# Kleisli Categories

In the last chapter, we came across a frankly startling realization – that our machines need not be differentiated from the values they manipulate. As it turns out, our symbolic computations are powerful enough to allow machines to themselves manipulate other machines. We called such things higher-order machines, and we studied some of their properties. In particular, we looked at how this strange bedfellow, our old friend machine composition, turned out to be nothing more than a specific example of a higher-order machine.

Such an occurrence should be an excuse to celebrate. Not only have we come up with a more parsimonious formulation of our toolset, but we have also reconciled seemingly-disjoint concepts. Our world-view is a little nicer now than it was, and, having discovered that these two things are one-and-the-same, we pave the way for finding other examples of the same pattern.

As promised, there are more ways of composing machines together than just compose : (a -> b) -> (b -> c) -> (a -> c), and the pattern hiding behind them will form the tool we need to create machines capable of remembering the past.

## A Pattern of Failures

Some time ago, we discussed a polymorphic type, called Maybe a. As you might recall, Maybe a is defined as such:

type Maybe a = Just a
| Nothing

and it represented a value of type a which may or may not be there. A missing value was represented as Nothing, but a value which did in fact exist was represented by Just value, for some value : a.

We originally used a Maybe Bool to represented whether we wanted to read or write from a Snap machine (the Bool part), or whether we wanted to leave the latch alone (a Nothing input). And so, as an input, a Maybe a corresponds to something which may or may not be desired.

Remember, we also made a type Nat, which corresponded to the natural numbers (0, 1, 2, and so on). It was defined like this:

type Nat = Zero
| S Nat

which means that a Natural number is either Zero, or the successive number of a Natural number. Constructing a succ function to get the successive number of a given input is rather easy:

succ : Nat -> Nat
succ a = S a

but note that it’s much harder to get the previous number:

prev : Nat -> Nat
prev (S num) = num
prev Zero    = ???

The issue of course, is that in our definition of Nat, there is no number that is previous to Zero! Zero is the first number, and we can’t go back any further than that! The problem is that sometimes there is no answer to the question we’re asking in prev. You could say that maybe there is no answer.

And so, as you might imagine, we can use Maybe to work backwards:

prev : Nat -> Maybe Nat
prev (S num) = Just num
prev Zero    = Nothing

We can think of a function which outputs a Maybe something as a computation which could possibly fail. A computation which maybe might not actually produce any output.

In blunt, Maybe a describes an a which might go wrong.

What if, for some reason, we wanted to add together two numbers. But, to make things interesting, we don’t want to add the numbers we’re given – we’d live to add the prevs of the numbers we’re given. Now obviously, this is a computation that can go wrong. If either of the numbers we are given to add together are Zero, clearly there is no prev to take, and so the entire computation must fail.

addPrevs : Nat -> Nat -> Maybe Nat
where
addMaybeNats : Maybe Nat -> Maybe Nat -> Maybe Nat
addMaybeNats (Just ma) (Just mb) = Just (ma + mb)
addMaybeNats _         _         = Nothing

Here, we’ve defined our function addPrevs in terms of a lemma addMaybeNats, which adds two Nats together if they’re both tagged with Just – which is to say that they both definitely exist. The result is also tagged with a Just. In any other case (either or both of the inputs are Nothing), addMaybeNats gives back a Nothing.

We can view addPrevs as a kind of machine composition – it’s made out of two machines (prev), either of which can fail. addPrevs itself inherits this “can fail” property from the submachines from which it is built. However, it should be pointed out that this is not the usual kind of composition with which we are familiar.

Our usual composition was of the form

compose : (a -> b) -> (b -> c) -> (a -> c)

which is to say that if you gave it a machine from a -> b (read: “from a to b”) and another machine from b -> c, compose could combine them for you into a single machine which was from a -> c. It did this by gluing the output of the first machine to the input of the second.

However, in the case of our addPrevs, we want a composition that is somehow “smart enough” to handle failure. A method of composition that knows that if one of the sub-machines fails, the entire thing has to fail as well.

In short, what we want is a machine:

composeMaybe : (a -> Maybe b) -> (b -> Maybe c) -> (a -> Maybe c)

composeMaybe is a machine which connects a machine a -> Maybe b to a machine b -> Maybe c. It does this by attempting to run the first machine, and if it succeeds, it passes connects the successful output from the first machine to the input of the second machine. The resulting machine takes an a and outputs a Maybe c, where the computation can spit out a Nothing if either the first or the second machine fails.

As a machine diagram, it might look something like this:

where only one of the Just c or Nothing wires will be raised at any given time, depending on whether or not the composed machine succeeded.

We can actually write such a thing as a symbolic computation as well:

composeMaybe : (a -> Maybe b) -> (b -> Maybe c) -> (a -> Maybe c)
composeMaybe m1 m2 = composed
where
composed : a -> Maybe c
composed a = bridge (m1 a)

bridge : Maybe b -> Maybe c
bridge (Just b) = m2 b
bridge Nothing  = Nothing

which, as promised, is another way we can combine two machines.

## Kleisli Categories

This function, composeMaybe : (a -> Maybe b) -> (b -> Maybe c) -> (a -> Maybe c) is what’s known as a Kleisli composition. A Kleisli composition is a way of combining two machines which output things that are not just plain types (they are of the form Maybe a, for example, rather than just a).

In order for such a thing to truly be a Kleisli composition, we must have one additional function, which we will call inject. For Maybe, it’s really simple:

injectMaybe : a -> Maybe a
injectMaybe a = Just a

All together, the combination of these three things: Maybe, compose and inject form what’s known as a Kleisli category. Kleisli categories will turn out to be a huge amount of interest to us in the near future. Because of the fact that the functions composeMaybe and injectMaybe exist, we say that “Maybe is Kleisli”.

If “Maybe can be Kleisli”, maybe other things can be as well? And indeed, being Kleisli is nothing more than an interesting pattern that forms around certain type constructors, such as Maybe. We will look at why Kleisli categories are interesting in a moment, but for now, the important part is merely that there is a common pattern in what it means to be Kleisli.

As a rather contrived example, we can look at a new type:

type Identity a = Identity a

Identity a is a boring thing, it is merely a wrapper around a, but serves no other purpose. Observe that we can write injectIdentity and composeIdentity functions:

injectIdentity : a -> Identity a
injectIdentity a = Identity a

and

composeIdentity : (a -> Identity b) -> (b -> Identity c) -> (a -> Identity c)
composeIdentity m1 m2 = composed
where
composed : a -> Identity c
composed a = bridge (m1 a)

bridge : Identity b -> Identity c
bridge (Identity b) = m2 b

As such, the combination of Identity (note, not Identity a), injectIdentity and composeIdentity form a Kleisli category, and so we can say too that Identity is also Kleisli.

So. We have identified a common pattern: a type constructor which takes a single type variable (call it m for now), a function inject : a -> m a, and a function compose : (a -> m a) -> (b -> m c) -> (a -> m c). Any m which satisfies such things is Kleisli. But what does that mean?

It means that we have those functions: inject and compose for our type constructor m.

Sorry, that was cheeky of me. The question we really wanted to ask was “why should we care whether something is Kleisli or not?”

Well, being Kleisli means that we have some notion of what it means to “compute in a some kind of environment or context.” The particular context/environment we’re computing in depends on our choice of m, and for every m, we will get a different environment. The environment behind the Kleisli-ness of Maybe is “computations which might fail”, while the environment behind Identity is “computations without any particular context”.

In that light, Identity sounds stupid, and it is. The key point is that even if we have an environment, we can choose to ignore it.

All of this should be oddly reminiscent of our earlier discussions of the RealWorld – computations which take place “in the context of reality”, as opposed to our toy logic world that we’ve been working in thus far. As it turns out, reality itself is Kleisli, and this fact is our first glimpse into the mindset that maybe all of this isn’t merely just a toy logic world after all.

## Exercises

1. Either Nat is Kleisli under some circumstances. One of those circumstances is when composeEither “short circuits” if any of its results are Left (similar to how composeMaybe short circuits whenever any of its results are Nothing).

Write the functions injectEither : a -> Either Nat a, and composeEither : (a -> Either Nat b) -> (b -> Either Nat c) -> (a -> Either Nat c) to prove that Either Nat is Kleisli.