5.9. Functors#

The problem we were having in the previous section was that we wanted to add code to two different modules, but that code needed to be parameterized on the details of the module to which it was being added. It’s that kind of parameterization that is enabled by an OCaml language feature called functors.


Why the name “functor”? In category theory, a category contains morphisms, which are a generalization of functions as we know them, and a functor is map between categories. Likewise, OCaml modules contain functions, and OCaml functors map from modules to modules.

The name is unfortunately intimidating, but a functor is simply a “function” from modules to modules. The word “function” is in quotation marks in that sentence only because it’s a kind of function that’s not interchangeable with the rest of the functions we’ve already seen. OCaml’s type system is stratified: module values are distinct from other values, so functions from modules to modules cannot be written or used in the same way as functions from values to values. But conceptually, functors really are just functions.

Here’s a tiny example of a functor:

module type X = sig
  val x : int

module IncX (M : X) = struct
  let x = M.x + 1

The functor’s name is IncX. It’s essentially a function from modules to modules. As a function, it takes an input and produces an output. Its input is named M, and the type of its input is X. Its output is the structure that appears on the right-hand side of the equals sign: struct let x = M.x + 1 end.

Another way to think about IncX is that it’s a parameterized structure. The parameter that it takes is named M and has type X. The structure itself has a single value named x in it. The value that x has will depend on the parameter M.

Since functors are essentially functions, we apply them. Here’s an example of applying IncX:

module A = struct let x = 0 end
module B = IncX (A)
module C = IncX (B)

Each time, we pass IncX a module. When we pass it the module bound to the name A, the input to IncX is struct let x = 0 end. Functor IncX takes that input and produces an output struct let x = A.x + 1 end. Since A.x is 0, the result is struct let x = 1 end. So B is bound to struct let x = 1 end. Similarly, C ends up being bound to struct let x = 2 end.

Although the functor IncX returns a module that is quite similar to its input module, that need not be the case. In fact, a functor can return any module it likes, perhaps something very different than its input structure:

module AddX (M : X) = struct
  let add y = M.x + y

Let’s apply that functor to a module. The module doesn’t even have to be bound to a name; we can just write an anonymous structure:

module Add42 = AddX (struct let x = 42 end)
Add42.add 1

Note that the input module to AddX contains a value named x, but the output module from AddX does not:



It’s tempting to think that a functor is the same as extends in Java, and that the functor therefore extends the input module with new definitions while keeping the old definitions around too. The example above shows that is not the case. A functor is essentially just a function, and that function can return whatever the programmer wants. In fact the output of the functor could be arbitrarily different than the input.

5.9.1. Functor Syntax and Semantics#

In the functor syntax we’ve been using:

module F (M : S) = ...

the type annotation : S and the parentheses around it, (M : S) are required. The reason why is that OCaml needs the type information about S to be provided in order to do a good job with type inference for F itself.

Much like functions, functors can be written anonymously. The following two syntaxes for functors are equivalent:

module F (M : S) = ...

module F = functor (M : S) -> ...

The second form uses the functor keyword to create an anonymous functor, like how the fun keyword creates an anonymous function.

And functors can be parameterized on multiple structures:

module F (M1 : S1) ... (Mn : Sn) = ...

Of course, that’s just syntactic sugar for a higher-order functor that takes a structure as input and returns an anonymous functor:

module F = functor (M1 : S1) -> ... -> functor (Mn : Sn) -> ...

If you want to specify the output type of a functor, the syntax is again similar to functions:

module F (M : Si) : So = ...

As usual, it’s also possible to write the output type annotation on the module expression:

module F (M : Si) = (... : So)

To evaluate an application module_expression1 (module_expression2), the first module expression is evaluated and must produce a functor F. The second module expression is then evaluated to a module M. The functor is then applied to the module. The functor will be producing a new module N as part of that application. That new module is evaluated as always, in order of definition from top to bottom, with the definitions of M available for use.

5.9.2. Functor Type Syntax and Semantics#

The simplest syntax for functor types is actually the same as for functions:

module_type -> module_type

For example, X -> Add below is a functor type, and it works for the AddX module we defined earlier in this section:

module type Add = sig val add : int -> int end
module CheckAddX : X -> Add = AddX

Functor type syntax becomes more complicated if the output module type is dependent upon the input module type. For example, suppose we wanted to create a functor that pairs up a value from one module with another value:

module type T = sig
  type t
  val x : t

module Pair1 (M : T) = struct
  let p = (M.x, 1)

The type of Pair1 turns out to be:

functor (M : T) -> sig val p : M.t * int end

So we could also write:

module type P1 = functor (M : T) -> sig val p : M.t * int end

module Pair1 : P1 = functor (M : T) -> struct
  let p = (M.x, 1)

Module type P1 is the type of a functor that takes an input module named M of module type T, and returns an output module whose module type is given by the signature sig..end. Inside the signature, the name M is in scope. That’s why we can write M.t in it, thereby ensuring that the type of the first component of pair p is the type from the specific module M that is passed into Pair1, not any other module. For example, here are two different instantiations:

module P0 = Pair1 (struct type t = int let x = 0 end)
module PA = Pair1 (struct type t = char let x = 'a' end)

Note the difference between int and char in the resulting module types. It’s important that the output module type of Pair1 can distinguish those. And that’s why M has to be nameable on the right-hand side of the arrow in P1.


Functor types are an example of an advanced programming language feature called dependent types, with which the type of an output is determined by the value of an input. That’s different than the normal case of a function, where it’s the output value that’s determined by the input value, and the output type is independent of the input value.

Dependent types enable type systems to express much more about the correctness of a program, but type checking and inference for dependent types is much more challenging. Practical dependent type systems are an active area of research. Perhaps someday they will become popular in mainstream languages.

The module type of a functor’s actual argument need not be identical to the formal declared module type of the argument; it’s fine to be a subtype. For example, it’s fine to apply F below to either X or Z. The extra item in Z won’t cause any difficulty.

module F (M : sig val x : int end) = struct let y = M.x end
module X = struct let x = 0 end
module Z = struct let x = 0;; let z = 0 end
module FX = F (X)
module FZ = F (Z)

5.9.3. The Map Module#

The standard library’s Map module implements a map (a binding from keys to values) using balanced binary trees. It uses functors in an important way. In this section, we study how to use it. You can see the implementation of that module on GitHub as well as its interface.

The Map module defines a functor Make that creates a structure implementing a map over a particular type of keys. That type is the input structure to Make. The type of that input structure is Map.OrderedType, which are types that support a compare operation:

module type OrderedType = sig
  type t
  val compare : t -> t -> int

The Map module needs ordering, because balanced binary trees need to be able to compare keys to determine whether one is greater than another. The compare function’s specification is the same as that for the comparison argument to List.sort_uniq, which we previously discussed:

  • The comparison should return 0 if two keys are equal.

  • The comparison should return a strictly negative number if the first key is lesser than the second.

  • The comparison should return a strictly positive number if the first key is greater than the second.


Does that specification seem a little strange? Does it seem hard to remember when to return a negative vs. positive number? Why not define a variant instead?

type order = LT | EQ | GT
val compare : t -> t -> order

Alas, historically many languages have used comparison functions with similar specifications, such as the C standard library’s strcmp function. When comparing two integers, it does make the comparison easy: just perform a subtraction. It’s not necessarily so easy for other data types.

The output of Map.Make supports all the usual operations we would expect from a dictionary:

module type S = sig
  type key
  type 'a t
  val empty: 'a t
  val mem: key -> 'a t -> bool
  val add: key -> 'a -> 'a t -> 'a t
  val find: key -> 'a t -> 'a

The type variable 'a is the type of values in the map. So any particular map module created by Map.Make can handle only one type of key, but is not restricted to any particular type of value. An Example Map#

Here’s an example of using the Map.Make functor:

module IntMap = Map.Make(Int)

If you show that output, you’ll see the long module type of IntMap. The Int module is part of the standard library. Conveniently, it already defines the two items required by OrderedType, which are t and compare, with appropriate behaviors. The standard library also already defines modules for the other primitive types (String, etc.) that make it convenient to use any primitive type as a key.

Now let’s try out that map by mapping an int to a string:

open IntMap;;
let m1 = add 1 "one" empty
find 1 m1
mem 42 m1
find 42 m1
bindings m1

The same IntMap module allows us to map an int to a float:

let m2 = add 1 1. empty
bindings m2

But the keys must be int, not any other type:

let m3 = add true "one" empty

That’s because the IntMap module was specifically created for keys that are integers and ordered accordingly. Again, order is crucial, because the underlying data structure is a binary search tree, which requires key comparisons to figure out where in the tree to store a key. You can even see that in the standard library source code (v4.12), of which the following is a lightly-edited extract:

module Make (Ord : OrderedType) = struct
  type key = Ord.t

  type 'a t =
    | Empty
    | Node of {l : 'a t; v : key; d : 'a; r : 'a t; h : int}
      (** left subtree, key, value/data, right subtree, height of node *)

  let empty = Empty

  let rec mem x = function
    | Empty -> false
    | Node {l, v, r} ->
        let c = Ord.compare x v in
        c = 0 || mem x (if c < 0 then l else r)

The key type is defined to be a synonym for the type t inside Ord, so key values are comparable using Ord.compare. The mem function uses that to compare keys and decide whether to recurse on the left subtree or right subtree.

Note how the implementor of Map had a tricky problem to solve: balanced binary search trees require a way to compare keys, but the implementor can’t know in advance all the different types of keys that a client of the data structure will want to use. And each type of key might need its own comparison function. Although Stdlib.compare can be used to compare any two values of the same type, the result it returns isn’t necessarily what a client will want. For example, it’s not guaranteed to sort names in the way we wanted above.

So the implementor of Map used a functor to solve their problem. They parameterized on a module that bundles together the type of keys with a function that can be used to compare them. It’s the client’s responsibility to implement that module.

The Java Collections Framework solves a similar problem in the TreeMap class, which has a constructor that takes a Comparator. There, the client has the responsibility of implementing a class for comparisons, rather than a structure. Though the language features are different, the idea is the same. Maps with Custom Key Types#

When the type of a key becomes complicated, we might want to write our own custom comparison function. For example, suppose we want a map in which keys are records representing names, and in which names are sorted alphabetically by last name then by first name. In the code below, we provide a module Name that can compare records that way:

type name = {first : string; last : string}

module Name = struct
  type t = name
  let compare { first = first1; last = last1 } { first = first2; last = last2 }
    match String.compare last1 last2 with
    | 0 -> String.compare first1 first2
    | c -> c

The Name module can be used as input to Map.Make because it satisfies the Map.OrderedType signature:

module NameMap = Map.Make (Name)

Now we could use that map to associate names with birth years:

let k1 = {last = "Kardashian"; first = "Kourtney"}
let k2 = {last = "Kardashian"; first = "Kimberly"}
let k3 = {last = "Kardashian"; first = "Khloe"}
let k4 = {last = "West"; first = "Kanye"}

let nm =
  NameMap.(empty |> add k1 1979 |> add k2 1980 |> add k3 1984 |> add k4 1977)

let lst = NameMap.bindings nm

Note how the order of keys in that list is not the same as the order in which we added them. The list is sorted according to the Name.compare function we wrote. Several of the other functions in the Map.S signature will also process map bindings in that sorted order—for example, map, fold, and iter. How Map Uses Module Type Constraints#

In the standard library’s map.mli interface, the specification for Map.Make is:

module Make (Ord : OrderedType) : S with type key = Ord.t

The with constraint there is crucial. Recall that type constraints specialize a module type. Here, S with type key = Ord.t specializes S to expose the equality of S.key and Ord.t. In other words, the type of keys is the ordered type.

You can see the effect of that sharing constraint by looking at the module type of our IntMap example from before. The sharing constraint is what caused the = Int.t to be present:

module IntMap : sig
  type key = Int.t

And the Int module contains this line:

type t = int

So IntMap.key = Int.t = int, which is exactly why we’re allowed to pass an int to the add and mem functions of IntMap.

Without the type constraint, type key would have remained abstract. We can simulate that by adding a module type annotation of Map.S, thereby resealing the module at that type without exposing the equality:

module UnusableMap = (IntMap : Map.S);;

Now it’s impossible to add a binding to the map:

let m = UnusableMap.(empty |> add 0 "zero")

This kind of use case is why module type constraints are quite important in effective programming with the OCaml module system. Often it is necessary to specialize the output type of a functor to show a relationship between a type in it and a type in one of the functor’s inputs. Thinking through exactly what constraint is necessary can be challenging, though!

5.9.4. Using Functors#

With Map we saw one use case for functors: producing a data structure that was parameterized on a client-provided ordering. Here are two more use cases. Test Suites#

Here are two implementations of a stack:

exception Empty

module type Stack = sig
  type 'a t
  val empty : 'a t
  val push : 'a -> 'a t -> 'a t
  val peek : 'a t -> 'a
  val pop : 'a t -> 'a t

module ListStack = struct
  type 'a t = 'a list
  let empty = []
  let push = List.cons
  let peek = function [] -> raise Empty | x :: _ -> x
  let pop = function [] -> raise Empty | _ :: s -> s

module VariantStack = struct
  type 'a t = E | S of 'a * 'a t
  let empty = E
  let push x s = S (x, s)
  let peek = function E -> raise Empty | S (x, _) -> x
  let pop = function E -> raise Empty | S (_, s) -> s

Suppose we wanted to write an OUnit test for ListStack:

let test = "peek (push x empty) = x" >:: fun _ ->
  assert_equal 1 ListStack.(empty |> push 1 |> peek)

Unfortunately, to test a VariantStack, we’d have to duplicate that code:

let test' = "peek (push x empty) = x" >:: fun _ ->
  assert_equal 1 VariantStack.(empty |> push 1 |> peek)

And if we had other stack implementations, we’d have to duplicate the test for them, too. That’s not so horrible to contemplate if it’s just one test case for a couple implementations, but if it’s hundreds of tests for even a couple implementations, that’s just too much duplication to be good software engineering.

Functors offer a better solution. We can write a functor that is parameterized on the stack implementation, and produces the test for that implementation:

module StackTester (S : Stack) = struct
  let tests = [
    "peek (push x empty) = x" >:: fun _ ->
      assert_equal 1 S.(empty |> push 1 |> peek)

module ListStackTester = StackTester (ListStack)
module VariantStackTester = StackTester (VariantStack)

let all_tests = List.flatten [

Now whenever we invent a new test we add it to StackTester, and it automatically gets run on both stack implementations. Nice!

There is still some objectionable code duplication, though, in that we have to write two lines of code per implementation. We can eliminate that duplication through the use of first-class modules:

let stacks = [ (module ListStack : Stack); (module VariantStack) ]

let all_tests =
  let tests m =
    let module S = (val m : Stack) in
    let module T = StackTester (S) in
  let open List in
  stacks |> map tests |> flatten

Now it suffices just to add the newest stack implementation to the stacks list. Nicer! Extending Multiple Modules#

Earlier, we tried to add a function of_list to both ListSet and UniqListSet without having any duplicated code, but we didn’t totally succeed. Now let’s really do it right.

The problem we had earlier was that we needed to parameterize the implementation of of_list on the add function and empty value in the set module. We can accomplish that parameterization with a functor:

module type Set = sig
  type 'a t
  val empty : 'a t
  val mem : 'a -> 'a t -> bool
  val add : 'a -> 'a t -> 'a t
  val elements : 'a t -> 'a list

module SetOfList (S : Set) = struct
  let of_list lst = List.fold_right S.add lst S.empty

Notice how the functor, in its body, uses S.add. It takes the implementation of add from S and uses it to implement of_list (and the same for empty), thus solving the exact problem we had before when we tried to use includes.

When we apply SetOfList to our set implementations, we get modules containing an of_list function for each implementation:

module ListSet : Set = struct
  type 'a t = 'a list
  let empty = []
  let mem = List.mem
  let add = List.cons
  let elements s = List.sort_uniq Stdlib.compare s

module UniqListSet : Set = struct
  (** All values in the list must be unique. *)
  type 'a t = 'a list
  let empty = []
  let mem = List.mem
  let add x s = if mem x s then s else x :: s
  let elements = Fun.id
module OfList = SetOfList (ListSet)
module UniqOfList = SetOfList (UniqListSet)

The functor has enabled the code reuse we couldn’t get before: we now can implement a single of_list function and from it derive implementations for two different sets.

But that’s the only function those two modules contain. Really what we want is a full set implementation that also contains the of_list function. We can get that by combining includes with functors:

module SetWithOfList (S : Set) = struct
  include S
  let of_list lst = List.fold_right S.add lst S.empty

That functor takes a set as input, and produces a module that contains everything from that set (because of the include) as well as a new function of_list.

When we apply the functor, we get a very nice set module:

module SetL = SetWithOfList (ListSet)
module UniqSetL = SetWithOfList (UniqListSet)

Notice how the output structure records the fact that its type t is the same type as the type t in its input structure. They share it because of the include.

Stepping back, what we just did bears more than a passing resemblance to class extension in Java. We created a base module and extended its functionality with new code while preserving its old functionality. But whereas class extension necessitates that the newly extended class is a subtype of the old, and that it still has all the old functionality, OCaml functors are more fine-grained in what they can accomplish. We can choose whether they include the old functionality. And no subtyping relationships are necessarily involved. Moreover, the functor we wrote can be used to extend any set implementation with of_list, whereas class extension applies to just a single base class. There are ways of achieving something similar in object-oriented languages with mixins, which enable a class to re-use functionality from other classes without necessitating the complication of multiple inheritance.