# Review: Codata in Action

Today we’re looking at Codata in Action by Downen, Sullivan, Ariola and Peyton Jones. Please excuse my lack of any sort of theme as I review papers, I’m just picking things that seem interesting to me. This paper in particular was recommended to me by Jonathan Lorimer in the inaugural edition of the Cofree Coffee Cast, which is an excellent podcast that I may or may not be involved in.

So anyway, today we’re looking at codata. What’s that? Essentially, lazy records. By virtue of being lazy, Haskell makes the differentiation between data and codata rather hard to spot. The claim is that functional languages are big on data, object-oriented languages really like codata, and that everything you can do with one can be emulated by the other, which is useful if you’d like to compile FP to OOP, or vice versa.

Codata, like the name implies, have a lot of duals with regular ol’ data. The paper introduces a bunch of parallels between the two:

Data Codata
Concerned with construction Concerned with destruction
Define the types of constructors Define the types of destructors
Directly observable Observable only via their interface
Common in FP Common in OOP
Initial algebras Terminal coalgebras
Algebraic data structures Abstract data structures
data class

The paper’s claim is that codata is a very useful tool for doing real-world work, and that we are doing ourselves a disservice by not making it first-class:

While codata types can be seen in the shadows behind many examples of programming—often hand-compiled away by the programmer—not many functional languages have support for them.

That’s a particularly interesting claim; that we’re all already using codata, but it’s hidden away inside of an idiom rather than being a first-class citizen. I’m always excited to see the ghosts behind the patterns I am already using.

## Examples of Codata🔗

The paper gives a big list of codata that we’re all already using without knowing it:

### Church/Boehm–Berarducci encodings🔗

data Bool where
True :: Bool
False :: Bool

I can instead do the usual Church encoding:

codata Bool where
if :: Bool -> a -> a -> a

which I might express more naturally in Haskell via:

ifThenElse :: Bool -> a -> a -> a
ifThenElse True  t _ = t
ifThenElse False _ f = f

(I suspect this is that “hand-compiling away” that the authors were talking about)

However, in the codata presentation, I can recover true and false by building specific objects that fiddle with their arguments just right (using copatterns from a few weeks ago):

True : Bool
if True t _ = t

False : Bool
if False _ f = f

That’s neat, I guess!

As a follow-up, we can try talking about Trees. Rather than the usual data definition:

data Tree t where
Leaf :: t -> Tree t
Branch :: Tree t -> Tree t -> Tree t

walk :: (t -> a) -> (a -> a -> a) -> Tree t -> a

we can do it in codata:

codata Tree t where
walk :: Tree t -> (t -> a) -> (a -> a -> a) -> a

and reconstruct the “constructors:”

Leaf x :: t -> Tree t
walk (Leaf t) mk _ = mk t

Branch :: Tree t -> Tree t -> Tree t
walk (Branch l r) mk comb = comb (walk l mk comb) (walk r mk comb)

The presentation in the paper hand-compiles Tree!data into two declarations:

codata TreeVisitor t a where
{ visitLeaf   :: TreeVisitor t a -> t -> a
, visitBranch :: TreeVisitor t a -> a -> a -> a
}

codata Tree t where
walk :: Tree t -> TreeVisitor t a -> a

which is the same thing, but with better named destructors.

### Demand-Driven Programming / Laziness🔗

You know the problem. You’re programming some search, and want to have a stopping depth. Maybe you’re writing a chessai and don’t want to wait until the ends of time for the search to finish. Easy enough, right? Just add an integer that counts down whenever you recurse:

search :: Int -> Position -> [Position]
search 0 _ = []
search n as = -- do lots of work

So you set n to something that seems reasonable, and get your moves back. But then you realize you had more time to kill, so you’d like to resume the search where you left off. But there’s no good way to do this, and starting back from the beginning would involve wasting a lot of effort. You can certainly program around it, but again, it’s hand-compiling away codata.

Instead, we can express the problem differently:

codata Rose a where
{ node :: Rose a -> a
, children :: Rose a -> [Rose a]
}

Recall that codata is built-in lazy, so by repeatedly following children we can further explore the tree state. In OOP I guess we’d call this a generator or an iterator or something. Probably a factory of some sort.

But once we have Rose we can implement pruning:

prune :: Int -> Rose Position -> Rose Position
node (prune n t) = node t
children (prune 0 t) = []
children (prune n t) = fmap (prune (n - 1)) $children t I really like copattern matching. ### Abstract Data Types🔗 You know how we have extentional and intentional definitions for sets? Like, compare: newtype Set a = Set { unSet :: [a] } lookup :: Set a -> a -> Bool lookup s t = elem t$ unset s

vs

newtype Set a = Set { lookup :: a -> Bool }

That latter version is the Church-encoded version. Instead we can give an interface for both sorts of sets as codata, defined by their interface as sets. This is everyday OOP stuff, but a little weird in FP land:

codata Set a where
{ isEmpty :: Set a -> Bool
, lookup :: Set a -> a -> Bool
, insert :: Set a -> a -> Set a
, union :: Set a -> Set a -> Set a
}

My dudes this is just an interface for how you might want to interact with a Set. We can implement the listy version from above:

listySet :: [a] -> Set a
isEmpty (listySet ls) = null ls
lookup (listySet ls) a = elem a ls
insert (listySet ls) a = listSet (a : ls)
union (listySet ls) s = foldr insert s ls

but we can also implement an infinitely big set akin to our functiony-version:

evensUnion :: Set Int -> Set Int
isEmpty (evensUnion s) = False
lookup (evensUnion s) a = mod a 2 == 0 || lookup a s
insert (evensUnion s) a = evensUnion $insert s a union (evensUnion s) s' = evensUnion$ union s s'

This thing is a little odd, but evensUnion is the set of the even numbers unioned with some other set. The built-in unioning is necessary to be able to extend this thing. Maybe we might call it a decorator pattern in OOP land?

### Pre and Post Conditions🔗

One last example, using type indices to represent the state of something. The paper gives sockets:

data State = Raw | Bound | Live

type Socket :: State -> Type
codata Socket i where
{ bind    :: Socket 'Raw -> String -> Socket 'Bound
, connect :: Socket 'Bound -> Socket 'Live
, send    :: Socket 'Live -> String -> ()
, recv    :: Socket 'Live -> String
, close   :: Socket 'Live -> ()
}

The type indices here ensure that we’ve bound the socket before connecting to it, and connected to it before we can send or receive.

Contrast this against what we can do with GADTs, which tell us how something was built, not how it can be used.

## Converting Between Data and Codata🔗

Unsurprisingly, data and codata are two sides of the same coin: we can compile one to the other and vice versa.

### Data to Codata🔗

Going from data to codata is giving a final encoding for the thing; as we’ve seen, this corresponds to the Boehm-Berarducci encoding. The trick is to replace the type with a function. Each data constructor corresponds to an argument of the function, the type of which is another function that returns a, and as arguments takes each argument to the data constructor. To tie the knot, replace the recursive bits with a.

Let’s take a look at a common type:

data List a = Nil | Cons a (List a)

We will encode this as a function, that returns some new type variable. Let’s call it x:

... -> x

and then we need to give eliminators for each case:

elim_nil -> elim_cons -> x

and then replace each eliminator with a function that takes its arguments, and returns x. For Nil, there are no arguments, so it’s just:

x -> elim_cons -> x

and then we do the same thing for Cons :: a -> List a -> List a:

x -> (a -> List a -> x) -> x

of course, there is no List a type anymore, so we replace that with x too:

x -> (a -> x -> x) -> x

And thus we have our codata-encoded list. For bonus points, we can do a little shuffling and renaming:

(a -> b -> b) -> b -> b

which looks very similar to our old friend foldr:

foldr :: (a -> b -> b) -> b -> [a] -> b

In fact, a little more reshuffling shows us that foldr is exactly the codata transformation we’ve been looking for:

foldr :: [a] -> ((a -> b -> b) -> b -> b)

Cool. The paper calls this transformation the “visitor pattern” which I guess makes sense; in order to call this thing we need to give instructions for what to do in every possible case.

This is an encoding of the type itself! But we also need codata encodings for the data constructors. The trick is to just ignore the “handlers” in the type that don’t correspond to your constructor. For example:

Nil :: (a -> b -> b) -> b -> b
Nil _ nil = nil

Cons
:: a
-> ((a -> b -> b) -> b -> b)
-> (a -> b -> b)
-> b
-> b
Cons head tail cons nil = cons nil (tail cons nil)

Really, these write themselves once you have an eye for them. One way to think about it is that the handlers are “continuations” for how you want to continue. This is the dreaded CPS transformation!

### Codata to Data🔗

Let’s go the other way too. Appropriately, we can use codata streams:

codata Stream a where
{ head :: Stream a -> a
, tail :: Stream a -> Stream a
}

I’m winging it here, but it’s more fun to figure out how to transform this than to get the information from the paper.

The obvious approach here is to just turn this thing directly into a record by dropping the Stream a -> part of each field:

data Stream a = Stream
, tail :: Stream a
}

While this works in Haskell, it doesn’t play nicely with strict languages. So, we can just lazify it by sticking each one behind a function:

data Stream a = Stream
{ head :: () -> a
, tail :: () -> Stream a
}

Looks good to me. But is this what the paper does? It mentions that we can tabulate a function, e.g., represent Bool -> String as (String, String). It doesn’t say much more than this, but we can do our own research. Peep the Representable class from adjunctions:

class Distributive f => Representable f where
type Rep f :: *
tabulate :: (Rep f -> a) -> f a
index    :: f a -> Rep f -> a

This thing is exactly the transformation we’re looking for; we can “represent” some structure f a as a function Rep f -> a, and tabulating gets us back the thing we had in the first place.

So the trick here is then to determine f for the Rep f that corresponds to our codata structure. Presumably that thing is exactly the record we worked out above.

What’s interesting about this approach is that it’s exactly scrap-your-typeclasses. And it’s exactly how typeclasses are implemented in Haskell. And last I looked, it’s the approach that Elm recommends doing instead of having typeclasses. Which makes sense why it’s annoying in Elm, because the language designers are forcing us to hand-compile our code! But I don’t need to beat that dead horse any further.

## Thoughts🔗

Something that piqued my interest is a quote from the paper:

Functional langauges are typically rich in data types … but a paucity of codata types (usually just function types.)

This is interesting, because functions are the only non-trivial source of contravariance in Haskell. Contravariance is the co- version of (the poorly named, IMO) covariance. Which is a strong suggestion that functions are a source of contravariance because they are codata, rather than contravariance being a special property of functions themselves.

I asked my super smart friend Reed Mullanix (who also has a great podcast episode), and he said something I didn’t understand about presheafs and functors. Maybe presheafs would make a good next paper.

## Conclusions🔗

This was a helpful paper to to wrap my head around all this codata stuff that smart people in my circles keep talking about. None of it is new, but as a concept it helps solidify a lot of disparate facts I had rattling around in my brain. Doing this final tagless encoding of data types gives us a fast CPS thing that is quick as hell to run because it gets tail-optimized and doesn’t need to build any intermediary data structures, and gets driven by its consumer. The trade-off is that CPS stuff is a damn mind-melter.

At Zurihac 2018, I met some guy (whose name I can’t remember, sorry!) who was working on a new language that supported this automatic transformation between data and codata. I don’t remember anything about it, except he would just casually convert between data and codata whenever was convenient, and the compiler would do the heavy lifting of making everything work out. It was cool. I wish I knew what I was talking about.