In statically typed functional programming, typeclasses are often used in place of interfaces to achieve polymorphism (that is, writing code that works for multiple different types of values). Fantasy-land is a JS standard for algebraic structures, which mostly employs interfaces to achieve reuse. Fp-ts is a popular library that contains the same algebraic structures from fantasy-land, except it's built on typeclasses. Typeclasses are much more extensible and lend themselves to certain type structures that would otherwise be complex to model with interfaces, at the cost of being a bit more verbose & clunky at times.

For example, let's say we wanted to build a function that takes a value, turns it into a string, and logs it. Let's suppose we can't just call JSON.stringify , and we need each object to be able to specify its own logic to achieve stringification.

Using an interface, this would look roughly like:

interface Printable { print ( ) : string } function logValue ( value : Printable ) { console . log ( value . print ( ) ) }

This works fine, but comes with a few limitations. First, it can only be used with values from modules that depend on the Printable interface. For instance, in order to use it with a string value, you'd need to wrap it in an adapter object:

const name = "Paul" logValue ( new StringPrintable ( name ) )

This isn't too bad, but suppose we needed the functionality of two interfaces, we'd need a special adapter which implements both of those interfaces. The problem gets worse as we add interfaces.

const name = "Paul" logValue ( new StringPrintableAndComparable ( name ) )

In Typescript, we encode typeclasses with interfaces, but we separate the value from the method it's implementing. Instead of implementing that interface with our original value, we create a separate value and have that second value implement the interface. Whenever we need to write a function for some value that "implements" the typeclass, we just have our function take two values; the original value, and an instance of a typeclass for that value.

For example, let's build the same logValue function with a typeclass. Our Printable interface becomes a typeclass, and it needs to work for any value, so we introduce a type parameter, A (The type is irrelevant to the definition of Printable , so we keep it concise). Our print function turns from a no-arg function, into a function that takes the value, A , and returns a string.

type Printable < A > = { print ( value : A ) : string }

Our logValue changes to take two values; the original value that we're converting, and an instance of the Printable typeclass for the original value (which will hold the logic for converting). We can use the print method on Printable instance, passing our original value in:

function logValue < T > ( t : T , stringable : Printable < T > ) { console . log ( stringable . print ( t ) ) }

Here, logValue works for any value. We don't need to wrap the value in an adapter layer, and adding capabilities simply consists of adding typeclass parameters:

declare function logValue < T > ( t : T , stringable : Printable < T > , equal : Equal < T > )

Let's take a closer look at that Equal typeclass. In plain Javascript, it's sometimes useful to compare two values. Oftentimes libraries take the shortcut of using the strict equality comparison algorithm in times that it needs to determine if two values are equal. For example, Set s in Javascript determine equality using this algorithm. By default, a React PureComponent will compare props based on strict equality. The reselect library also uses strict equality to determine if parameters are the same. Sometimes this is fine, but other times it's useful to have finer-grained control over how two values are determined to be "equal."

If we were to build this as an interface, it might look something like this:

interface Equal { equals ( other : Equal ) : boolean }

and then a few implementations:

class User implements Equal { constructor ( private id : number ) { } equals ( user : User ) { return user . id === this . id } } class Point implements Equal { constructor ( private x : number , private y : number ) { } equals ( point : Point ) { return this . x === point . x && this . y === point . y } }

However this isn't totally accurate, For example, I can have two different instances of Equal s, but I can still compare them, while satisfying the typechecker:

const user : Equal = new User ( 1 ) const point : Equal = new Point ( 15 , 30 ) user . equals ( point )

To make this more type-safe, we can use a typeclass to model the Equal interface. Instead of building a method that takes a single parameter, we build a method that takes two parameters; the original value to operate on, and an additional parameter to compare it to:

type Equal < A > = { equals ( left : A , right : A ) }

Since the two parameters are fixed on a single type, there's no chance to mess this up. Using this new typeclass, let's build a function that takes in an array of values and removes the duplicates. You may be tempted to just create a Set from the array, but Set uses strict equality checking, which may lead to surprising results:

const users = [ { id : 1 , name : "Bob" } , { id : 2 , name : "Susan" } , { id : 1 , name : "Bob" } ] console . log ( new Set ( users ) )

This logs:

Set { { id: 1, name: 'Bob' }, { id: 2, name: 'Susan' }, { id: 1, name: 'Bob' } }

Oops! We wanted to remove duplicates, but Set can only make decisions based on referential equality.

Let's write a function using our Equal typeclass which implements this functionality correctly:

In order to use this with User values, we simply need to construct an instance of Equal<User> :

const userEq : Equal < User > = { equals ( l , r ) { return l . id === r . id } }

And then use it when invoking removeDupes :

const users = [ { id : 1 , name : "Bob" } , { id : 2 , name : "Susan" } , { id : 1 , name : "Bob" } ] console . log ( removeDupes ( users , userEq ) )

This logs:

[ { id: 1, name: 'Bob' }, { id: 2, name: 'Susan' } ]

which is much closer to what we'd expect.

Using typeclasses brings a slight shift of thinking when building polymorphic functions. Instead of restricting parameters to all subclasses of an interface, you just introduce a type parameter, and then also add a typeclass for the needed functionality, for example:

Instead of working for only subclasses of Talker :

function speak ( talker : Talker ) : string { ... }

typeclasses allow any values that have an instance of the Talker typeclass:

function speak < T > ( talker : T , tc : Talker < T > ) : string { ... }

When using libraries that are built on typeclasses, instead of thinking, "Does this value implement the X interface," it's more useful to think "Is there an instance of typeclass X for this value?"

The Equal typeclass we just wrote exists in fp-ts as Eq . Fp-ts also includes the removeDupes function we wrote, named as uniq (in its Array module). Let's get a feel for using the fp-ts library by using the fp-ts equivalents of the functions we built.

Creating an instance of Eq is as simple as building a value that "implements" the Eq interface. Let's suppose we had a type that models coordinates on a 2d plane as tuples (an X value and a Y value), and we had a list of them:

type Point = [ number , number ] const points : Point [ ] = [ [ 1 , 2 ] , [ 6 , 7 ] , [ 1 , 2 ] ]

Let's say we wanted to remove each duplicate value before processing them further. We'll accomplish this in two steps:

Build an instance of the Eq typeclass for Point s. Use that Eq instance with the uniq function.

All we need to do make the Eq instance is to build a value whose type is Eq<Point> . There's just one method we have to implement, and that's equal(a: Point, b: Point) => boolean :

import { Eq } from 'fp-ts/lib/Eq' const pointEq : Eq < Point > = { equals ( a : Point , b : Point ) { } }

what should the implementation of equals look like?

Click to see equals

The uniq function (unlike our removeDupes function) is curried, and takes the Eq instance first:

function uniq < A > ( E : Eq < A > ) : ( as : Array < A > ) => Array < A >

It then returns a function that takes an array of those values, and returns a filtered version of that array, where equality between elements is determined by the Eq instance.

import { uniq } from 'fp-ts/lib/Array' const filterPoints = uniq ( pointEq ) filterPoints ( points )

Constantly implementing typeclasses for straightforward values like combinations of numbers can quickly become cumbersome. Fp-ts provides useful utilities for building complex typeclasses by composing smaller ones. For instance, there is already an Eq instance which compares numbers, and there is already a function which builds a Eq instance for tuples that contain any value. Let's use these utilities to compose an Eq instance for Point s, rather than handcrafting our own.

import { Eq , getTupleEq , eqNumber } from 'fp-ts/lib/Eq' const pointEq : Eq < Point > = getTupleEq ( eqNumber , eqNumber )

Two tuples are equal only if their contents are equal, so you can't have one typeclass instance for all tuples (since tuples have many different types of values!), `getTupleEq` "solves" this by taking any number of tuple instances for the values inside and constructing one on the fly.

The Monoid typeclass from fp-ts abstracts over how to combine two values.

It contains just two members:

interface Monoid < A > { empty : A ; concat : ( x : A , y : A ) => A ; }

concat encapsulates the logic to combine the values, and empty is a value of the same type that represents a 'zero' value. We can make an instance of Monoid<number> to represent combining two numbers with addition:

const addition : Monoid < number > = { empty : 0 , concat : ( a , b ) => a + b } addition . concat ( 4 , 8 )

What can we do with this now? Let's take a look at a useful method from fp-ts, foldMap.

Let's introduce this method with a thought experiment. Let's suppose you have a list of apples and you have way to combine two apples into one apple. Given these two things, you should be able to take that list and "squash" it into one, taking each apple and combining it with the previous one.

Let's also suppose that you have a list of "bananas" and a way to combine two oranges into one orange. What would you need to combine the list of bananas into a single orange? If you had a way to turn bananas into oranges, you could iterate over the bananas, turning each one into an orange, and then combine them once they're in their orange state.

This is the pattern behind foldMap , except it's generalized, meaning it works for a list of any kind of contents, A , and any kind of combination method for B ( Monoid<B> ), as long as we have a way to turn those A 's into B 's.

Here's the signature for foldMap :

foldMap : < B > ( M : Monoid < B > ) => < A > ( f : ( a : A ) => B ) => ( fa : A [ ] ) => B

It can be broken up into three different parts, let's take it one section at a time:

<B>(M: Monoid<B>) , which returns a: <A>(f: (a: A) => B) , which returns a (fa: A[]) => B

This function allows us to take an array of values of type A , a monoid for values of type B , and a function which turns A 's into B 's, and finally, returns a single B . Phew that was a mouthful, let's use this to count all the login counts across some users.

import { Monoid } from "fp-ts/lib/Monoid" ; import { foldMap } from "fp-ts/lib/Array" ; type User = { name : string , logins : number } const users : User [ ] = [ { name : "Paul" , logins : 10 } , { name : "Sue" , logins : 7 } , { name : "Bob" , logins : 8 } ] ; foldMap ( addition ) ( ( u : User ) => u . logins ) ( users ) ;

foldMap allows us to combine values by transforming them into something our Monoid can combine. If there are no items in the list, then foldMap simply takes the zero value from the Monoid .

This is similar to sumBy in lodash:

_ . sumBy ( users , u => u . logins )

The difference here is that we can use any Monoid to combine these values, not just the addition Monoid! What if we wanted to compute whether or not all of the users in our list are frequent users? We'd want to reduce the array to true if all of the users have logged in at least five times, and false if one of them have not.

First, we'd want to map each user to a boolean ( true if they have logged in five time, false otherwise), then we'd want to combine those boolean values with a monoid that operates on boolean values.

declare const andMonoid : Monoid < Boolean > ; const userIsFrequent = ( u : User ) => u . logins > 4 foldMap ( andMonoid ) ( userIsFrequent ) ( users ) ;

what does the implementation of andMonoid need to be in order to facilitate this? Try implementing this on your own and test the results with various arrays of users.

Click to see andMonoid

as another exercise, try to implement foldMap :

const foldMap : < B > ( M : Monoid < B > ) => < A > ( f : ( a : A ) => B ) => ( as : A [ ] ) => B = M => fab => as => { }

Click to see foldMap