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Experimental implementations of ML-style modules in Scala

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Exploring ML-Style Modular Programming in Scala

I recently watched a talk by Martin Odersky (2014) in which he boils Scala down to what he considers to be the essential parts of the language. In it he remarks that Scala is designed to be a modular programming language and its modular abstractions are greatly inspired by modular programming in ML (SML). I found this intriguing, because I regard SML-style modular programming as a great way to organise software when doing programming-in-the-large. If Prof. Odersky's assertion about modular programming in Scala is correct, SML-style modules might be a great fit with Scala.

As is usually the case, I went searching for what others have had to say on the matter. I found a great post (James, 2014) showing several attempts and approaches at encoding ML-style modules in Scala, and trying out several syntactic 'styles' of Scala to see which one might 'feel' better in use. James starts with Odersky's central premise, which I interpret as the following key points:

  • Scala object = ML module

  • Scala trait = ML signature

  • Scala class = ML functor

He then goes on to explore actually implementing modules using the driving example of a

data structure from Okasaki (1996).

I also found a very thoughtful answer and discussion ('Encoding Standard ML modules in OO', 2014) of the same question, posed on Stack Overflow. The answer and discussion here are just a gold mine of information about Scala's theoretical issues with handling ML-style modules. The key points I took away from them:

  • ML functor = Scala function. This is slightly more elegant than the cumbersome declaration of a new class; but it does require a little finesse to avoid typechecker traps.

  • Scala's type relationships are nominal as opposed to ML's structural relationships. In practice this means we will need to declare and define all our types and traits before trying to set up relationships between them using functors. This is admittedly a limitation, but perhaps one worth living with given the code organisation and maintainability benefits we gain in exchange.

Finally, I found a fantastic talk (Martens, 2015) on ML modules, their use, and the type theory behind them. I'm still mulling all the new ideas over, but so far I've managed to take away the following main points:

  • SML functors are generative functors, so called because they generate new modules as return values for each time they're called. This is true even if they're called with the exact same arguments each time. Similar to Haskell's

    newtype A = Int
    newtype B = Int
    creating two completely distinct types
    despite being literally the same type.
  • SML functors are actually modelled using formal logic techniques which I won't pretend to understand--just appreciate.

  • It's possible and sometimes even desirable to get different results from two modules which have been created by the same functor. This despite the general idea behind functors being to abstract implementation away from interface, implying that all implementations should return the same results (otherwise the interface 'breaks its promise').

A Functional Convenience

Before we start creating modules, let's define a helper function to make it easy to apply functions in a reverse application order, like F# or OCaml, e.g.:

x |> f |> g |> h
implicit class Piper[A](val x: A) extends AnyVal {
  def |>[B](f: A => B) = f(x)

For an explanation of how this works and why I recommend it for Scala, see 'In scala, what's the idiomatic way to apply a series of composed functions to a value?' (2013).

A Basic Module

As a sort of review, let's look at a simple SML module and beside it a Scala port. This example is adapted from a fantastic ground-up tutorial on ML modules (Tofte, 1996). It implements a finite map from integers to values of some arbitrary type. In other words, a vector. On a side note, the interesting thing about this data structure is that it's implemented purely using function composition.

/* structure IntFn =              */ object IntFn {
/*   struct                       */   case class NotFound() extends Exception()
/*     exception NotFound         */   type T[A] = Int => A
/*     type 'a t = int -> 'a      */
/*                                */   def empty[A]: T[A] =
/*     fun empty i =              */     (i: Int) => throw NotFound()
/*       raise NotFound           */
/*                                */   def get[A](i: Int)(x: T[A]) = x(i)
/*     fun get i x = x i          */
/*                                */   def insert[A](k: Int, v: A)(x: T[A]) =
/*     fun insert (k, v) x i =    */     (i: Int) => if (i == k) v else get(i)(x)
/*       if i = k then v else x i */ }
/*   end;                         */

With this implementation, we can do things like:

scala> IntFn.empty |> IntFn.insert(1, "a") |> IntFn.get(1)
res7: String = a

A few points to take away from this:

  • Scala's generic methods will require type parameters, which is really clunky, unless you manage to define the type parameter elsewhere, as we will see later.

  • Scala is somewhat denser than SML for the equivalent functionality. To me, this is mostly a result of Scala's weaker type inference that forces us to specify a lot more.

The Module Signature

The next step in the evolution of a module is usually to extract its signature:

/* signature INTMAP =                       */ trait IntMap[A] {
/*   sig                                    */   type NotFound
/*     exception NotFound                   */   type T
/*     type 'a t                            */
/*                                          */   val empty: T
/*     val empty: 'a t                      */   def get(i: Int)(x: T): A
/*     val get: int -> 'a t -> 'a           */   def insert(k: Int, v: A)(x: T): T
/*     val insert: int * 'a -> 'a t -> 'a t */ }
/*   end;                                   */

Now we start making some trade-offs in Scala. Some points to take away:

  • We're able to clean out the type parameters from all the methods, but we're now passing in the type parameter into the trait. This has upsides and downsides: it makes the signature's declared types simpler (we don't have to parameterise any of the inner types or methods), but it also means we can't instantiate a single module in Scala to handle any input type, as we'll see later.

  • We express

    as a
    instead of as a
    because we want it to return the exact same thing each time; so no need for a function call to do that. We couldn't do this with the object version before because
    s can't accept type parameters (this is a hard fact of Scala syntax). This is another reason to move out the type parameter to the trait, so that it's in scope by the time we start declaring

After that, the next step is to express the module's implementation in terms of the signature:

/* structure IntFn :> INTMAP =    */ trait IntFn[A] extends IntMap[A] {
/*   struct                       */   case class NotFound() extends Exception()
/*     exception NotFound         */   type T = Int => A
/*     type 'a t = int -> 'a      */
/*                                */   override val empty =
/*     fun empty i =              */     (i: Int) => throw NotFound()
/*       raise NotFound           */
/*                                */   override def get(i: Int)(x: T) = x(i)
/*     fun get i x = x i          */
/*                                */   override def insert(k: Int, v: A)(x: T) =
/*     fun insert (k, v) x i =    */     (i: Int) =>
/*       if i = k                 */       if (i == k) v else get(i)(x)
/*         then v                 */ }
/*         else get i x           */
/*   end;                         */

As I mentioned, we express the SML module directly as a concrete structure, while we express the Scala module as an abstract trait that takes a type parameter. We decided in the signature to pass in a type parameter for a few different reasons, which we'll elaborate on later.

Well, as a consequence of these decisions, we also aren't able to directly create a module (i.e., a Scala object) that can work on any given type. Scala objects can't take type parameters; they can only be instantiated with concrete types (well, unless you use existential types, which is advanced type hackery that I want to avoid).

So, we have to define something which can take a type parameter; and the choices are a trait or a class (if we're defining something at the toplevel, that is). I went with a trait partly to minimise the number of different concepts I use, and partly to emphasise the abstract nature of this 'module template', if you will.

Now, we can instantiate concrete Scala modules (objects) with a type parameter of our choosing, and within some scope (not at the toplevel):

object MyCode {
  val IntStrFn: IntMap[String] = new IntFn[String] {}

IntStrFn.empty |> IntStrFn.insert(1, "a") |> IntStrFn.get(1) |> print }

Notice how:

  • We upcast the

    module to only expose the
    interface, just as we constrained the SML
    module to only expose the
    signature using the constraint operator
    . As a quick reminder, ML calls this 'opaque signature ascription' and we use it to get the benefit of hiding our implementation details.

    In Scala, we implement opaque signature ascription simply with upcasting.

    There is another type of ascription, 'transparent' ascription, which means 'the module exposes at least this signature, but possibly also more'. We get that in Scala by simply leaving out the type annotation from the module declaration and letting Scala infer a subtype of the signature trait for our module.

    These types of ascription are described in Tofte (1996, p. 4).

  • We define the module (

    ) inside an object
    because in Scala,
    s and
    s can't be in the toplevel--they need to be contained within some scope. In practice we can easily work around that restriction by defining everything inside some object and then importing all names from that object into the toplevel.
  • The Scala implementation ends up using two traits for two levels of abstraction (the module interface and the implementation using a representation of composed functions), which is somewhat sensible.

  • We use

    extensively in the
    trait to explicitly show which methods and values are from the signature. Of course, we can have more methods and values in the
    trait and object instances (e.g.
    ) and these will automatically be private--they'll never be seen outside the module because the module will be upcast immediately on creation to its signature's type, and its runtime type will never be known by any user.
  • We implement the

    module as actually an anonymous class that extends the
    trait and passes in the concrete type as the type parameter. The class has an empty body because it extends a trait which defines all its methods and values already.

A Detour into Module Opaque Types

If you evaluate all the Scala traits and modules shown upto this point in the REPL, and then evaluate the following code:

import MyCode._

You'll get back something like:

res: MyCode.IntStrFn.T = 

This is one of the elegant things about ML-style modules. Each module contains all definitions and types it needs to operate, in a single bundle. Traditionally, the module's primary type is just called

, so
has the sense that it's the
module's primary type.

This type alias that you get from the module, known as an opaque type, doesn't give you any information or operations on itself. It limits you to using values of the type in only exactly the operations that the module itself provides. And that's a great thing for modularity and information hiding.

You might point out that the REPL actually tells you that the type is really a

, so you immediately know you can call it and do certain other operations on it. But that's a detail of how the REPL prints out the values of arbitrary objects after evaluating them. It's not something you'll have access to when you're actually building programs to run standalone.

To see a concrete example of how ML-style opaque types are great at helping you make compiler-enforced guarantees in your programs, see Prof. Dan Grossman's excellent course using SML and especially his explanations of data type abstraction (Grossman, 2013, pp. 3--6).


Now that we've set up all the building blocks of modules, we can tackle one of ML's most flexible methods for modular code organisation: functors, functions which build modules. To illustrate functors, I'll re-implement a functorised module from James (2014), a functional

data structure. Here I show and explain my version.

The steps we will take here are:

  • Define an

  • Define a

  • Define a function which, given an object of type

    , returns an object of type

That's it--that's all a functor is.

trait Ordered[A] {
  type T = A

def compare(t1: T, t2: T): Int }

This is almost exactly the same as James'

trait; it's just that I've tried to stick closer to the SML names wherever possible. In essence, it sets up a signature for a module that can define a comparator function for any given datatype. To actually define the comparator, you just need to create a concrete module, an example of which we will see later.
trait MySet[A] {
  type E = A
  type T

val empty: T def insert(e: E)(t: T): T def member(e: E)(t: T): Boolean }

This is a signature for a module which can implement a set. You'll notice that I'm using both type parameters and abstract types in my signatures so far. The reason for using a type parameter is as follows. The set functions

declare parameters of type
(= Element), which is aliased to type parameter
. This allows concrete modules to pass in values of the concrete type they've been instantiated with to the functions, and these are internally 'seen' as values of type
without any need for type refinement or other type trickery.

The reason for using the type alias

when we already have the type parameter
is as follows. If and when we do implement a concrete module with the
signature, e.g.:
val IntSet: MySet[Int] = new
MySet[Int] { ... }
, we'll be able to define both
using the exact same types that we have in the trait. We won't have to say e.g.
def insert(e: Int)(t: T) = ...
; we'll just say
def insert(e:
E)(t: T) = ...
. This reduces the possibility for simple copy-paste errors and such.

In fact, you can see this in action in our next module:

object Modules {
  val IntOrdered: Ordered[Int] = new Ordered[Int] {
    override def compare(t1: T, t2: T) = t1 - t2

Here we're forced to start putting our concrete modules inside a containing scope because as mentioned earlier Scala

s can't reside in the toplevel.

We could have declared

in the toplevel using
IntOrdered extends Ordered[Int] { ... }
but that wouldn't have achieved opaque signature ascription; the module wouldn't have been as tightly controlled as it is with just the type
. So we'll define it inside a container module (
) and later import everything from
into the toplevel.
def UnbalancedSet[A](O: Ordered[A]): MySet[A] = // 1
  new MySet[A] { // 2
    sealed trait T
    case object Leaf extends T
    case class Branch(left: T, e: E, right: T) extends T

override val empty = Leaf

override def insert(e: E)(t: T) =
  t match {
    case Leaf => Branch(Leaf, e, Leaf)
    case Branch(l, x, r) =>
      val comp =, x) // 3

      if (comp < 0) Branch(insert(e)(l), x, r)
      else if (comp > 0) Branch(l, x, insert(e)(r))
      else t

override def member(e: E)(t: T) =
  t match {
    case Leaf => false
    case Branch(l, x, r) =>
      val comp =, x) // 4

      if (comp < 0) member(e)(l)
      else if (comp > 0) member(e)(r)
      else true


The rest of the implementation is almost exactly the same as in James (2014). I'll just point out the interesting bits from our perspective, which I've marked above with the numbers:

  1. This is the start of the functor definition. Notice how it's just a normal Scala function which happens to take what we think of as a concrete module as a parameter; and

  2. It happens to return what we think of as a new concrete module. Of course, at the level of the language syntax, they're both just simple objects that implement some interface.

    Notice also that in 1 we constrain the functor's return type to a more general

    instead of letting Scala infer the return type. This is in line with our general philosophy of doing ML-style opaque signature ascription, and also it's a convenience for whoever uses the functor as now they won't need to annotate their concrete module which they get from the functor call.
  3. And also 4. Here we actually use the comparator function defined in the

    signature to figure out if the value we were given is less than, greater than, or equal to, values we already have in the set. These two usages are exactly why the
    functor has a dependency on a module with signature
    . And the great thing is it can be any module that does any thing, as long as it ascribes to the
    signature (and, of course, also as long as it typechecks).

If you're curious about the mechanics of how functors work:

  • The functor doesn't define the output module as an inheritor of the input module. The modules don't necessarily have any nominal relationship.

  • Technically, the output module does hold a reference to the input module--but only because the former's methods close over (in the sense of being closures over) the latter. So this technique resembles composition, except you don't compose objects together yourself--you provide functors which know how to do it and let the user choose exactly which ones to compose later.

  • The output module knows only the signature (the upcast type) of the input module. I.e., it doesn't have any knowledge of the latter's internals. It relies only on what the signature allows it to know.

// UIS = UnbalancedIntSet
val UIS = UnbalancedSet(IntOrdered)

This is where we actually define a concrete module which behaves as a set of integers implemented as an unbalanced tree. All the expected operations work:

scala> import Modules._
import Modules._

scala> UIS.empty |> UIS.insert(1) |> UIS.insert(1) |> UIS.insert(2) |> UIS.member(1) res0: Boolean = true


Looking back at the various techniques in this article, we take away the following main points:

  • Scala object = ML module

  • Scala trait = ML signature

  • Scala upcasting type annotation = ML opaque signature ascription

  • Scala trait type parameter ~ ML opaque type but with the Scala-specific benefits that we can pass in values of this type to methods in the Scala trait without any special hackery; and also we simplify the types of the module's contents

  • Also Scala abstract type = ML opaque type

  • Alias a trait-internal type name to the trait's type parameter for easy reference in any derived trait or module

  • Scala function = ML functor

  • Annotate all Scala module and functor types to better hide implementation details

  • Need to put Scala

    s (modules) and
    s (functors) inside some other scope because we can't declare them in the toplevel. But we can import them into the toplevel once declared

The examples I've shown throughout this article are also all replicated in the the accompanying

file, as are a few more advanced examples.

Given the above, it seems very plausible that Scala can reliably encode ML-style modular programming--and beyond.


Encoding Standard ML modules in OO. (2014, April 11). Retrieved March 30, 2015, from

Grossman, D. (2013). CSE431: Programming Languages Spring 2013 Unit 4 Summary. University of Washington. Retrieved from

In scala, what's the idiomatic way to apply a series of composed functions to a value? (2013, December 13). Retrieved March 30, 2015, from

James, D. (2014, August 14). Scala's Modular Roots. Retrieved from

Martens, C. (2015, January). Modularity and Abstraction in Functional Programming. Presented at the Compose Conference, New York. Retrieved from

Odersky, M. (2014, August). Scala: The Simple Parts. Presented at the GOTO Conferences. Retrieved from

Okasaki, C. (1996). Purely functional data structures. Carnegie Mellon University, Pittsburgh, PA 15213. Retrieved from

Tofte, M. (1996). Essentials of Standard ML Modules. In J. Launchbury, E. Meijer, & T. Sheard (Eds.), Advanced Functional Programming (pp. 208--229). Springer Berlin Heidelberg. Retrieved from

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