One important function in functional programming is the map function. When I learned F# I must admit that I had some problems first, understanding it. The problem was, I already knew the map function from dozens of other languages. Or to say it correctly, I mostly learned a wrong explanation of map.

The typical explanation I’m talking about often goes something like this: map takes a function and a list. It applies the function to every element in the list, and returns a new list. You will often see examples like this:

// F#
let xs = List.map (fun x -> x * 2) [1;2;3;4;5]

// C#
var xs = Enumerable.Range(1,5).Select(x => x * 2) // Select is `map`

// JavaScript
var xs = [1,2,3,4,5].map(function(x) { return x * 2 })

All examples start with some kind of array collection that contains the numbers from 1 to 5. And all of them take a function multiplying the number by two. All of the examples will result in a new collection containing [2;4;6;8;10].

While this explanation of map is right for List.map, this is not a right explanation of map in general. The problem starts when you encounter a functional language, because besides a List.map you will also encounter things like String.map or Option.map. On top you will also often find the advice that you should provide a map function for every type you create (if possible). When you have a Result type you should also provide a Result.map. Also a Async.map is a good idea. So if you only knew map from the idea of going through a collection you will probably suffer to understand what map is about. If you try to implement map for yourself, you will probably even wonder what map anyway should do for an arbitrary type? What is for example the purpose of Async.map?

To explain what map really is about, let’s forget about what you already know and start from scratch again.

Some functions

Before we look at map, let’s create some simple functions. These functions will be used throughout the article.

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// Squares a number: int -> int
let square x = x * x

// Add 10 to every number: int -> int
let add10 x = x + 10

// Returns the length of a string: string -> int
let length (str:string) = str.Length

List.map

We now assume that we don’t have most of the functions from the List module. Especially not List.map. Sooner or later you will encounter one problem. With our square function we can square an int. But our square doesn’t work at all with a list<int>.

So what do you do if you want to apply square to every int inside a list? You sure start looping over the list, and because we are immutable, we build a new list. As for easiness I write very imperative code with a loop, without recursion or fold or foldBack.

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let squareList xs =
    let mutable results = []
    for x in xs do
        let res = square x
        results <- res :: results
    List.rev results

So what we now have is a squareList function, this function now takes a list<int> as input and returns a new list<int>. Our squareList function basically does the same thing as square, but instead of int -> int we have upgraded it somehow to work with list<int> -> list<int> instead.

A final note is the List.rev at the end, if it is unclear why we need it. x :: xs creates a new list, but it prepends elements. We actually cannot add elements to the end. So when we loop over a list like [1;2;3;4;5] we will first square 1 and add it to an empty list resulting in [1]. Then we square 2 and the result is added to [1] yielding in [4;1] and so on. That’s why we have to reverse the list at the end when we are done!

Some time later we are faced with the problem that we also want to use our add10 function on a list so we also write a new function for this.

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let add10List xs =
    let mutable results = []
    for x in xs do
        let res = add10 x
        results <- res :: results
    List.rev results

Besides that the code is anyway not really nice or functional to begin with, the big problem is that we basically have written two completely identical functions! The only difference between those two functions is line 4. The only thing that is different is the function we call to compute res.

Because we like DRY (Don’t Repeat Yourself) we do what functional programmers always tell Parametrize all the things. So instead of directly calling our function, we just expect that the concrete function to execute for every element is just passed as an argument. Or simply we Abstract those two functions. Abstracting always means that we put the things that are the same into one function, everything that is different will be expected as an argument. So what we finally end up with, is our own map function.

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let mapList f xs =
    let mutable results = []
    for x in xs do
        let mapping = f x
        results <- mapping :: results
    List.rev results

We now can use mapList like this.

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let listOfsquared = mapList square [1;2;3] // [1;4;9]
let listOfAdd10   = mapList add10  [1;2;3] // [11;12;13]

Currently it doesn’t seems like a big difference to other introductions, but let’s reconsider what lead to the idea of creating a map function. The idea was. We have a function int -> int. A list<int> contains int so we could use square or add10 on every element. But in order to apply our function to every element we have to handle the list, and we need to loop through them. Because this process is the same for every function, we abstract the looping away in it’s own function named map.

Before we go even deeper in why this is different from other explanation. Let’s first look at the signature of our mapList function, and let’s just remember the signature.

('a -> 'b) -> list<'a> -> list<'b>

Option.map

Suddenly later when we are programming, we face a new problem. We encounter a option<int> value. option<int> contains an int. So because it contains an int, we also could use our square function on the inner value. But sure, now we have to handle option. So what do we do? We can sure unwrap it, and in case of a Some we apply our function to it. But what do we do in a case of None? Returning some kind of default int doesn’t seem to make sense or like a good idea. So what we will instead do, we just create a new function that will return an option again. In the case of None we just return None and do nothing.

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let squareOption opt =
    match opt with
    | None       -> None
    | Some value -> Some (square value)

Like squareList previously we now created a squareOption. It is already interesting to see some common between all those function. square could simply square an int. squareList could square a list<int> and now squareOption can square an option<int>. Let’s go further and let’s implement add10Option.

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let add10Option opt =
    match opt with
    | None       -> None
    | Some value -> Some (add10 value)

Once again we can see the code duplication. So instead of checking again and again for every option if it is None or Some and only in the Some case call a function we starting to abstract it! Instead of calling our function directly, we once again expect it to be passed as an argument. We will call this abstract function mapOption.

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let mapOption f opt =
    match opt with
    | None       -> None
    | Some value -> Some (f value)

We now can use mapOption like this.

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let OptionSquare1 = mapOption square (Some 5) // Some 25
let OptionSquare2 = mapOption square None     // None
let OptionAdd10_1 = mapOption add10  (Some 5) // Some 15
let OptionAdd10_2 = mapOption add10  None     // None

Let’s once again look at the type signature of our mapOption

('a -> 'b) -> option<int> -> option<int>

Does this already look familiar?

The commonalities between `List.map` and `Option.map`

Now let’s reconsider with what we started. We started with functions like square and add10. But those function only could work with int. But while we were programming we faced values like list<int> or option<int>. To use our functions on those values, we somehow have to unwrap the values. Apply our function to the inner type int, and wrap it up again. unwraping is different for every type. For list it means we loop through a list. For an option it means we have to check whether it is Some or None. But we still can think of it as some kind of an unwrap function. Because what we do is, at some point in our function we turn an list<int> or an option<int> just to in int, so we can use the int with our square or add10 function. But after applying our function we still return the type of what we started. When we started with a list<int> we still have to return a list again. When we started with an option we still return an option again.

But this is a repetitive task, as this kind of unwraping and re-wraping is always the same. It doesn’t matter which type we have inside list<> or option. And it also doesn’t matter which function we use.

That’s why we abstract those idea of unwrapping, applying a function, re-wrap into it’s own function and name it map. To understand this process further let’s look again at the type signatures of our mapList and mapOption function.

('a -> 'b) -> list<'a>    -> list<'b>
('a -> 'b) -> option<int> -> option<int>

This type-signature is the essence of a map function. Every map function has to look like this. The only part that changes is the wrapping-type. So at this point you could probably already assume how a map function for Result, Async or Whatever should look like

('a -> 'b) -> Result<'a>   -> Result<'b>
('a -> 'b) -> Async<'a>    -> Async<'b>
('a -> 'b) -> Whatever<'a> -> Whatever<'b>

Currying and Partial Application

At this point it is important to talk about Currying. Currying is the idea that there only exists functions with one-argument and they always have to return a value. F# is such a language and does currying automatically.

That is also the very reason why you see multiple -> inside a function signature. -> is basically the symbol for a function. On it’s left-side is the input of the function, on the right side is the output. When we look at a signature like

string -> int -> float

We often say it has two arguments, a string and a int and it returns a float. But this isn’t quite correct. What we really have is a function that only has one argument a string and it will return a int -> float, or in other words. A new function! That is also the reason why functional languages don’t use braces as arguments, it just uses a space. Something like

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let ys = List.map f xs

really means. Execute List.map f this returns a new function, and we immediately pass xs to that new function. That’s also the reason why we can add braces around the function and the first argument without changing the meaning.

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let ys = (List.map f) xs

Not only that, we also can extract it, and save the intermediate function as a new value.

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let newF = List.map f
let ys   = newF xs

The idea to not pass all needed values is what we call Partial Application. The interesting stuff about all of this is, that with this idea, we can come up with different interpretation of the same function. And this kind of interpretation is what we can apply to map. Actually we can view map as a single argument function, or as a two argument functions. Both have some different meaning. When we interpret map as a single argument function, we now have something like this

Function	Input	Output
List.map	‘a -> ‘b	list<‘a> -> list<‘b>
Option.map	‘a -> ‘b	option<‘a> -> option<‘b>
Result.map	‘a -> ‘b	Result<‘a> -> Result<‘b>
Async.map	‘a -> ‘b	Async<‘a> -> Async<‘b>
Whatever.map	‘a -> ‘b	Whatever<‘a> -> Whatever<‘b>

It basically means we can think of map of some kind of function that can upgrade a function. If we pass a int -> string function for example to List.map we get a list<int> -> list<string> function back! If we pass the same function to Async.map we get a Async<int> -> Async<string> function back.

So map is a way to upgrade both sides (input and output) and add a layer to both sides. There are two reason on why this concept is important.

Code-Reuse. If a type supports map, you just can upgrade a function to work with this type.
In your own functions, you don’t need to care about the layer itself.

Code Reuse

So let’s look again at our starting functions and just use them with the already built-in List.map and Option.map functions.

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let squareL = List.map square
let add10L  = List.map add10
let lengthL = List.map length

let squareO = Option.map square
let add10O  = Option.map add10
let lengthO = Option.map length

So we just can reuse our three functions. We never have to write special functions that loops through a list. Or that handles option, Async, Result, we just can upgrade any function we already have written.

You don’t need to care for the layers

This is probably the biggest advantage, as you don’t have to care for the layers. You want to convert a list of int to a list of string. Just write a function that does int -> string no List handling, no looping, no recursion. Use List.map and you are done.

And the big advantage. You also can use that function with Option.map to turn it into a function that works on a option type. If you pass it to Async.map you get a function that can work on an asynchronous value. You don’t need to write code for looping through a list, do pattern match an option, or write code to handle asynchronicity.

All of this is done for you by the map function!

Async.map

Currently F# don’t have a built-in Async.map function. So let’s create the map function for Async ourselves.

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module Async =
    let map f op = async {
        let! x    = op
        let value = f x
        return value
    }

So how do we now that we have to implement it in this way? Because that is what the type-signature is telling us. We have to write a function with the signature

('a -> 'b) -> Async<'a> -> Async<'b>

That means a function with two arguments. The first arguments is a function 'a -> 'b, the second is an Async<'a>, and we have to return an Async<'b>.
Because we have to return an Async we start with async { ... }.
Now op is an Async<'a>, with let! x = op we run the the async operation. This will unwrap our Async<'a> and just returns an 'a.
We can pass that 'a to our function f that converts 'a to an 'b.
Once we have a 'b we return it. return basically wraps the 'b and adds the Async<> layer.

Stacking Layers

The interesting idea is now. We are not restricted to adding a single layer. We can add as much layer we want and stack them. For example we could have option values that are wrapped inside a list returned by an Async operation.

To be more concrete. Let’s assume we have some kind of async operation that downloads from a website (The Async layer). This tries to Parse a table on a website that contains numbers (The List Layer). But because parsing could fail, for example a table entry is not a number, we wrap it in a Option (The Optional Layer).

Let’s write a mock function that returns this kind of data.

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let downloadPage = async {
    // Simulating Download, wait 1 second
    do! Async.Sleep 1000
    // A list of optionals list<option<int>>
    let numbers = [Some 1; Some 2; None; Some 3; None; None; Some 10]
    // return it: This adds async<> layer
    return numbers
}

What we now have is a Async<list<option<int>>>. Puh looks complicated! So what do we now if we want to square the int inside our Async<List<Option<...>>> construct? We just add one layer after another to square. At first, we do a Option.map on square. The result of this is a function that we pass to List.map that adds the List layer. And once again the Async.map finally adds the Async layer.

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let squaring = Async.map (List.map (Option.map square))

We now have squaring that has the following signature

Async<List<Option<int>>> -> Async<List<Option<int>>>

We can now do

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let data = Async.RunSynchronously (squaring downloadPage)

And data will be [Some 1; Some 4; None; Some 9; None; None; Some 100]

All Option, List and Async handling was handled for us. We just upgraded square with the different map functions until it matches our needed signature.

Let’s assume we wouldn’t have Async.map, List.map, Option.map. We would have needed to write it like this.

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let squaring' input = async {
    let! data = input
    let squared = [
        for x in data ->
            match x with
            | None       -> None
            | Some value -> Some (square value)
    ]
    return squared
}

Functors

Whenever we have a type with a map function we call it a Functor if the implementation of map satisfies two laws. Those two laws ensures that map is predictable and don’t do additional stuff we didn’t expect.

1. Law: Mapping `id`

The first rule says that mapping over the id function must not change the input. The id function is just a function that returns its input as-is

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let id x = x

So when we write

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let xs = List.map id [1;2;3;4;5]

Then xs still must be [1;2;3;4;5].

2. Law: Function composition

The second rule says that composing two functions and then mapping, should be the same as mapping over both functions separately.

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// 1 solution: compose two functions, and then map
let comp = square >> add10
let cxs  = List.map comp [1;2;3;4;5]

// 2 solution: mapping it two times
let sxs =
    [1;2;3;4;5]
    |> List.map square
    |> List.map add10

cxs = sxs // must be the same

It shouldn’t matter if we go through the list take one element and then do square and add10 in one-step. Or if we go trough our list two times and do it in two separately steps. Both cxs and sxs have to return the same result [11;14;19;26;35]

Because List.map satisfies both laws, we say that List is a functor.

Summary

I hope it is now clear why map is such an important function. Implementing a map function just means you can upgrade already available functions. It opens up a lot of code reuse as you don’t have to write special glue code that handles your type/layer.

It also can make writing new functions easier, as you don’t have to care about a layer. If you find yourself writing a function that has a list as its input and a list as its output then you are probably doing something wrong! The same goes for every other type.

Not only is it easier to just write a function that don’t contain any list/looping/recursion logic. Such a function is even more reusable.

Understanding map

Some functions

List.map

Option.map

The commonalities between `List.map` and `Option.map`

Currying and Partial Application

Code Reuse

You don’t need to care for the layers

Async.map

Stacking Layers

Functors

1. Law: Mapping `id`

2. Law: Function composition

Summary

Further Reading

Some functions

List.map

Option.map

The commonalities between List.map and Option.map

Currying and Partial Application

Code Reuse

You don’t need to care for the layers

Async.map

Stacking Layers

Functors

1. Law: Mapping id

2. Law: Function composition

Summary

Further Reading

The commonalities between `List.map` and `Option.map`

1. Law: Mapping `id`