# JavaScript Monads Made Simple

Note: This is part of the “Composing Software” series on learning functional programming and compositional software techniques in JavaScript ES6+ from the ground up. Stay tuned. There’s a lot more of this to come!

< Previous | << Start over at Part 1

Before you begin to learn monads, you should already know:

- Function composition:
`compose(f, g)(x) = (f ∘ g)(x) = f(g(x))`

- Functor basics: An understanding of the
`Array.map()`

operation.

“Once you understand monads, you immediately become incapable of explaining them to anyone else” Lady Monadgreen’s curse ~ Gilad Bracha (used famously by Douglas Crockford)

“Dr. Hoenikker used to say that any scientist who couldn’t explain to an eight-year-old what he was doing was a charlatan.” ~ Kurt Vonnegut’s novel Cat’s Cradle

If you go searching the internet for “monad” you’re going to get bombarded by impenetrable category theory math and a bunch of people “helpfully” explaining monads in terms of burritos and space suits.

Monads are simple. The lingo is hard. Let’s cut to the essence.

A **monad** is a way of composing functions that require context in addition to the return value, such as computation, branching, or I/O. Monads type lift, flatten and map so that the types line up for lifting functions `a => M(b)`

, making them composable. It's a mapping from some type `a`

to some type `b`

along with some computational context, hidden in the implementation details of lift, flatten, and map:

- Functions map:
`a => b`

- Functors map with context:
`Functor(a) => Functor(b)`

- Monads flatten and map with context:
`Monad(Monad(a)) => Monad(b)`

But what do “flatten” and “map” and “context” mean?

**Map**means, “apply a function to an`a`

and return a`b`

". Given some input, return some output.**Context**is the computational detail of the monad’s composition (including lift, flatten, and map). The Functor/Monad API and its workings supply the context which allows you to compose the monad with the rest of the application. The point of functors and monads is to abstract that context away so we don’t have to worry about it while we’re composing things. Mapping inside the context means that you apply a function from`a => b`

to the value inside the context, and return a new value`b`

wrapped inside the same kind of context. Observables on the left? Observables on the right:`Observable(a) => Observable(b)`

. Arrays on the left side? Arrays on the right side:`Array(a) => Array(b)`

.**Type lift**means to lift a type into a context, blessing the value with an API that you can use to compute from that value, trigger contextual computations, etc…`a => F(a)`

(Monads are a kind of functor).**Flatten**means unwrap the value from the context.`F(a) => a`

.

Example:

const x = 20; // Some data of type `a`

const f = n => n * 2; // A function from `a` to `b`

const arr = Array.of(x); // The type lift.

// JS has type lift sugar for arrays: [x]

// .map() applies the function f to the value x

// in the context of the array.

const result = arr.map(f); // [40]

In this case, `Array`

is the context, and `x`

is the value we're mapping over.

This example did not include arrays of arrays, but you can flatten arrays in JS with `.concat()`

:

[].concat.apply([], [[1], [2, 3], [4]]); // [1, 2, 3, 4]

### You’re probably already using monads.

Regardless of your skill level or understanding of category theory, using monads makes your code easier to work with. Failing to take advantage of monads may make your code harder to work with (e.g., callback hell, nested conditional branches, more verbosity).

Remember, the essence of software development is composition, and monads make composition easier. Take another look at the essence of what monads are:

- Functions map:
`a => b`

which lets you compose functions of type`a => b`

- Functors map with context:
`Functor(a) => Functor(b)`

, which lets you compose functions`F(a) => F(b)`

- Flatten and map with context:
`Monad(Monad(a)) => Monad(b)`

, which lets you compose lifting functions`a => F(b)`

These are all just different ways of expressing **function composition**. The whole reason functions exist is so you can compose them. Functions help you break down complex problems into simple problems that are easier to solve in isolation, so that you can compose them in various ways to form your application.

The key to understanding functions and their proper use is a deeper understanding of function composition.

Function composition creates function pipelines that your data flows through. You put some input in the first stage of the pipeline, and some data pops out of the last stage of the pipeline, transformed. But for that to work, each stage of the pipeline must be expecting the data type that the previous stage returns.

Composing simple functions is easy, because the types all line up easily. Just match output type `b`

to input type `b`

and you're in business:

g: a => b

f: b => c

h = f(g(a)): a => c

Composing with functors is also easy if you’re mapping `F(a) => F(b)`

because the types line up:

g: F(a) => F(b)

f: F(b) => F(c)

h = f(g(Fa)): F(a) => F(c)

But if you want to compose functions from `a => F(b)`

, `b => F(c)`

, and so on, you need monads. Let's swap the `F()`

for `M()`

to make that clear:

g: a => M(b)

f: b => M(c)

h = composeM(f, g): a => M(c)

Oops. In this example, *the component function types don’t line up!* For `f`

's input, we wanted type `b`

, but what we got was type `M(b)`

(a monad of `b`

). Because of that misalignment, `composeM()`

needs to unwrap the `M(b)`

that `g`

returns so we can pass it to `f`

, because `f`

is expecting type `b`

, not type `M(b)`

. That process (often called `.bind()`

or `.chain()`

) is where *flatten* and *map* happen.

It unwraps the `b`

from `M(b)`

before passing it to the next function, which leads to this:

g: a => M(b) flattens to => b

f: b maps to => M(c)

h composeM(f, g):

a flatten(M(b)) => b => map(b => M(c)) => M(c)

Monads make the types line up for lifting functions `a => M(b)`

, so that you can compose them.

In the above diagram, the `flatten`

from `M(b) => b`

and the map from `b => M(c)`

happens inside the `chain`

from `a => M(c)`

. The `chain`

invocation is handled inside `composeM()`

. At a high level, you don't have to worry about it. You can just compose monad-returning functions using the same kind of API you'd use to compose normal functions.

Monads are needed because lots of functions aren’t simple mappings from `a => b`

. Some functions need to deal with side effects (promises, streams), handle branching (Maybe), deal with exceptions (Either), etc...

Here’s a more concrete example. What if you need to fetch a user from an asynchronous API, and then pass that user data to another asynchronous API to perform some calculation?:

getUserById(id: String) => Promise(User)

hasPermision(User) => Promise(Boolean)

Let’s write some functions to demonstrate the problem. First, the utilities, `compose()`

and `trace()`

:

const compose = (...fns) => x => fns.reduceRight((y, f) => f(y), x);

const trace = label => value => {

console.log(`${ label }: ${ value }`);

return value;

};

Then some functions to compose:

{

const label = 'API call composition';

// a => Promise(b)

const getUserById = id => id === 3 ?

Promise.resolve({ name: 'Kurt', role: 'Author' }) :

undefined

;

// b => Promise(c)

const hasPermission = ({ role }) => (

Promise.resolve(role === 'Author')

);

// Try to compose them. Warning: this will fail.

const authUser = compose(hasPermission, getUserById);

// Oops! Always false!

authUser(3).then(trace(label));

}

When we try to compose `hasPermission()`

with `getUserById()`

to form `authUser()`

we run into a big problem because `hasPermission()`

is expecting a `User`

object and getting a `Promise(User)`

instead. To fix this, we need to swap out `compose()`

for `composePromises()`

— a special version of compose that knows it needs to use `.then()`

to accomplish the function composition:

{

const composeM = chainMethod => (...ms) => (

ms.reduce((f, g) => x => g(x)[chainMethod](f))

);

const composePromises = composeM('then');

const label = 'API call composition';

// a => Promise(b)

const getUserById = id => id === 3 ?

Promise.resolve({ name: 'Kurt', role: 'Author' }) :

undefined

;

// b => Promise(c)

const hasPermission = ({ role }) => (

Promise.resolve(role === 'Author')

);

// Compose the functions (this works!)

const authUser = composePromises(hasPermission, getUserById);

authUser(3).then(trace(label)); // true

}

We’ll get into what `composeM()`

is doing, later.

Remember the essence of monads:

- Functions map:
`a => b`

- Functors map with context:
`Functor(a) => Functor(b)`

- Monads flatten and map with context:
`Monad(Monad(a)) => Monad(b)`

In this case, our monads are really promises, so when we compose these promise-returning functions, we have a `Promise(User)`

instead of the `User`

that `hasPermission()`

is expecting. Notice that if you took the outer `Monad()`

wrapper off of `Monad(Monad(a))`

, you'd be left with `Monad(a) => Monad(b)`

, which is just the regular functor `.map()`

. If we had something that could flatten `Monad(x) => x`

, we'd be in business.

### What Monads are Made of

A monad is based on a simple symmetry — A way to wrap a value into a context, and a way to unwrap the value from the context:

**Lift/Unit:**A type lift from some type into the monad context:`a => M(a)`

**Flatten/Join:**Unwrapping the type from the context:`M(a) => a`

And since monads are also functors, they can also map:

**Map:**Map with context preserved:`M(a) -> M(b)`

Combine flatten with map, and you get chain — function composition for monad-lifting functions, aka Kleisli composition, named after Heinrich Kleisli:

**FlatMap/Chain:**Flatten + map:`M(M(a)) => M(b)`

For monads, `.map()`

methods are often omitted from the public API. Lift + flatten don't explicitly spell out `.map()`

, but you have all the ingredients you need to make it. If you can lift (aka of/unit) and chain (aka bind/flatMap), you can make `.map()`

:

const MyMonad = value => ({

// <... insert arbitrary chain and of here ...>

map (f) {

return this.chain(a => this.constructor.of(f(a)));

}

});

So, if you define `.of()`

and `.chain()`

/`.join()`

for your monad, you can infer the definition of `.map()`

.

The lift is the factory/constructor and/or `constructor.of()`

method. In category theory, it's called “unit”. All it does is lift the type into the context of the monad. It turns an `a`

into a `Monad`

of `a`

.

In Haskell, it’s (very confusingly) called `return`

, which gets extremely confusing when you try to talk about it out-loud because nearly everyone confuses it with function returns. I almost always call it "lift" or "type lift" in prose, and `.of()`

in code.

That flattening process (without the map in `.chain()`

) is usually called `flatten()`

or `join()`

. Frequently (but not always), `flatten()`

/`join()`

is omitted completely because it's built into `.chain()/.flatMap()`

. Flattening is often associated with composition, so it's frequently combined with mapping. Remember, unwrapping + map are both needed to compose `a => M(a)`

functions.

Depending on what kind of monad you’re dealing with, the unwrapping process could be extremely simple. In the case of the identity monad, it’s just like `.map()`

, except that you don't lift the resulting value back into the monad context. That has the effect of discarding one layer of wrapping:

{ // Identity monad

const Id = value => ({

// Functor mapping

// Preserve the wrapping for .map() by

// passing the mapped value into the type

// lift:

map: f => Id.of(f(value)),

// Monad chaining

// Discard one level of wrapping

// by omitting the .of() type lift:

chain: f => f(value),

// Just a convenient way to inspect

// the values:

toString: () => `Id(${ value })`

});

// The type lift for this monad is just

// a reference to the factory.

Id.of = Id;

But the unwrapping part is also where the weird stuff like side effects, error branching, or waiting for async I/O typically hides. In all software development, composition is where all the real interesting stuff happens.

For example, with promises, `.chain()`

called `.then()`

. Calling `promise.then(f)`

won't invoke `f()`

right away. Instead, it will wait for the promise to resolve, and *then* call `f()`

(hence the name).

Example:

{

const x = 20; // The value

const p = Promise.resolve(x); // The context

const f = n =>

Promise.resolve(n * 2); // The function

const result = p.then(f); // The application

result.then(

r => console.log(r) // 40

);

}

With promises, `.then()`

is used instead of `.chain()`

, but it's *almost* the same thing.

You may have heard that a promise is not strictly a monad. That’s because it will only unwrap the outer promise if the value is a promise to begin with. Otherwise, `.then()`

behaves like `.map()`

.

But because it behaves differently for promise values and other values, `.then()`

does not strictly obey all the mathematical laws that all functors and/or monads must satisfy for all given values. In practice, as long as you're aware of that behavior branching, you can usually treat them as either. Just be aware that some generic composition tools may not work as expected with promises.

### Building monadic (aka Kleisli) composition

Let’s take a deeper look at the `composeM`

function we used to compose promise-lifting functions:

const composeM = method => (...ms) => (

ms.reduce((f, g) => x => g(x)[method](f))

);

Hidden in that weird reducer is the algebraic definition of function composition: `f(g(x))`

. Let's make it easier to spot:

{

// The algebraic definition of function composition:

// (f ∘ g)(x) = f(g(x))

const compose = (f, g) => x => f(g(x));

const x = 20; // The value

const arr = [x]; // The container

// Some functions to compose

const g = n => n + 1;

const f = n => n * 2;

// Proof that .map() accomplishes function composition.

// Chaining calls to map is function composition.

trace('map composes')([

arr.map(g).map(f),

arr.map(compose(f, g))

]);

// => [42], [42]

}

What this means is that we could write a generalized compose utility that should work for all functors which supply a `.map()`

method (e.g., arrays):

const composeMap = (...ms) => (

ms.reduce((f, g) => x => g(x).map(f))

);

This is just a slight reformulation of the standard `f(g(x))`

. Given any number of functions of type `a -> Functor(b)`

, iterate through each function and apply each one to its input value, `x`

. The `.reduce()`

method takes a function with two input values: An accumulator (`f`

in this case), and the current item in the array (`g`

).

We return a new function `x => g(x).map(f)`

which becomes `f`

in the next application. We've already proved above that `x => g(x).map(f)`

is equivalent to lifting `compose(f, g)(x)`

into the context of the functor. In other words, it's equivalent to applying `f(g(x))`

to the values in the container: In this case, that would apply the composition to the values inside the array.

Performance Warning: I’m not recommending this for arrays. Composing functions in this way would require multiple iterations over the entire array (which could contain hundreds of thousands of items). For maps over an array, compose simple`a -> b`

functions first, then map over the array once, or optimize iterations with`.reduce()`

or a transducer.

For synchronous, eager function applications over array data, this is overkill. However, lots of things are asynchronous or lazy, and lots of functions need to handle messy things like branching for exceptions or empty values.

That’s where monads come in. Monads can rely on values that depend on previous asynchronous or branching actions in the composition chain. In those cases, you can’t get a simple value out for simple function compositions. Your monad-returning actions take the form `a => Monad(b)`

instead of `a => b`

.

Whenever you have a function that takes some data, hits an API, and returns a corresponding value, and another function that takes that data, hits another API, and returns the result of a computation on that data, you’ll want to compose functions of type `a => Monad(b)`

. Because the API calls are asynchronous, you'll need to wrap the return values in something like a promise or observable. In other words, the signatures for those functions are `a -> Monad(b)`

, and `b -> Monad(c)`

, respectively.

Composing functions of type `g: a -> b`

, `f: b -> c`

is easy because the types line up: `h: a -> c`

is just `a => f(g(a))`

.

Composing functions of type `g: a -> Monad(b)`

, `f: b -> Monad(c)`

is a little harder: `h: a -> Monad(c)`

is not just `a => f(g(a))`

because `f`

is expecting `b`

, not `Monad(b)`

.

Let’s get a little more concrete and compose a pair of asynchronous functions that each return a promise:

{

const label = 'Promise composition';

const g = n => Promise.resolve(n + 1);

const f = n => Promise.resolve(n * 2);

const h = composePromises(f, g);

h(20)

.then(trace(label))

;

// Promise composition: 42

}

How do we write `composePromises()`

so that the result is logged correctly? *Hint: You've already seen it.*

Remember our `composeMap()`

function? All you need to do is change the `.map()`

call to `.then()`

. `Promise.then()`

is basically an asynchronous `.map()`

.

{

const composePromises = (...ms) => (

ms.reduce((f, g) => x => g(x).then(f))

);

const label = 'Promise composition';

const g = n => Promise.resolve(n + 1);

const f = n => Promise.resolve(n * 2);

const h = composePromises(f, g);

h(20)

.then(trace(label))

;

// Promise composition: 42

}

The weird part is that when you hit the second function, `f`

(remember, `f`

*after* `g`

), the input value is a promise. It's not type `b`

, it's type `Promise(b)`

, but `f`

takes type `b`

, unwrapped. So what's going on?

Inside `.then()`

, there's an unwrapping process that goes from `Promise(b) -> b`

. That operation is called `join`

or `flatten`

.

You may have noticed that `composeMap()`

and `composePromises()`

are almost identical functions. This is the perfect use-case for a higher-order function that can handle both. Let's just mix the chain method into a curried function, then use square bracket notation:

const composeM = method => (...ms) => (

ms.reduce((f, g) => x => g(x)[method](f))

);

Now we can write the specialized implementations like this:

const composePromises = composeM('then');

const composeMap = composeM('map');

const composeFlatMap = composeM('flatMap');

### The monad laws

Before you can start building your own monads, you need to know there are three laws that all monads should satisfy:

- Left identity:
`unit(x).chain(f) ==== f(x)`

- Right identity:
`m.chain(unit) ==== m`

- Associativity:
`m.chain(f).chain(g) ==== m.chain(x => f(x).chain(g))`

### The Identity Laws

A monad is a functor. A functor is a morphism between categories, `A -> B`

. The morphism is represented by an arrow. In addition to the arrow we explicitly see between objects, each object in a category also has an arrow back to itself. In other words, for every object `X`

in a category, there exists an arrow `X -> X`

. That arrow is known as the identity arrow, and it's usually drawn as a little circular arrow pointing from an object and looping back to the same object.

### Associativity

Associativity just means that it doesn’t matter where we put the parenthesis when we compose. For example, if you’re adding, `a + (b + c)`

is the same as `(a + b) + c`

. The same holds true for function composition: `(f ∘ g) ∘ h = f ∘ (g ∘ h)`

.

The same holds true for Kleisli composition. You just have to read it backwards. When you see the composition operator (`chain`

), think `after`

:

h(x).chain(x => g(x).chain(f)) ==== (h(x).chain(g)).chain(f)

### Proving the Monad Laws

Let’s prove that the identity monad satisfies the monad laws:

{ // Identity monad

const Id = value => ({

// Functor mapping

// Preserve the wrapping for .map() by

// passing the mapped value into the type

// lift:

map: f => Id.of(f(value)),

// Monad chaining

// Discard one level of wrapping

// by omitting the .of() type lift:

chain: f => f(value),

// Just a convenient way to inspect

// the values:

toString: () => `Id(${ value })`

});

// The type lift for this monad is just

// a reference to the factory.

Id.of = Id;

const g = n => Id(n + 1);

const f = n => Id(n * 2);

// Left identity

// unit(x).chain(f) ==== f(x)

trace('Id monad left identity')([

Id(x).chain(f),

f(x)

]);

// Id monad left identity: Id(40), Id(40)

// Right identity

// m.chain(unit) ==== m

trace('Id monad right identity')([

Id(x).chain(Id.of),

Id(x)

]);

// Id monad right identity: Id(20), Id(20)

// Associativity

// m.chain(f).chain(g) ====

// m.chain(x => f(x).chain(g)

trace('Id monad associativity')([

Id(x).chain(g).chain(f),

Id(x).chain(x => g(x).chain(f))

]);

// Id monad associativity: Id(42), Id(42)

}

### Conclusion

Monads are a way to compose type lifting functions: `g: a => M(b)`

, `f: b => M(c)`

. To accomplish this, monads must flatten `M(b)`

to `b`

before applying `f()`

. In other words, functors are things you can map over. Monads are things you can flatMap over:

- Functions map:
`a => b`

- Functors map with context:
`Functor(a) => Functor(b)`

- Monads flatten and map with context:
`Monad(Monad(a)) => Monad(b)`

A monad is based on a simple symmetry — A way to wrap a value into a context, and a way to unwrap the value from the context:

- Lift/Unit: A type lift from some type into the monad context:
`a => M(a)`

- Flatten/Join: Unwrapping the type from the context:
`M(a) => a`

And since monads are also functors, they can also map:

- Map: Map with context preserved:
`M(a) -> M(b)`

Combine flatten with map, and you get chain — function composition for lifting functions, aka Kleisli composition:

- FlatMap/Chain Flatten + map:
`M(M(a)) => M(b)`

Monads must satisfy three laws (axioms), collectively known as the monad laws:

- Left identity:
`unit(x).chain(f) ==== f(x)`

- Right identity:
`m.chain(unit) ==== m`

- Associativity:
`m.chain(f).chain(g) ==== m.chain(x => f(x).chain(g)`

Examples of monads you might encounter in every day JavaScript code include promises and observables. Kleisli composition allows you to compose your data flow logic without worrying about the particulars of the data type’s API, and without worrying about the possible side-effects, conditional branching, or other details of the unwrapping computations hidden in the `chain()`

operation.

This makes monads a very powerful tool to simplify your code. You don’t have to understand or worry about what’s going on inside monads to reap the simplifying benefits that monads can provide, but now that you know more about what’s under the hood, taking a peek under the hood isn’t such a scary prospect.

No need to fear Lady Monadgreen’s curse.

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