og arbejde med objektorienteret programmering tog jeg et skridt tilbage for at tænke på systemets kompleksitet.

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

Ved at undersøge noget fandt jeg funktionelle programmeringskoncepter som uforanderlighed og rene funktioner. Disse koncepter giver dig mulighed for at opbygge bivirkningsfrie funktioner, så det er lettere at vedligeholde systemer - med nogle andre fordele.

I dette indlæg vil jeg fortælle dig mere om funktionel programmering og nogle vigtige begreber med mange kodeeksempler i JavaScript.

Hvad er funktionel programmering?

Funktionel programmering er et programmeringsparadigme - en stil til opbygning af strukturen og elementerne i computerprogrammer - der behandler beregning som evaluering af matematiske funktioner og undgår skiftende tilstand og ændrede data - Wikipedia

Rene funktioner

Det første grundlæggende koncept, vi lærer, når vi vil forstå funktionel programmering, er rene funktioner . Men hvad betyder det egentlig? Hvad gør en funktion ren?

Så hvordan ved vi, om en funktion er pureeller ej? Her er en meget streng definition af renhed:

  • Det returnerer det samme resultat, hvis det gives de samme argumenter (det kaldes også deterministic)
  • Det forårsager ingen observerbare bivirkninger

Det returnerer det samme resultat, hvis de får de samme argumenter

Forestil dig, at vi vil implementere en funktion, der beregner arealet af en cirkel. En uren funktion modtager radiussom parameter og beregner derefter radius * radius * PI:

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

Hvorfor er dette en uren funktion? Simpelthen fordi det bruger et globalt objekt, der ikke blev sendt som en parameter til funktionen.

Forestil dig nu, at nogle matematikere hævder, at PIværdien faktisk er 42og ændrer værdien af ​​det globale objekt.

Vores urene funktion vil nu resultere i 10 * 10 * 42= 4200. For den samme parameter ( radius = 10) har vi et andet resultat.

Lad os ordne det!

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

Nu sender vi altid værdien af PIsom en parameter til funktionen. Så nu har vi bare adgang til parametre, der sendes til funktionen. Nej external object.

  • For parametrene radius = 10og PI = 3.14vil vi altid have det samme resultatet:314.0
  • For parametrene radius = 10og PI = 42vil vi altid have det samme resultatet:4200

Læsning af filer

Hvis vores funktion læser eksterne filer, er det ikke en ren funktion - filens indhold kan ændre sig.

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

Tilfældig talgenerering

Enhver funktion, der er afhængig af en tilfældig talgenerator, kan ikke være ren.

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

Det forårsager ingen observerbare bivirkninger

Eksempler på observerbare bivirkninger inkluderer ændring af et globalt objekt eller en parameter, der sendes som reference.

Nu vil vi implementere en funktion for at modtage en heltal og returnere værdien øget med 1.

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

Vi har counterværdien. Vores urene funktion modtager den værdi og tildeler tælleren igen med værdien øget med 1.

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

Observation : mutabilitet frarådes i funktionel programmering.

Vi ændrer det globale objekt. Men hvordan skulle vi klare det pure? Bare returner værdien steget med 1.

Se at vores rene funktion increaseCounterreturnerer 2, men counterværdien er stadig den samme. Funktionen returnerer den inkrementerede værdi uden at ændre variabelens værdi.

Hvis vi følger disse to enkle regler, bliver det lettere at forstå vores programmer. Nu er hver funktion isoleret og ude af stand til at påvirke andre dele af vores system.

Rene funktioner er stabile, konsistente og forudsigelige. Med de samme parametre vil rene funktioner altid returnere det samme resultat. Vi behøver ikke at tænke på situationer, hvor den samme parameter har forskellige resultater - fordi det aldrig vil ske.

Fordele ved rene funktioner

Koden er bestemt lettere at teste. Vi behøver ikke at spotte noget. Så vi kan teste rene funktioner med forskellige sammenhænge:

  • Givet en parameter A→ forvent, at funktionen returnerer værdiB
  • Givet en parameter C→ forvent, at funktionen returnerer værdiD

Et simpelt eksempel ville være en funktion til at modtage en samling af numre og forvente, at den forøger hvert element i denne samling.

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

Vi modtager numbersarrayet, bruges maptil at forøge hvert nummer og returnere en ny liste med inkrementerede numre.

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

For det input[1, 2, 3, 4, 5]forventede outputville være [2, 3, 4, 5, 6].

Uforanderlighed

Uændret over tid eller kan ikke ændres.

Når data er uforanderlige, er dettilstand kan ikke ændre sigefter det er oprettet.Hvis du vil ændre et uforanderligt objekt, kan du ikke. I stedet,du opretter et nyt objekt med den nye værdi.

In JavaScript we commonly use the for loop. This next for statement has some mutable variables.

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

For each iteration, we are changing the i and the sumOfValue state. But how do we handle mutability in iteration? Recursion.

 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.

Den mapFremgangsmåden transformerer en samling ved at anvende en funktion til alle dens elementer og opbygge en ny samling fra de returnerede værdier.

Lad os få den samme peoplesamling ovenfor. Vi ønsker ikke at filtrere efter "over alder" nu. Vi vil bare have en liste over strenge, noget lignende TK is 26 years old. Så den sidste streng kan være :name is :age years oldhvor :nameog :ageer attributter fra hvert element i peoplesamlingen.

På en tvingende JavaScript-måde ville det være:

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'] 

På en deklarativ JavaScript-måde ville det være:

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']

Hele ideen er at omdanne et givet array til et nyt array.

Et andet interessant Hacker Rank-problem var problemet med opdateringslisten. Vi vil bare opdatere værdierne for et givet array med deres absolutte værdier.

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!

Hej folk, jeg håber, du havde det sjovt ved at læse dette indlæg, og jeg håber, du har lært meget her! Dette var mit forsøg på at dele det, jeg lærer.

Her er arkivet med alle koder fra denne artikel.

Kom og lær med mig. Jeg deler ressourcer og min kode i dette Learning Functional Programming repository.

Jeg skrev også et FP-indlæg, men brugte hovedsageligt Clojure

Jeg håber, du så noget nyttigt for dig her. Og vi ses næste gang! :)

Min Twitter & Github.

TK.