"The Essence of the Iterator Pattern"(EIP) is the paper I liked the most last year. It gave me a brand new look over something which I had be using for years: the `for`

loop.

In this post I'll try to present some ideas of that paper and show how to implement the solution described by the authors using Scalaz-like code. A minimum previous exposure to functional programming, functors and monads will definitely help!

## What's in a for loop?

That was really what hooked me. What do you mean "what's in a `for`

loop"?. Is there anything magic in that construct I've been using for years?

The introduction of EIP shows an example of a `for`

loop to iterate on elements (not the C-like `for`

used with an index). I'm transmogrifying it here to Scala but the idea remains the same:

` val basket: Basket[Fruit] = Basket(orange, apple)`

var count = 0

val juices = Basket[Juice]()

for (fruit <- basket) {

count = count + 1

juices.add(fruit.press)

}

We start from a "container" of fruits: `Basket`

. It could actually be anything, a `List`

, a `Tree`

, a `Map`

... Then the `for`

loop actually does 3 things:

- it returns a container having
*the same "shape"*;`juices`

is still a`Basket`

- it
*accumulates*some kind of measure. Here, the number of fruits in the`count`

variable - it
*maps*the elements to other elements: pressing the fruits to get some juice

And this `for`

loop is actually not the most complex:

- the
`count`

variable could influence the mapping of elements:`juices.add(fruit.press(harder=count))`

- we could have several variables depending on each other:
`cumulative = cumulative + count`

- the mapping could also influence a "measure" variable:
`liquid = liquid + fruit.press.quantity`

The purpose of EIP is to show that the "essence" of what happens in the `for`

loop above can be abstracted by an *Applicative Traversal*. And the authors go on showing that given this *Applicative* abstraction, we get an incredible modularity for programming.

## The Applicative typeclass

How can an *Applicative traversal* be better than a for loop, and what does that even *mean*?? EIP has a lot of sentences and expressions which can be hard to grasp if you don't have a strong functional programming / Haskell background. Let's try to dissect that slowly and start with the formal definitions anyway.

### What is a Functor?

The first thing we need to talk about is the `Functor`

typeclass:

` trait Functor[F[_]] {`

def fmap[A, B](f: A => B): F[A] => F[B]

}

One way of interpreting a `Functor`

is to describe it as a computation of values of type `A`

. For example `List[A]`

is a computation returning several values of type `A`

(non-deterministic computation), `Option[A]`

is for computations that you may or may not have, `Future[A]`

is a computation of a value of type `A`

that you will get later, and so on. Another way of picturing it is as some kind of "container" for values of type `A`

.

Saying that those computations are `Functors`

is essentially showing that we can have a very useful way of combining them with regular functions. We can apply a function to the value that is being computed. Given a value `F[A]`

and a function `f`

, we can apply that function to the value with `fmap`

. For example, `fmap`

is a simple `map`

for a `List`

or an `Option`

.

### Pointed Functor

By the way, how do you even *create* a value of type `F[A]`

? One way to do that is to say that `F[_]`

is `Pointed`

:

` trait Pointed[F[_]] {`

def point[A](a: => A): F[A]

}

That is, there is a `point`

function taking a value of type `A`

and returning a `F[A]`

. For example, a regular `List`

is `Pointed`

just by using the constructor for Lists:

` object PointedList extends Pointed[List] {`

def point[A](a: => A) = List(a)

}

Then combining the 2 capabilities, pointed and functor, gives you a `PointedFunctor`

:

` trait PointedFunctor[F[_]] {`

val functor: Functor[F]

val pointed: Pointed[F]

def point[A](a: => A): F[A] = pointed.point(a)

def fmap[A, B](f: A => B): F[A] => F[B] = functor.fmap(f)

}

The `PointedFunctor`

trait is merely the aggregation of a `Pointed`

and a `Functor`

.

What about `Applicative`

then? We're getting to it, the last missing piece is `Applic`

.

### Applic

`Applic`

is another way to combine a "container" with a function.

Instead of using `fmap`

to apply the function to the computed value we suppose that the function is itself a computed value *inside the container F* (

`F[A => B]`

) and we provide a method `applic`

to apply that function to a value `F[A]`

:` trait Applic[F[_]] {`

def applic[A, B](f: F[A => B]): F[A] => F[B]

}

Let's take an example. Say I have a way to compute the price of a `Fruit`

when the market is open:

` def pricer(market: Market): Option[Fruit => Double]`

If the market is closed, `pricer`

returns `None`

, because we don't know what are the prices. Otherwise it returns a pricing function. Now if I have a `grow`

function possibly returning a `Fruit`

:

` def grow: Option[Fruit]`

Then, using the `Applic`

instance, you can price the `Fruit`

:

` val price: Option[Double] = applic(pricer(market)).apply(grow)`

The `price`

will necessarily be an `Option`

because you may not have a pricer nor a `Fruit`

to price. And a bit of renaming and pimping reveals why we're using the term "Applicative":

` val pricingFunction = pricer(market)`

val fruit = grow

val price: Option[Double] = pricingFunction ⊛ fruit

In a way we're just doing a normal function application, but we're just doing it *inside* the Applicative container. Now we have all the pieces to build the `Applicative`

functor that EIP is talking about.

### Applicative Functor

An `Applicative Functor`

is the aggregation of an `Applic`

and a `PointedFunctor`

:

` trait Applicative[F[_]] {`

val pointedFunctor: PointedFunctor[F]

val applic: Applic[F]

def functor: Functor[F] = new Functor[F] {

def fmap[A, B](f: A => B) = pointedFunctor fmap f

}

def pointed: Pointed[F] = new Pointed[F] {

def point[A](a: => A) = pointedFunctor point a

}

def fmap[A, B](f: A => B): F[A] => F[B] = functor.fmap(f)

def point[A](a: => A): F[A] = pointed.point(a)

def apply[A, B](f: F[A => B]): F[A] => F[B] = applic.applic(f)

}

Let's see how that can be implemented for a `List`

. `fmap`

and `point`

are straightforward:

` def fmap[A, B](f: A => B): F[A] => F[B] = (l: List[A]) => l map f`

def point[A](a: => A): F[A] = List(a)

`apply`

turns out to be more interesting because there are 2 ways to implement it, both of them being useful:

apply a list of functions to each element and gather the results in a List:

`def apply[A, B](f: F[A => B]): F[A] => F[B] = (l: List[A]) =>`

for { a <- l; func <- f } yield func(a)`zip`

the list of functions to the list of elements to apply each function to each element`def apply[A, B](f: F[A => B]): F[A] => F[B] = (l: List[A]) =>`

(l zip f) map (p => p._2 apply p._1)

There is even a third way to use `List`

as an `Applicative`

by using the fact that `List`

is a `Monoid`

. But more on that later, for now we still have to see how all of this relates to the `for`

loop...

## Traversing the structure

When we do a `for`

loop, we take a "structure" containing some elements and we "traverse" it to return:

- that same structure containing other elements
- a value computed from the structure elements
- some combination of above

Gibbons & Oliveira argue that *any kind of for loop* can be represented as the following `traverse`

operation:

` trait Traversable[T[_]] {`

def traverse[F[_] : Applicative, A, B](f: A => F[B]): T[A] => F[T[B]]

}

That is, if the container/structure of type `T`

has this `traverse`

function using an `Applicative F`

, then we can do whatever we would do with a `for`

loop on it.

To get a better feel for this `traverse`

function, we're going to implement the `Traversable`

trait for a binary tree and then we'll see how can we `loop`

on that tree.

### A Binary Tree

For all the other examples in this post, we're going to use a very simple binary tree:

` sealed trait BinaryTree[A]`

case class Leaf[A](a: A) extends BinaryTree[A]

case class Bin[A](left: BinaryTree[A], right: BinaryTree[A]) extends BinaryTree[A]

On the other hand, the first shot at the `Traversable`

implementation is barely readable!

` def BinaryTreeIsTraversable[A]: Traversable[BinaryTree] = new Traversable[BinaryTree] {`

def createLeaf[B] = (n: B) => (Leaf(n): (BinaryTree[B]))

def createBin[B] = (nl: BinaryTree[B]) =>

(nr: BinaryTree[B]) => (Bin(nl, nr): BinaryTree[B])

def traverse[F[_] : Applicative, A, B](f: A => F[B]):

BinaryTree[A] => F[BinaryTree[B]] = (t: BinaryTree[A]) => {

val applicative = implicitly[Applicative[F]]

t match {

case Leaf(a) => applicative.apply(applicative.point(createLeaf[B]))(f(a))

case Bin(l, r) =>

applicative.apply(applicative.apply(applicative.point(createBin[B]))(traverse[F, A, B](f).apply(l))).

apply(traverse[F, A, B](f).apply(r))

}

}

}

This is a shame because the corresponding Haskell code is so concise:

` instance Traversable Tree where`

traverse f (Leaf x) = pure Leaf ⊛ f x

traverse f (Bin t u) = pure Bin ⊛ traverse f t ⊛ traverse f u

A bit of pimping to the rescue and we can improve the situation:

`def traverse[F[_] : Applicative, A, B](f: A => F[B]): BinaryTree[A] => F[BinaryTree[B]] = (t: BinaryTree[A]) => {`

t match {

case Leaf(a) => createLeaf[B] ∘ f(a)

case Bin(l, r) => createBin[B] ∘ (l traverse f) <*> (r traverse f)

}

}

Informally the `traverse`

method applies the function `f`

to each node and "reconstructs" the tree by using the `apply`

method (`<*>`

) of the `Applicative`

functor.

That's certainly still some Ancient Chinese to you (as it was for me) so we'd better see the `traverse`

method at work now. But we need to take another detour :-)

### Applicative Monoid

One simple thing we might want to do, when iterating on a `BinaryTree`

, is to get the content of that tree in a `List`

. To do that, we're going to use the 3rd way to use `List`

as an `Applicative`

, as mentioned earlier. It turns out indeed that any `Monoid`

(what is it?) gives rise to an `Applicative`

instance but in a way that's a bit surprising.

` /** Const is a container for values of type M, with a "phantom" type A */`

case class Const[M, +A](value: M)

implicit def ConstIsPointed[M : Monoid] = new Pointed[({type l[A]=Const[M, A]})#l] {

def point[A](a: => A) = Const[M, A](implicitly[Monoid[M]].z)

}

implicit def ConstIsFunctor[M : Monoid] = new Functor[({type l[A]=Const[M, A]})#l] {

def fmap[A, B](f: A => B) = (c: Const[M, A]) => Const[M, B](c.value)

}

implicit def ConstIsApplic[M : Monoid] = new Applic[({type l[A]=Const[M, A]})#l] {

def applic[A, B](f: Const[M, A => B]) = (c: Const[M, A]) => Const[M, B](implicitly[Monoid[M]].append(f.value, c.value))

}

implicit def ConstIsPointedFunctor[M : Monoid] = new PointedFunctor[({type l[A]=Const[M, A]})#l] {

val functor = Functor.ConstIsFunctor

val pointed = Pointed.ConstIsPointed

}

implicit def ConstIsApplicative[M : Monoid] = new Applicative[({type l[A]=Const[M, A]})#l] {

val pointedFunctor = PointedFunctor.ConstIsPointedFunctor

val applic = Applic.ConstIsApplic

}

In the code above, `Const`

is the `Applicative`

instance for a given `Monoid`

. `Const`

contains values of type `T`

where `T`

is a `Monoid`

and we progressively establish what are the properties that `Const`

must satisfy to be `Applicative`

.

it must first be

`Pointed`

. Informally, the`point`

method puts the neutral element of the`Monoid`

in a`Const`

instancethen it must be a

`Functor`

. Here the`fmap`

function doesn't do anything but changing the type of`Const`

from`Const[M, A]`

to`Const[M, B]`

finally it must be an

`Applic`

where the`apply`

method of`Applic`

uses the`append`

method of the`Monoid`

to "add" 2 values and return the result in a`Const`

instance.

There is unfortunately a lot of typing vodoo thing here:

the type declaration for

`Const`

is`Const[A, +B]`

. It has a type parameter`B`

which is actually not represented by a value in the`Const`

class! It is a*phantom type*. But it is actually indispensable to match the type declarations of the typeclassesthe type

`F`

that is supposed to be`Applicative`

is...`({type l[A] = Const[T, A]})#l`

. Ouch, this deserves some explanation!

What we want is not so hard. The type `Const[A, B]`

has 2 type parameters. We need a way to *fix* `A`

to be `T`

and get the resulting type which will have only one type parameter. The expression above is the most concise way to get this desired type:

`{ type l = SomeType }`

is an anonymous type with a type member called`l`

. We can access that type`l`

in Scala by using`#`

:`{ type l = SomeType }#l`

Then, in

`{ type l[A] = SomeType[T, A] }#l`

,`l`

is a*higher-kinded type*, having a type variable`A`

(actually`SomeType[T, A]`

where`T`

is fixed)

That was a really long detour for a mere `for`

loop, isn't it? Now... profit!

### Contents of a BinaryTree...

We're going to use the `Traversable`

instance for the `BinaryTree`

and the `List Monoid Applicative`

to get the contents of a `BinaryTree`

:

` import Applicative._`

val f = (i: Int) => List(i)

val tree = Bin(Leaf(1), Leaf(2))

(tree.traverse[...](f)).value must_== List(1, 2)

Simple, for each element of the tree, we put it in a `List`

then we let the `List Monoid`

do its magic and aggregate all the results as we traverse the tree. The only difficulty here is the limits of Scala type inference. The `...`

stands for type annotations that the compiler requires:

` tree.traverse[Int, ({type l[A]=Const[List[Int], A]})#l](f)`

Not pretty :-(

*Update*: As pointed out by Ittay Dror in the comments,

`List[Int]`

is not an applicative by itself and we need to put this list into a `Const`

value to make it usable by the `traverse`

function. This is actually done by an implicit conversion method,

`liftConst`

, provided by the `Applicative object`

:` implicit def liftConst[A, B, M : Monoid](f: A => M): A => Const[M, B] = `

(a: A) => Const[M, B](f(a))

#### Profit time

Not everything is lost! We can encapsulate a bit the complexity in this case. We can extract part of the code above and create a `contents`

method which will work on *any* of `Traversable`

instance (assume I'm pimping the following examples so that I can write `tree.method`

instead of `method(tree)`

):

` val tree: BinaryTree[Int] = Bin(Leaf(1), Leaf(2))`

tree.contents must_== List(1, 2)

This is based on the following definition:

` def contents[A]: T[A] => List[A] = {`

val f = (a: A) => Const[List[A], Any](List(a))

(ta: T[A]) => traverse[({type l[U]=Const[List[A], U]})#l, A, Any](f).apply(ta).value

}

It also turns out that the `contents`

function is a specialized version of something even more generic, the `reduce`

function, working with any `Monoid`

:

` def contents[A]: T[A] => List[A] = reduce((a: A) => List(a))`

def reduce[A, M : Monoid](reducer: A => M): T[A] => M = {

val f = (a: A) => Const[M, Any](reducer(a))

(ta: T[A]) => traverse[({type l[A]=Const[M, A]})#l, A, Any](f).apply(ta).value

}

The `reduce`

function can traverse any `Traversable`

structure with a function mapping each element to a `Monoid`

element. We've used it to get the contents of the tree but can as easily get the number of elements:

` def count[A]: T[A] => Int = reduce((a: A) => 1)`

tree.count must_== 2

Can it get simpler than this :-)? Actually in that case it can! Since we don't need `(a: A)`

at all we can use `reduceConst`

:

` def reduceConst[A, M : Monoid](m: M): T[A] => M = reduce((a: A) => m)`

def count[A]: T[A] => Int = reduceConst(1)

It's like a Scala standard `reduce`

on steroids because instead you don't need to provide a binary operation, you just need a `Monoid`

instance.

### .... and shape of a BinaryTree

We've addressed the question of doing some kind of accumulation based on the elements in the tree, now we're going to "map" them.

#### Monads are Applicatives too!

The following `map`

method can be derived from the `traverse`

method (note that no type annotations are necessary in that case, yes!):

` def map[A, B](mapper: A => B) = (ta: T[A]) => traverse((a: A) => Ident(mapper(a))).apply(ta).value`

Here we're traversing with an `Applicative`

which is very simple, the `Ident`

class:

` case class Ident[A](value: A)`

The `Ident`

class is a simple wrapper around a value, nothing more. That simple class is an `Applicative`

. But how?

Easy. `Ident`

is actually a `Monad`

and we can construct an `Applicative`

instance from *every Monad*. This comes from the fact that a `Monad`

is both a `PointedFunctor`

and an `Applic`

:

` trait Monad[F[_]] {`

val pointed: Pointed[F]

val bind: Bind[F]

def functor: Functor[F] = new Functor[F] {

def fmap[A, B](f: A => B): F[A] => F[B] = (fa: F[A]) =>

bind.bind((a: A) => pointed.point(f(a))).apply(fa)

}

def pointedFunctor: PointedFunctor[F] = new PointedFunctor[F] {

val functor = Monad.this.functor

val pointed = Monad.this.pointed

}

def applic: Applic[F] = new Applic[F] {

def applic[A, B](f: F[A => B]) = a =>

bind.bind[A => B, B](ff => functor.fmap(ff)(a))(f)

}

def applicative: Applicative[F] = new Applicative[F] {

val pointedFunctor = Monad.this.pointedFunctor

val applic = Monad.this.applic

}

}

And the `Ident`

class is trivially a `Monad`

(having a `pointed`

and a `bind`

member):

` implicit def IdentIsMonad = new Monad[Ident] {`

val pointed = new Pointed[Ident] {

def point[A](a: => A): Ident[A] = Ident(a)

}

val bind = new Bind[Ident] {

def bind[A, B](f: A => Ident[B]): Ident[A] => Ident[B] =

(i: Ident[A]) => f(i.value)

}

}

We can use our brand new `map`

function now:

` tree.map((i: Int) => i.toString) must_== Bin(Leaf("1"), Leaf("2"))`

We can even use it to get the "shape" of our container and discard all the elements:

` tree.shape must_== Bin(Leaf(()), Leaf(()))`

The `shape`

method just maps each element to `()`

.

### Decompose / Compose

I recap. We implemented a *very* generic way to iterate over a structure, *any kind of structure* (as long as it's `Traversable`

), containing elements, *any kind of element*, with a function which does an "application", *any kind of application*. Among the possible "applications", we've seen 2 examples: collecting and mapping which are the essential operations that we usually do in a `for`

loop.

Specifically we were able to get the `contents`

of a tree and its `shape`

. Is there a way to compose those 2 operations into a `decompose`

operation that would get both the content and the shape at once? Our first attempt might be:

` def decompose[A] = (t: T[A]) => (shape(t), contents(t))`

tree.decompose must_== (Bin(Leaf(()), Leaf(())), List(1, 2))

This works but it is pretty naive because this requires 2 traversals of the tree. Is that possible to do just one?

#### Applicative products

This is indeed possible by noticing the following: *the product of 2 Applicatives is still an Applicative*.

Proof, proof. We define `Product`

as:

` case class Product[F1[_], F2[_], A](first: F1[A], second: F2[A]) {`

def tuple = (first, second)

}

I spare you the full definition of `Product`

as an `Applicative`

to just focus on the `Applic`

instance:

` implicit def ProductIsApplic[F1[_] : Applic, F2[_] : Applic] =`

new Applic[({type l[A]=Product[F1, F2, A]})#l] {

val f1 = implicitly[Applic[F1]]

val f2 = implicitly[Applic[F2]]

def applic[A, B](f: Product[F1, F2, A => B]) = (c: Product[F1, F2, A]) =>

Product[F1, F2, B](f1.applic(f.first).apply(c.first),

f2.applic(f.second).apply(c.second))

}

That's not too complicated, you just have to follow the types. What's more troubling is the amount of type annotations which are necessary to implement `decompose`

. Ideally we would like to write:

` def decompose[A] = traverse((t: T[A]) => shape(t) ⊗ contents(t))`

Where `⊗`

is an operation taking 2 `Applicative`

s and returning their product. Again the lack of partial type application for `Const`

muddies the whole (upvote SI-2712 please!):

`val shape = (a: A) => Ident(())`

val content = (a: A) => Const[List[A], Unit](List(a))

val product = (a: A) => (shape(a).⊗[({type l[T] = Const[List[A], T]})#l](content(a)))

implicit val productApplicative =

ProductIsApplicative[Ident, ({type l1[U] = Const[List[A], U]})#l1]

(ta: T[A]) => { val (Ident(s), Const(c)) =

traverse[({type l[V] = Product[Ident, ({type l1[U] = Const[List[A], U]})#l1, V]})#l, A, Unit](product).

apply(ta).tuple

(s, c)

}

We can improve the code sligthly by moving the implicit definition for `productApplicative`

inside the `Applicative`

companion object:

` object Applicative {`

...

implicit def ProductWithListIsApplicative[A[_] : Applicative, B] =

ProductIsApplicative[A, ({type l1[U] = Const[List[B], U]})#l1]

}

Then no `implicit val productApplicative`

is necessary and the `Applicative`

imports will be all we need.

#### Collection and dispersal

There is another way to do things "in parallel" while traversing the structure. The `collect`

method that we're going to build will do 2 things:

- it will
*accumulate*some kind of state, based on the elements that we meet - it will
*map*each element to another kind of element

So, as we're iterating, we can do a regular mapping while computing some kind of measure. But before that, we need to take a little detour (again?? Yes, again) with the `State`

monad.

##### The `State`

monad

The `State`

Monad is defined by:

` trait State[S, +A] {`

def apply(s: S): (S, A)

}

It is basically:

- an object keeping some previous "state", of type
`S`

- a method to extract a meaningful value from this "state", of type
`A`

- this method computes a new "state", of type
`S`

For example, a simple counter for the number of elements in a `List[Int]`

can be implemented by:

` val count = state((n: Int) => (n+1, ()))`

It takes the previous "count" number `n`

and returns the new state `n+1`

and the extracted value (`()`

here, because we don't need to extract anything special).

The `State`

type above is a `Monad`

. I encourage you to read "Learn You a Haskell" to get a better understanding on the subject. I will just show here that the `flatMap`

(or `bind`

) method of the `Monad`

typeclass is central in putting that `State`

to work:

` val count = (s: String) => state((n: Int) => (n+1, s + n))`

(count("a-") flatMap count flatMap count).apply(0) must_== (3, "a-012")

The `count`

function takes the latest computed string and returns a `State`

where we increment the current "state" by 1 and we have a new String as the result, where the current count is appended. So when we start with the string `"a-"`

and we `flatMap`

`count`

2 times, we get `(3, "a-012")`

where 3 is the number of times we've applied the `n+1`

function and `"a-012"`

the result of appending to the current string.

By the way, why do we need to `apply(0)`

?

When we do all the `flatMap`

s, we actually store "stateful computations". And they are executed only once we provide the initial state: `0`

!

##### Collecting elements

Let's now define a `collect`

operation on `Traversable`

which will help us to count:

` def collect[F[_] : Applicative, A, B](f: A => F[Unit], g: A => B) = {`

val applicative = implicitly[Applicative[F]]

import applicative._

val application = (a: A) => point((u: Unit) => g(a)) <*> f(a)

traverse(application)

}

This `collect`

operation, defined in EIP, is different from the `collect`

operation on Scala collections which is the equivalent of `filter + map`

. The `collect`

of EIP is using 2 functions:

`f: A => F[Unit]`

which collects data from each element "effectfully" (that is, possibly keeping state)`g: A => B`

which maps each element to something else

So we could say that the EIP `collect`

is a bit like `fold + map`

. Knowing this, we can use `collect`

to count elements *and* do some mapping:

` val count = (i: Int) => state((n: Int) => (n+1, ()))`

val map = (i: Int) => i.toString

tree.collect[({type l[A]=State[Int, A]})#l, String](count, map).apply(0) must_==

(2, Bin(Leaf("1"), Leaf("2")))

Here again the type annotations are obscuring the intent a bit and if type inference was perfect we would just read:

` val count = (i: Int) => state((n: Int) => (n+1, ()))`

val map = (i: Int) => i.toString

tree.collect(count, map).apply(0) must_== (2, Bin(Leaf("1"), Leaf("2")))

I don't know about you, but I find this a bit magical. With the `Applicative`

and `Traversable`

abstractions, we can assemble our program based on 2 independent functions possibly developed and tested elsewhere.

##### Dispersing elements

The next utility function proposed by EIP is the `disperse`

function. Its signature is:

` def disperse[F[_] : Applicative, A, B, C](f: F[B], g: A => B => C): T[A] => F[T[C]]`

What does it do?

`f`

is the`Applicative`

context that's going to evolve when we traverse the structure, but regardless of what the elements of type`A`

are`g`

is a function which, for each element of type`A`

says what to do with the current context value,`B`

, and map that element back to the structure

Please, please, a concrete example!

Say I want to mark each element of a `BinaryTree`

with its "number" in the Traversal (the "label"). Moreover I want to use the element name to be able to qualify this label:

` // a BinaryTree of Doubles`

val tree: BinaryTree[Double] = Bin(Leaf(1.1), Bin(Leaf(2.2), Leaf(3.3)))

// the "label" state returning integers in sequence

val labelling: State[Int, Int] = state((n: Int) => (n+1, n+1))

// for each element in the tree, and its label,

// produce a String with the name and label

val naming: Double => Int => String = (p1: Double) => (p2: Int) => p1+" node is "+p2

// testing by applying an initial state (label `0`) and

// taking the second element of the pair `(last label, resulting tree)`

tree.disperse[elided for sanity](labelling, naming).apply(0)._2 must_==

Bin(Leaf("1.1 node is 1"), Bin(Leaf("2.2 node is 2"), Leaf("3.3 node is 3")))

Note that the `naming`

function above is curried. A more familiar way to write it would be:

` val naming: (Double, Int) => String = (p1: Double, p2: Int) => p1+" node is "+p2`

But then you would have to curry that function to be able to use it with the `disperse`

function:

` tree.disperse[...](labelling, naming.curried)`

The implementation of `disperse`

is:

` def disperse[F[_] : Applicative, A, B, C](f: F[B], g: A => B => C) = {`

val applicative = implicitly[Applicative[F]]

import applicative._

val application = (a: A) => point(g(a)) <*> f

traverse(application)

}

It is using the very capabilities of the applicative functor, the `point`

method and the `<*>`

application.

### An overview of traversals

We've seen in the 2 examples above that we get different, specialized, versions of the `traverse`

function by constraining how mapping and `Applicative`

effects occur. Here's a tentative table for classifying other specialized versions of the `traverse`

function:

function | map element | create state | mapped depend on state | state depend on element |
---|---|---|---|---|

`collect` | X | X | X | |

`disperse` | X | X | X | |

`measure` | X | X | ||

`traverse` | X | X | X | X |

`reduce` | X | X | ||

`reduceConst` | X | |||

`map` | X |

The only function we haven't shown before is `measure`

. It is mapping and accumulating state but this accumulation does not depend on the current element. Here's an example:

` val crosses = state((s: String) => (s+"x", ()))`

val map = (i: Int) => i.toString

tree.measure(crosses, map).apply("") must_==

("xxx", Bin(Leaf("1"), Bin(Leaf("2"), Leaf("3"))))

Other than not looking very useful, the code above is also lying! It is not possible to have a `measure`

function accepting a `State`

monad without having to provide the usual ugly type annotations. So the actual example is:

` tree.measureState(crosses, map).apply("") must_== `

("xxx", Bin(Leaf("1"), Bin(Leaf("2"), Leaf("3"))))

where `measureState`

is a specialization of the `measure`

method to `States`

. I think that one take-away of this post is that it might be beneficial to specialize a few generic functions in Scala , like `traverse`

, `collect`

... for `Const`

and `State`

in order to avoid type annotations.

## Composing traversals

There's another axis of composition that we haven't exploited yet.

In a `for`

loop, without thinking about it, you may write:

` for (a <- as) {`

val currentSize = a.size

total += currentSize

result.add(total)

}

In the body of that `for`

loop, you have statements with dependency on each other. In an `Applicative`

traversal, this translates to the *Sequential composition* of Applicatives. From 2 `Applicatives`

, we can create a third one which is their *Sequential composition*. More precisely, this means that if `F1[_]`

and `F2[_]`

are `Applicatives`

then `F1[F2[_]]`

is an `Applicative`

as well. You want the demonstration? Ok, go.

First, we introduce a utility function on `ApplicFunctor`

s:

` def liftA2[A, B, C](function: A => B => C): F[A] => F[B] => F[C] = `

fa => applic.applic(functor.fmap(function)(fa))

`liftA2`

allows to `lift`

a regular function of 2 arguments to a function working on the `Applicative`

arguments. This is using the fact that an `ApplicFunctor`

is a `Functor`

so we can apply `function: A => B => C`

to the "`a`

in the box", to get a `F[B => C]`

"in the box". And then, an `ApplicFunctor`

is an `Applic`

, so we can "apply" `F[B]`

to get a `F[C]`

Armed with this function, we can write the `applic`

method for `F1[F2[_]]`

:

` implicit val f1ApplicFunctor = implicitly[ApplicFunctor[F1]]`

implicit val f2ApplicFunctor = implicitly[ApplicFunctor[F2]]

val applic = new Applic[({type l[A]=F1[F2[A]]})#l] {

def applic[A, B](f: F1[F2[A => B]]) = (c: F1[F2[A]]) => {

f1ApplicFunctor.liftA2((ff: F2[A => B]) => f2ApplicFunctor.apply(ff))(f).apply(c)

}

}

It's not so easy to get an intuition for what the code above is doing except that saying that we're using previous definitions to allow a `F1[F2[A => B]]`

to be applied to `F1[F2[A]]`

.

In mere mortal terms, this means that if we do an `Applicative`

computation inside a loop and if we reuse that computation in another `Applicative`

computation, we still get an `Applicative`

computation. The EIP illustration of this principle is a crazy function, the `assemble`

function.

### The `assemble`

function

The `assemble`

function takes the *shape* of a `Traversable`

and a list of elements. If there are enough elements it returns `Some[Traversable]`

filled with all the elements (+ the reminder), otherwise it returns `None`

(and an empty list). Let's see it in action:

` // the "shape" to fill`

val shape: BinaryTree[Unit] = Bin(Leaf(()), Leaf(()))

// we assemble the tree with an exact list of elements

shape.assemble(List(1, 2)) must_== (List(), Some(Bin(Leaf(1), Leaf(2))))

// we assemble the tree with more elements

shape.assemble(List(1, 2, 3)) must_== (List(3), Some(Bin(Leaf(1), Leaf(2))))

// we assemble the tree with not enough elements

shape.assemble(List(1)) must_== (List(), None)

What's the implementation of the `assemble`

function? The implementation uses 2 `Monads`

(which are also `Applicatives`

as we know now):

- the
`State[List[Int], _] Monad`

is going to keep track of what we've already consumed - the
`Option[_] Monad`

is going to provide, or not, an element to put in the structure - the composition of those 2 monads is
`State[List[Int], Option[_]]`

(our`F1[F2[_]]`

in the`ApplicFunctor`

definitions above

So we just need to traverse the `BinaryTree`

with one function:

`def takeHead: State[List[B], Option[B]] = state { s: List[B] =>`

s match {

case Nil => (Nil, None)

case x :: xs => (xs, Some(x))

}

}

The `takeHead`

function is a `State`

instance where each state application removes the first element of the list of elements if possible, and returns it in an Option.

This is why the result of the `assemble`

function, once we apply it to a list of elements, is of type `(List[Int], Option[BinaryTree[Int]])`

.

#### A recursive implementation

Just for the fun of comparison, I'm going to write a recursive version doing the same thing:

` def assemble(es: List[Int], s: BinaryTree[Unit]) : (List[Int], Option[BinaryTree[Int]]) = {`

(es, s) match {

case (Nil, _) => (es, None)

case (e :: rest, Leaf(())) => (rest, Some(Leaf(e)))

case (_, Bin(left, right)) => {

assemble(es, left) match {

case (l, None) => (l, None)

case (Nil, Some(l)) => (Nil, None)

case (rest, Some(l)) => assemble(rest, right) match {

case (r, None) => (r, None)

case (finalRest, Some(r)) => (finalRest, Some(Bin(l, r)))

}

}

}

}

}

assemble(List(1, 2, 3), shape) must_== (List(3), Some(Bin(Leaf(1), Leaf(2))))

It works, but it makes my head spin!

#### A classical for-loop implementation

By the way, what would be the *real* `for`

loop version of that functionality? That one is not so easy to come up with because AFAIK there's no easy way to iterate on a `BinaryTree`

to get a similar `BinaryTree`

with just a `for`

loop! So, for the sake of the argument, we're going to do something similar with just a `List`

structure:

` def assemble[T](es: List[T], shape: List[Unit]) = {`

var elements = es

var list: Option[List[T]] = None

for (u <- shape) {

if (!elements.isEmpty) {

list match {

case None => list = Some(List(elements.first))

case Some(l) => list = Some(l :+ elements.first)

}

elements = elements.drop(1)

} else {

list = None

}

}

(elements, list)

}

assemble(List(1, 2, 3), List((), ())) must_== (List(3), Some(List(1, 2)))

Contrast and compare with:

` List((), ()).assemble(List(1, 2, 3)) must_== (List(3), Some(List(1, 2)))`

where you just define `List`

as a `Traversable`

:

` implicit def ListIsTraversable[A]: Traversable[List] = new Traversable[List] {`

def traverse[F[_] : Applicative, A, B](f: A => F[B]): List[A] => F[List[B]] =

(l: List[A]) => {

val applicative = implicitly[Applicative[F]]

l match {

case Nil => applicative.point(List[B]())

case a :: rest =>

((_:B) :: (_: List[B])).curried ∘ f(a) <*> (rest traverse f)

}

}

}

The Applicative composition is indeed very powerful, but we're going to see that there are other ways to compose functions and use them with `Traversables`

.

### Monadic composition

This paragraph is exploring the fine relationships between applicative composition and monadic composition when doing traversals. We've seen that `Applicative`

instances can be composed and that `Monads`

can be `Applicative`

. But `Monads`

can also be composed using the so-called *Kleisli composition*. If we have:

` val f: B => M[C]`

val g: A => M[B]

Then

` val h: A => M[C] = f ∎ g // is also a function from a value to a Monad`

If we have 2 "monadic" functions `f`

and `g`

, we can then compose them, in the Kleisli sense, and use the composed version for a traversal. Indeed we can, but does this traversal have "nice properties"? Specifically, do we have:

` traverse(f ∎ g) == traverse(f) ∎ traverse(g)`

The answer is... it depends.

#### Monad commutativity

EIP shows that, if the `Monad`

is *commutative*, then this will always be true. What is a *commutative* `Monad`

you ask?

A `Monad`

is commutative if for all `mx: M[X]`

and `my: M[Y]`

we have:

` val xy = for {`

x <- mx

y <- my

} yield (x, y)

val yx = for {

y <- my

x <- mx

} yield (x, y)

xy == yx

This is *not* the case with the `State Monad`

for example:

` val mx = state((n: Int) => (n+1, n+1))`

val my = state((n: Int) => (n+1, n+1))

xy.apply(0) must_== (2, (1, 2))

yx.apply(0) must_== (2, (2, 1))

#### Monadic functions commutativity

Another slightly different situation is when we have a non-commutative `Monad`

but commutative functions:

` val plus1 = (a: A) => state((n: Int) => (n+1, a))`

val plus2 = (a: A) => state((n: Int) => (n+2, a))

val times2 = (a: A) => state((n: Int) => (n*2, a))

Here `plus1`

and `times2`

are not commutative:

` (0 + 1) * 2 != (0 * 2) + 1`

However it is obvious that `plus1`

and `plus2`

are commutative. What does that mean when we do a traversal?

If we traverse a simple List of elements using monadic composition we get:

`List(1, 2, 3).traverse(times2 ∎ plus1) === 22`

`List(1, 2, 3).traverse(times2) ∎ List(1, 2, 3).traverse(plus1) === 32`

We get different results. However, when `f`

and `g`

commute we get the same result:

`List(1, 2, 3).traverse(plus2 ∎ plus1) === 10`

`List(1, 2, 3).traverse(plus2) ∎ List(1, 2, 3).traverse(plus1) === 10`

#### Applicative composition vs Monadic composition

Another question we can ask ourselves is: if we consider the *monadic* functions as *applicative* functions (because each `Monad`

is `Applicative`

), do we get the nice "distribution" property we're after? The answer is yes, even when the functions are not commutative:

`List(1, 2, 3).traverse(times2 ⊡ plus1) === 4`

`List(1, 2, 3).traverse(times2) ⊡ List(1, 2, 3).traverse(plus1) === 4`

Well... more or less. The real situation is a bit more complex. `List(1, 2, 3).traverse(times2 ⊡ plus1)`

returns a `State[Int, State[Int, List[Int]]]`

while the second expression returns a `State[Int, List[State[Int, Int]]`

so what I'm hiding here is some more manipulations to be able to query the final result with some kind of `join`

.

## Conclusion

You wouldn't believe it but I've only shown here half of the ideas presented in EIP!

To finish off this post here's 3 take-away points that I've learned while writing it:

functional programming is also about mastering some of these higher-level control structures like

`Applicative`

. Once you master them, your toolbox expands considerably in power (just consider the`assemble`

example)Scalaz is an incredible library but somewhat obscure to the beginner. For this post I've rewritten all the typeclasses I needed to have, and lots of examples (using specs2 of course). That gave me a much better understanding of the Scalaz functionality. You may consider doing the same to learn Scalaz (my code is available on github)

Scala is lacking behind Haskell in terms of type inference and it's a real pain for higher-order, generic programming. This can be sometimes encapsulated away by specializing generic functions to very common types (like

`traverseState`

instead of`traverse`

). Again, please upvote SI-2712!

Finally, I want to mention that there are many other Haskell functional pearls waiting to be transliterated to Scala. I mean, it's a shame that we don't have yet any equivalent for "Learn you a Haskell" or "Typeclassopedia" in the Scala world. I hope that my post, like this other one by Debasish Ghosh, will also contribute to bridge the gap.