A Category for Correct-By-Construction Serializers and Deserializers

Frequently in computer programming we need to work with data in different representations, and we need to work with the data on both sides of said representation. For example, we might have some Haskell data types in memory, which we later serialize to disk. When the user restarts our application, we need to reload this data back into Haskell data types, to allow them to resume work.

Haskell provides us with machinery for doing this serialization via the binary library, which gives us the Binary type class with two methods:

class Binary t where
  put :: t -> Put
  get :: Get t

get deserializes a sequence of bytes into Haskell values, while put operates in the reverse direction - transforming Haskell values to a sequence of bytes.

Usually, we want the implementations of these methods to be mutual inverses - the computation in get should restore data serialized with put, and vice versa. Unfortunately, nothing in the type system nor the structure of these methods gives us this guarantee - it’s all down to the programmer. I don’t trust myself, so I set out to investigate a more reliable approach.

Ideally, we would like to build up serializers and deserializers from smaller pieces, such that each piece carries its own inverse. For example, we could pair up serialization for a String with its own inverse:

type Serializer a = (Get a, a -> Put)

string :: Serializer String
string = (get, put)

As long as String has a Binary instance where get and put correctly specified, we know that string is going to function as we expect in both directions.

We’re on to something here, but currently this only works for single Strings. What if I have a pair of Strings that I want to serialize? From what we’ve seen so far, there’s no way to combine our bidirectional serializers. Earlier I mentioned that we would like to work with small pieces and compose them - lets see if we can solve this problem for just serialization first.

Serialization consumes data. If we have data to serialize, the application of one serializer should consume some of this data, leaving us with slightly less data that we have to continue serializing. By repeated application of serializers, we will eventually have considered the entire structure and will have nothing left to do. This consumption of a structure bit-by-bit suggests that serialization will be a type changing operation, as a record with a field removed is certainly not the same type as its larger record. So let’s try and incorporate that:

data Serializing a b = Serializing (a -> PutM b)

pString2 :: Serializing (String, String) String
pString2 = Serializing $ \(a, b) -> do put a; return b

pString1 :: Serializing String ()
pString1 = Serializing $ \a -> put a

Composition of Serializing should now be clear - we just need to compose them one after another:

(.) :: Serializing b c -> Serializing a b -> Serializing a c
(Serializing g) . (Serializing f) = Serializing (f >=> g)

putTwoStrings :: Serializing (String, String) ()
putTwoStrings = pString1 . pString2

We’ve built a serializer that can serialize a tuple of two strings, and we did so piece-by-piece. While what we have so far is not entirely satisfactory, it seems like we’re heading in the right direction. We’ll come back to this later, but first let’s see if the same ideas translate to deserializers.

Our serializer consumed data, so deserialization is naturally the opposite of consuming data - that is, deserialization produces data. When we deserialize we’ll start with nothing, and we’ll deserialize a few bytes into part of our structure one step at a time. Each step of deserialization should take the smaller structure and expand it into a larger structure - eventually leading us to the desired structure. Again, this will be a type changing operation, and we can encode all of this just as we did with Serializing:

data Deserializing a b = Deserializing (a -> Get b)

getString1 :: Deserializing () String
getString1 = Deserializing $ \() -> get

getString2 :: Deserializing String (String, String)
getString2 = Deserializing $ \s -> do { s' <- get; return (s, s') }

(.) :: Deserializing b c -> Deserializing a b -> Deserializing a c
(Deserializing g) . (Deserializing f) = Deserializing (f >=> g)

getTwoStrings :: Deserializing () (String, String)
getTwoStrings = getString2 . getString1

As you can see, it’s pretty much exactly the same idea. The only difference is that now each of our deserializers return a slightly bigger structure, whereas our serializers would move our structure to something smaller.

Just to prove that what we have so far works, we can try this in GHCI:

> let bytes = case putTwoStrings of Serializing p -> runPut (p ("Hello", "World!"))
> case getTwoStrings of Deserializing g -> runGet (g ()) (LBS.pack bytes)

To carry on working towards our goal, we need to pair the Serializer up with its Deserializer. Unfortunately, what we have so far won’t work:

data Serializer a b = Serializer (a -> Get b) (a -> Put b)

Notice here how the types both move from a to b - that’s certainly not going to work, as the shape of the data is changing in opposite directions! In Get, a is “smaller” than b, whereas for Put a is “larger” then b. In order to work around this, we just need to swap the order of types in one of these functions - I’ve swapped the order for Put:

data Serializer a b = Serializer (a -> Get b) (b -> PutM a)

This makes sense - if put will shrink our structure, then get can move from this smaller structure back to the original structure. We can express our string1 and string2 serializers now:

string2 :: Serializer (String, String) String
string2 = Serializer (\(a, b) -> do put a; return b)
                     (\s -> do { s' <- get; return (s', s) })

string1 :: Serializer String ()
string1 = Serializer put (\() -> get)

We were able to compose things before, and we can certainly compose things here…

(.) :: Serializer b c -> Serializer a b -> Serializer a c
(Serializer g g') . (Serializer f f') = Serializer (f >=> g) (g' >=> f')

twoStrings :: Serializer (String, String) ()
twoStrings = string1 . string2

However, this has a rather significant problem - can you spot it? Take time to think about this and see if you can work out what’s going wrong.

Did you find it? If we fire up GHCI and have a play with our twoStrings serializer, lets see what we get…

> let bytes = case twoStrings of Serializer _ p -> runPut (p ("A", "B"))
> case twoStrings of Serializer g _ -> runGet (g ()) bytes

Oh no - that’s not what we wanted at all! The problem is that the order of effects are being reversed. When we put data, we put the first tuple element first, and then the second. However, we’re reading data in the opposite order - expecting the second element to be first in the stream, which is clearly not correct. For (String, String) the deserializer works but the tuple is in the wrong order - for other data types this would lead to a runtime exception.

With the current definition of Serializer, there’s simply no way around this - the types won’t let us run effects in different orders. The reason for this is that we can only access the underlying Get computation by having the smaller structure around first. However, we can be sneaky and changes things around just enough to let us run Get in a different order. Now the Get computation is no longer a function, but is a computation that returns a function:

data Serializer a b = Serializer (Get (a -> b)) (b -> PutM a)

With this change we do have access to any Get computation we like, and we are free to run them in a different order:

(.) :: Serializer b c -> Serializer a b -> Serializer a c
(Serializer g g') . (Serializer f f') =
  Serializer (do buildF <- g
                 buildG <- f
                 return (buildF . buildG))
             (g' >=> f')

Now it’s clear that both our Put and our Get computations are sequenced in the same order - nice! It turns out that our composition comes with a sane definition of identity too, which means our Serializer can be used with Category:

instance Category Serializer where
  (Serializer g g') . (Serializer f f') =
    Serializer (do buildF <- g
                   buildG <- f
                   return (buildF . buildG))
               (g' >=> f')

  id = Serializer (return id) return

Serializing Through Heterogeneous Lists

We have finally reached a nice core to our solution, but the surface API isn’t really working out. We had to write different Serializers for both (String, String) and String, which is certainly not desirable. Ultimately, we would like to be able to work with just one Serializer for String, and compose them however we please.

Unfortunately, working with tuples is causing us the real pain here. The reason for this is that tuples don’t really have any structure that would allow us to work with them in any sort of principled manner. Instead, what we can do is use a heterogeneous list, which we can recurse on just like an ordinary linked list. So, we introduce a type for heterogeneous lists:

data List :: [*] -> * where
  Nil :: List '[]
  Cons :: a -> List as -> List (a ': as)

And now we can use the new poly-kinded Category to upgrade Serializer to work with these lists:

data Serializer :: [*] -> [*] -> * where
  Serializer :: (Get (List a -> List b))
             -> (List b -> PutM (List a))
             -> Serializer a b

instance Category Serializer where
  (Serializer g g') . (Serializer f f') =
    Serializer (do mkB <- g
                   mkA <- f
                   return (mkB . mkA))
               (g' >=> f')

  id = Serializer (return id) return

This was quite a detour, and has this really helped us? Indeed it has, as we can now we can write a much more general Serializer String:

string :: Serializer as (String ': as)
string = Serializer (do a <- get; return (Cons a))
                    (\(Cons a as) -> do put a; return as)

The type of string now indicates that this Serializer can serialize anything that starts with a String, and likewise when deserializing it expects a String to be the first element. This composes exactly as we’d expect:

twoStrings :: Serializer as (String ': String ': as)
twoStrings = string . string

All we need to do is unwrap the List resulting from a Get or wrap up data in a List for Put and we’re good to go:

> let bytes = case twoStrings of
                Serializer _ p -> runPut (void $ p ("A" `Cons` ("B" `Cons` Nil)))

> case twoStrings of
    Serializer g _ -> runGet (($ Nil) <$> g) bytes

Cons "A" (Cons "B" Nil)

Destructuring Data Via Prisms

The API we’ve built works really well if we already have data decomposed into a List, but we don’t normally have this luxury. This means we need a way to convert from a data type to it’s constituent parts, and this is exactly the functionality that Prisms in the lens library provide us with. While Prisms can be a little hard to get your head around, it can be illuminating to experiment with them in GHCI:

> review _Cons (10, [])

> review _Cons (10, [20])

> review _Cons (1, [2..5])

> preview _Cons [10, 20, 30]
Just (10,[20,30])

> preview _Cons [10]
Just (10,[])

> preview _Cons []

Prisms have two main operations: review and preview. review lets us construct some data out of its parts - above we use _Cons with (10, [20]), which is the same as (10 : [20]) - resulting in the list [10, 20]. preview lets us go the other way, which is the same idea as pattern matching on a constructor. If we preview _Cons on non-empty lists, then the pattern matching succeeds and the list is separated into its head and tail. However, we can’t pattern match with _Cons on an empty list, so preview returns Nothing - which corresponds to a pattern match failure.

Armed with Prism, we’re almost entirely ready to go! The only problem is that Prism normally works with tuples, which we’ve already seen aren’t a great data for our needs. It’s entirely mechanical to convert between tuples and List, so we simply move between them with a type class. Combining this all together, we have the following:

class ListIso a b | a -> b, b -> a where
  _HList :: Iso' b (List a)

usePrism :: ListIso a b  => Prism' d b -> Serializer a '[d]
usePrism p = Serializer get put
  put (Cons d Nil) = do
    Just tuple <- return (preview p d)
    return (tuple ^. _HList)

  get =
    return $ \hlist -> Cons (review p (hlist ^. from _HList)) Nil

Now we are free to use this on our data types, just as we’d expect:

instance ListIso '[a, b] (a, b) where
  _HList = iso (\(a, b) -> Cons a (Cons b Nil)) (\(Cons a (Cons b Nil)) -> (a, b))

data PairOfStrings = PairOfStrings String String
makePrisms ''PairOfStrings

pairOfStrings :: Serializer '[] '[PairOfStrings]
pairOfStrings = usePrism _PairOfStrings . string . string


If you look closely at our definition of usePrism you might have seen something suspicious. Here’s the relevant code:

usePrism = ...
  put (Cons d Nil) = do
    Just tuple <- return (Lens.preview p d)
    return (tuple ^. _HList)

In our put definition, we are assuming that Lens.preview is always returning a Just value. However, we saw earlier that this isn’t necessarily the case - the Prism corresponds to one of potentially many constructors. If we try and use usePrism with a prism that doesn’t match our expectations, then things go horribly wrong:

data Strings = PairOfStrings String String | ThreeStrings String String String
makePrims ''Strings
> case pairOfStrings of
    Serializer _ p -> runPut (void $ p (ThreeStrings "Uh" "Oh" "!" `Cons` Nil))

"*** Exception: Pattern match failure in do expression at ...

What we need to do is to allow for choice - if we have multiple possible prisms, then we need to consider each one. This corresponds to exhaustive pattern matching in case analysis.

It turns out choice is relatively straight forward to add in. Get is already an instance of MonadPlus, so we get choice there for free. Put however is a little more involved, as it doesn’t have an instance of MonadPlus. The best solution I’ve found thus far is to wrap up our Put computation inside Maybe, but this isn’t entirely satisfactory. Unfortunately binary doesn’t quite export enough to have a less expensive solution (PairS doesn’t have its oconstructor exported).

Monoid is a sensible type class to use for alternatives - choice is associative, and there is a sane identity (an always-failing Serializer). Thus the final definition of Serializer and its associated type classes are:

data Serializer :: [*] -> [*] -> * where
  Serializer :: (Get (List a -> List b)) -> (List b -> Maybe (PutM (List a))) -> Serializer a b

instance Category Serializer where
  (Serializer g g') . (Serializer f f') =
    Serializer (g >>= \b -> f >>= \a -> return (b . a))
               (\a -> do putF <- g' a
                         let (b, lbs) = runPutM putF
                         putG <- f' b
                         return (putLazyByteString lbs >> putG))

  id = Serializer (return id) (return . return)

instance Monoid (Serializer a b) where
  mempty = Serializer mzero (const mzero)
  (Serializer g p) `mappend` (Serializer g' p') =
    Serializer (g `mplus` g') (\i -> p i `mplus` p' i)

Armed with this final definition of Serializer, we’re almost ready to provide a complete definition of serializing our Strings type. We need to provide a little extra information however, which allows us to disambiguate constructors. This is because if we are deserializing, if I read two strings I don’t necessarily know constructor to choose (yes, if we considered EOF this could be done, I’m going for brevity). You can find the definition of disambiguate in the full code listing.

Thus the final user-facing code is just:

strings :: Serializer '[] '[Strings]
strings = mconcat
  [ usePrism _PairOfStrings . disambiguate 1 . string . string
  , usePrism _ThreeStrings . disambiguate 2 . string . string . string

And just to prove it all works…

> let Just putter = case strings of
        Serializer _ p -> p (ThreeStrings "A" "B" "C" `Cons` Nil)
      bytes = runPut (void putter)

> case strings of Serializer g _ -> runGet (($ Nil) <$> g) bytes
Cons (ThreeStrings "A" "B" "C") Nil

> let Just putter = case strings of
        Serializer _ p -> p (PairOfStrings  "Hello" "World!" `Cons` Nil)
      bytes = runPut (void putter)

> case strings of Serializer g _ -> runGet (($ Nil) <$> g) bytes
Cons (PairOfStrings "Hello" "World!") Nil

Final Thoughts

We’ve seen that it’s possible to build correct-by-construction serializers and deserializers, and we got there by breaking down our problem into small parts and finding a good way to combine the parts together. Hopefully, I’ve illustrated some of the problems that arise from a naive solution, and how these problems guided us towards an implementation that is both more correct and more flexible.

Serializer is still not perfect however. With the idea of choice above, there’s no way to indicate exhaustive pattern matching. For example, in strings we are considering both constructors, yet put strings returns Maybe Put. This isn’t particularly satisfactory, because it should now always be possible to serialize this data type! On a similar note, it becomes harder to get compile time checks about exhaustive pattern matching, because we’re no longer doing case analysis explicitly. This is an interesting problem to me, and one that I would still like to solve.

There is also a bit more work that we might want to consider doing with Get and Put, which is to use a different concept of choice. There are other options than using Maybe - for example we could use lists which would inform us of all possible serializations for a data type, which might provide better debugging information than simply using the first one that matches.

I’d like to conclude by mentioning that the ideas here aren’t particularly new. In 2010 Rendel and Ostermann presented a solution using a category of partial isomorphisms and product functors from this category to Hask, which lead to various libraries on Hackage such as invertible-syntax, and boomerang. At ZuriHack, Martijn van Steenbergen presented the latest version of JsonGrammar, which uses a free category to describe operations on a JSON AST, and also illustrated how one can use prisms to provide a modern vocabulary for partial isomorphisms. json-grammar uses a stack-prism data type, which achieves the same goal as using heterogeneous lists, but does require another Template Haskell call (makeStackPrisms).

While I’m happy with the solution so far, I haven’t finished playing around with this. It’s unclear to me how this plays with recursive data types (for example, does this work for lists? What about trees?), and I need to learn more about stack-prism to see if using heterogenous lists impedes composition (as Sjoerd Visscher has warned me!). Hopefully I’ll be able to start using what I have so far in production, iron out the last problems, and release this to Hackage in the near future.

Thanks for reading, a full code listing can be found on Github

You can contact me via email at ollie@ocharles.org.uk or tweet to me @acid2. I share almost all of my work at GitHub. This post is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.