Maksim I

4. Type classes and ADTs

Another important thing in functional programming is type-classes and algebraic data types. This is what makes functional programming so flexible and abstract by providing a convenient way of using ad-hoc polymorphism. Actually, we already dealt with it in the previous article when talked about the functors.

4.1 Type classes

Type class is a pattern used to achieve ad-hoc polymorphism. In general, it allows you to create generic type for provided list of operations, associated with a particular type, and this type is determined by parametric polymorphism.

Sounds complex, but let’s see with examples. Here’s an example of a function that compares two strings:

const eqString = (a: string, b: string) => a === b;

Okay, that’s work. But what if we want to create more complex types?

type Student = { name: string, score: number };

const eqStudents = (a: Student, b: Student) => a.score === b.score && ===;
const eqStudentsScore = (a: Student, b: Student) => a.score === b.score;

How can we generalize such an operation? We can create a type for it!

type Equals = <T>(a: T, b: T) => boolean;

Seems okay. But, what if we want not just to compare whether they are equal, but to additionally determine if one of them is greater or less? So, we would need to have two functions: equals and compare.

const eqStudentsScore = (a: Student, b: Student) => a.score === b.score;
const compareStudentsScore = (a: Student, b: Student) => a.score === b.score
  ? 0
  : a.score > b.score
    ? 1
    : -1;

These two functions are interconnected, but in the current implementation, they are separate, which in some cases is not convenient.

And here are the type-classes that come into the game! We can create a type constructor for such operations. Let’s start with equality:

// In some languages and mathematics it's called Setoid
interface Eq<T> {
  equals(a: T, b: T): boolean;

const eqNumber: Eq<number> = {
  equals: (a, b) => a === b,

For now, the benefits are not so obvious. Let’s proceed and build comparing:

enum Ordering {
  LESS = -1,
  EQUALS = 0,
  GREATER = 1,

interface Ord<T> extends Eq<T> {
  compare: (a: T, b: T) => Ordering,

const byStudentScore: Ord<Student> = {
  compare: (a, b) => a.score === b.score
    ? Ordering.EQUALS
    : a.score > b.score
      ? Ordering.GREATER
      : Ordering.LESS,
  equals: (a, b) => a.score === b.score,

Great! But such a way of creating new instances of Ord is exhausting, and thanks to a such simple abstraction of Ord, we can handle it. Let’s create a contramap utility (Contravariant Functor).

Try in playground: Listing 4.1.8 Creating Ord with contramap constructor

const ordNumber: Ord<number> = {
  compare: (a, b) => a === b
    ? Ordering.EQUALS
    : a > b
      ? Ordering.GREATER
      : Ordering.LESS,
  equals: (a, b) => a === b,

// You pass the function for transforming A to B, and then Ord<B>. Now you have Ord<A>
const contramap = <A, B>(f: (a: A) => B) => (ord: Ord<B>): Ord<A> => ({
  equals: (a, b) => ord.equals(f(a), f(b)),
  compare: (a, b) =>, f(b)),

const byStudentScore: Ord<Student> = contramap(
  (student) => student.score // function to map Student -> number
)(ordNumber); // Ord for comparing two numbers

const students = [
  { name: 'John', score: 10 },
  { name: 'Jane', score: 5 },
  { name: 'Jack', score: 7 },
const sorted = [...students].sort(;

Some info about contramap: It’s a contravariant of When map makes F<A> -> F<B> transformation using morphism A -> B, contramap makes F<A> -> F<B> transformation using morphism B -> A. Here’s a very demonstrative example of how contravariance works: stackoverflow.

And this is only a small part of the huge number of possibilities that these abstractions provide.

We already worked with another type-class in the previous chapter - Functors, it has only one operator: map, but you can create from it more complex abstractions, like Monad.

You can check the full list of type-classes: (it’s called there an algebraic structure, it’s another concept from math, just a non-empty set with some operations and laws).

Do not be afraid if you don't understand it, this is a specification for library creators, not educational purposes.

Type classes are pretty similar to the Strategy pattern from OOP (that we will explore later). Some algorithms are put inside one class and form a family, where distinct instances of different classes of the same strategy are interchangeable.

4.2 Algebraic data types

By definition, an algebraic data type is a composite data type formed by combining other types using sum or product techniques.

The product type is just a bounded combination of types, for example, Record or Tuple. The sum type is also called disjoin union, some combination of different types, like type DisjoinUnion = A | B

type Product = { a: string; b: number };
type Sum = string | number;
type ADTs = Product & Sum;

It’s pretty simple. And from here, we might want to combine polymorphism of type-classes and ADTs to achieve huge flexibility. For example, let’s take the disjoin union of Value | null, and transform it into Functor. How can we do that?

type None = {
  _type: 'None',

type Some<A> = {
  _type: 'Some',
  value: A,

const isSome = <T>(optional: Maybe<T>): optional is Some<T> => optional._type === 'Some';
const some = <T>(value: T): Some<T> => ({
  _type: 'Some',
const none = {
  _type: 'None'

type Maybe<A> = Some<A> | None;

// @type Functor<Maybe>
const maybeFunctor = {
  map: <A, B>(fa: (a: A) => B) => (m: Maybe<A>): Maybe<B> => (
    isSome(m) ? some(fa(m)) : none

Yeah, we have already seen this before, and now let’s look deeply into it. Here, the Maybe functor is just a casual value | null which we often use, but it is transformed into such a shape, so we can apply it to different type-classes. We can transform it into the Monad, Foldable, Traversable, Apply, Applicative and many other different interesting concepts. You might have a question: “So, is Maybe a type-class or an ADT?” - the answer is “Both”. Type class is a type system, or roughly saying in OOP terms - a pattern. ADT is a composite type, which is formed by combining other types. And we can combine them together to achieve huge flexibility.

4.3 Further reading

Type classes and Algebraic data types are essential, but complex aspects of functional programming and an explanation above can give you only a blurry understanding of them. If you are interested to learn more - we recommend you to read this series of articles

Also, you are ready to learn about another technique for handling side effects that we mentioned in the beginning. It’s the Effect functor! In simple terms, it’s an ADT for eventually computed values.

Try in on sandbox: Listing 4.3.1 - Effect ADT

import { contramap } from 'fp-ts/Ord';
import { Ord as ordNumber } from 'fp-ts/number';
import { pipe } from 'fp-ts/function';
import { sort, tail, filter } from 'fp-ts/Array';

type Effect<A> = {
  run: () => A;

// @type Functor<Effect>
const effectFunctor = {
  map: <A, B>(fa: (a: A) => B) => (m: Effect<A>): Effect<B> => ({
    run: () => fa(,

type DatabaseResponse<V> = { response: V };
type User = { name: string; age: number };

const getUsers = (): Effect<DatabaseResponse<Array<User>>> => ({
  run: () => {
    console.log('performing database request...');
    return {
      response: [
        { name: 'James', age: 23 },
        { name: 'Jhone', age: 14 },

// pipe is just like compose, but left to right and the first argument is an _argument_ which would be passed to the first _function_
const users = pipe(
  getUsers(), // _argument_ => query.response), // _function_

const byAge = pipe(
  contramap((user: User) => user.age),
const oldestUser = pipe(
  users,, // sort array by age, // get first element

const onlyAbove18 = pipe(users, => user.age >= 18)));

So, what is it all about? The trick is that we already have a list of users, an oldestUser, and a list of all users above 18, BUT! No side effect was called! If you run this code and look at the console, there would be no output. Database call would be called only when .run would be called. With this Functor, we can do whatever we want with impure values without even having them. Our code is pure until the moment when we call run.

Such an effect system gives us so much power, but it is not apparent at first glance. It allows us to make programs as values. Every piece of our logic is just a value, and these values are composed into programs, which are values as well, and we can compose these small programs together to create one big program, which is as well pure and predicted. And then, in the end, in an isolated place, it runs. You might see quite a similar concept in React.js (the application would render only after the ReactDOM.render call) and Express.js (the server would start only after the .listen call).

This is a hard topic as well, so if you are interested, we recommend you to read this article.

4.4 New version of the initial example

Now, when we know all the basics of functional programming, let’s rewrite our Listing 1.2 to something more functional, using functors and ADTs. We will use the fp-ts library.

Try in sandbox: Listing 4.4.1 - Rework of Listing 1.2

import * as E from 'fp-ts/Either';
import * as N from 'fp-ts/number';
import * as NEA from 'fp-ts/NonEmptyArray';
import * as A from 'fp-ts/Array';
import { contramap } from 'fp-ts/Ord';
import { pipe, flow } from 'fp-ts/function';

type Student = {
  name: string;
  score: number;
  class: 'A' | 'B'

export const students: Array<Student> = [
  { name: 'Jhon', score: 70, class: 'B' },
  { name: 'James', score: 60, class: 'A' },
  { name: 'Jones', score: 67, class: 'A' },
  { name: 'Raul', score: 55, class: 'A' }

const byStudentScore = pipe(
  contramap((student: Student) => student.score)
const toNonEmptyArray = <L, R>(onEmpty: () => L) => (
  arr: Array<R>
): E.Either<L, NEA.NonEmptyArray<R>> => (
  arr.length === 0 ? E.left(onEmpty()) : E.right(arr as NEA.NonEmptyArray<R>)
const getTailAndHead = <T>(list: NEA.NonEmptyArray<T>): [T, T] => [
  list[list.length - 1]!

export const main = flow(
  toNonEmptyArray<string, Student>(() => 'The list of students is empty!'),
      A.filter((student) => student.class === 'A'),
      toNonEmptyArray(() => 'There are no students from the "A" class')
      (sortedStudents) => (
          // Average score
          sortedStudents.reduce((sum, student) => sum + student.score, 0) / sortedStudents.length,
          // Highest and lowest performers
        ] as const
      ([average, [lowest, highest]]) => `\
Class "A":
The highest score has: ${}, score: ${highest.score}
The lowest score has: ${}, score: ${lowest.score}
An average score is ${average}`


4.5 What’s the point of all of it?

We have learned many things about functional programming, and let’s try to answer this question: Why is FP used in the real world? Why a lot of production companies are using it? What problems does it solve?

Let’s look back at the procedural and OOP programming. It has many conventions, patterns, and principles; using them is the only possible way to write an extensive, growing and maintainable production-grade application. Why do they appear in the first place? Because one of the main problems with OOP - it’s too low-level in the architectural sense. We control every bit of the program; we should decide how every connection would be organised and structured by ourselves. What is the result of it? We have all these MVC, SOLID, DRY, GRASP, YAGNI, KISS, and a large zoo of patterns. Their only purpose is to restrict, structure and formulate how we build applications. It’s too hard to develop something in such a way without extensive experience.

But can functional programming resolve this problem? All these concepts are already baked into it! Let’s elaborate on the most ambitious of them - SOLID.

  1. Single Responsibility - Our functions are pure and referentially transparent; we’re composing functions to create a new one and combining them in type classes. With such a restriction, we cannot even potentially make them do something different than they should.
  2. Open-Closed - We have one function which does only one most primitive thing. We can create new functions via composition. We may have different base-type classes and monoids, and we can derive new ones from them. We never change the existing ones; we only extend them.
  3. Liskov Substitution - The most intriguing one! All this hierarchy of type classes like Monoids, Functors and others allows us to construct programs abstractly. Does my function accept Monoid? I can pass whatever Monoid of any type we want! If our application relies on some Effect Functor, and with time we need to use another one, more performant and async - we can change it at the beginning of the program, and everything would work as expected! Because it would obey the same laws as the previous one.
  4. Interface Segregation - Type classes segregated by their purpose. We might have some function that accepts the parameter of Functor, but with time we also want it to be, for example, Applicative - you’re welcome, extend it, and nothing breaks!
  5. Dependency Inversion - The essence of Functional Programming. We always have absolute control over what is going on during computations. Computational flow is abstract, and every function obeys strict laws, so we can be sure of what it does by looking at its type signature.

Now you can look back on the code examples and concepts we discussed, and you will see that it works! Of course, this course only gives you some theoretics and practice, and it is absolutely not enough to build something valuable in FP languages like Haskell or Scala. But the purpose was to introduce you to the world of FP, to demystify its shape, and to intrigue you to go down deep into it if you found such concepts close to you. If so, here you can see a comprehensive glossary of the most FP terms you can find on your way: