Haskell generics explained

Published on November 22, 2019, last updated October 31, 2021

This is a new, revised version of the old tutorial I wrote.

• The shape of a data type
• The Generic and Generic1 type classes
• Metadata wrappers
• Example: deriving Functor
• Example: counting constructor fields
• Packing it in the type classes
• Conclusion

This tutorial serves as an introduction to generics in GHC. Generics is a way to reduce boilerplate and associated with it errors. More precisely, it is a way to use the same code with different data types. In this regard it is very close to polymorphism, which in Haskell comes in two flavors:

• Parametric polymorphism, when we have type variables in functions/data types. This allows the same function to work with different types of arguments, as long as the more general types from a function’s signature can be unified with the concrete types we want to work with.

• Ad-hoc polymorphism, which allows us to perform a computation abstracted over instances of one or more type classes. We request that a type have some properties of interest and then describe the computation in terms of these properties. The code is then applicable to any data type that has these properties.

How are generics different? Generics allow us to define functions that work in terms of general combinators that describe the shape of a data type and some metadata. This way we can declare how to perform a computation on almost any data type.

Haskell features that make generics possible are type classes and ad-hoc polymorphism. The ability to describe a data type in terms of a set of combinators is our property, captured by the Generic type class. Values of a type that has an instance of this type class can be passed to functions that are defined in terms of the generic representation, not the data type itself. These functions are by definition polymorphic and usually hidden behind a type class interface. The code usually takes the form of automatic derivation of a type class instance:

1. A given data type gets Generic instance automatically, as it can be generated by the compiler with the help of the DeriveGeneric language extension.

2. For a type class T of interest, there is a type class instance T Foo which implements the methods of T by inspecting generic representation of Foo. The representation comes from the Generic Foo instance. As soon as the condition from the step 1 is satisfied we get the T Foo instance for free.

If you know about the Data and Typeable type classes, then you probably know that it’s possible to do something similar using the information that the methods of those type classes provide. Data and Typeable are beyond the scope of this tutorial, but you can read about them in this blog post by Chris Done if you’re interested.

The shape of a data type

What could it look like? Well, if I showed you the data types as they are, you would probably run away, cursing the tutorial and Haskell. I have a better idea. Let’s start with the simplest thing possible and then iterate asking ourselves how to tackle some more interesting use cases that we might need to support. This will force us to make the data types less obvious, but also more powerful. We will do it step by step until we arrive at the definitions that are actually used.

For better or worse, algebraic data types lock us into a view of the world that is made up of sums and products. So, we need to be able to represent the following:

• Data types without constructors at all: uninhabited types like Void. This can be described as data V1, which has no constructors.

• Constructors without arguments, i.e. data U1 = U1.

• Sums: data (f :+: g) = L1 f | R1 g. If we have a sum data type with two alternatives we can represent other sum data types with any number of alternatives via nesting.

• Products: data (f :*: g) = f :*: g.

Let’s try to use this representation to derive a Functor instance. Deriving such an instance means that we should provide the fmap function which looks like this:

fmap :: (a -> b) -> Rep f -> Rep f

Here, Rep f maps to the type of generic representation of f :: * -> *. There is a problem though. You see, the functor’s inner type that changes from a to b in this example is not found in Rep f! This approach will work only for the types with kind * and type classes such as Show. If we want to use this system to derive a Functor instance, we need to allow it to work with the kind * -> *.

The solution is to add one more type parameter p to all our combinators:

data V1 p
data U1 p = U1
data (f :+: g) p = L1 (f p) | R1 (g p)
data (f :*: g) p = (f p) :*: (g p)

This way fmap would be:

fmap :: (a -> b) -> Rep f a -> Rep f b
--                  p  =  a    p  =  b

But what happens to the type classes that work with * kinds? Our choices are:

• Have a separate set of combinator types for each case (* and * -> * kinds).

• Use the most general form (with p), but for * kinds just treat the extra p parameter as a dummy type index that has no meaning.

The authors of the generics extension went with the second option, and I can’t blame them. We will see that there are a lot of wrappers already and we really should try to keep their number from exploding.

Let’s try to map from a data type to its representation and see if we’re still missing something:

data Maybe a = Nothing | Just a

-- Interestingly, we could build a representation that works on ‘Maybe a’,
-- that is, a thing of kind *, if we wanted to derive something like ‘Show’.
-- At the same time if we wanted to derive ‘Functor’, we would work with
-- ‘Maybe’ of kind * -> *. This means that there are actually two different
-- possible representations depending on our aim. This is addressed with two
-- different generics type classes, as we will see later.

-- For kind *, things like ‘Show’:

-- type: (U1 :+: ?) p

How to represent Just a? We need to way to allow it to have an argument. Let’s add the following:

data Rec0 c p = Rec0 { unRec0 :: c }

Rec part in the type’s name hints that it may be possibly recursive.

But in fact, due to a historical accident, it’s defined a bit differently:

type Rec0 = K1 R

newtype K1 i c p = K1 { unK1 :: c } -- c is the value, ‘a’ in ‘Maybe a’
--         ^   ^
--         |   |
--         |   +-------- dummy p
--         |
-- type-level tag, R or P

You see the type-level tag R? There used to be another one, P and the type synonym type Par0 = K1 P which is now deprecated. Bottom line: Rec0 is used for data constuctor arguments (fields), that are not p parameter.

With Rec0, we can finally build the representation of Maybe a:

-- This is the type of our representation: (U1 :+: Rec0 a) p

-- Examples of values for ‘Maybe Int’:

-- Nothing => L1 U1
-- Just 5  => R1 (K1 5) -- remember where K1 comes from?
--            ^
--            |
--            +--- L1 and R1 are from our representation of sum types

Let’s derive a different representation that works with * -> * kinds:

-- Type of our representation: (U1 :+: ?) p

We need a way to tell if we have an argument of p type (like a in Functor f => f a) or some other type if we’re to write a generic fmap function. For this the generics extension uses Par1 p:

newtype Par1 p = Par1 { unPar1 :: p } -- “par” stands for “parameter”

Par1 is used to mark occurrences of p. Our representation thus becomes:

-- The type of our representation: (U1 :+: Par1) p

-- Examples of values for ‘Maybe Int’:

-- Nothing => L1 U1 -- the same
-- Just 5  => R1 (Par1 5)

The final example is for lists. Given the standard definition of linked list:

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

How do we build its generic representation for the kind * -> *? The tricky part is, of course, List a, which is a recursive occurrence of entire functorish part with the parameter inside it. If we mark occurrences of the parameter by Par1, then why not mark this data constructor too? For that we have Rec1.

-- The type of representation: (U1 :+: (Par1 :*: Rec1 List)) p

-- Examples of values for ‘List Int’:

-- Nil                 => L1 U1
-- Cons 5 Nil          => R1 (Par1 5 :*: Rec1 Nil)
-- Cons 5 (Cons 4 Nil) => R1 (Par1 5 :*: Rec1 (Cons 4 Nil))

If we had just arguments of a data constructor that are not related to parameter p, plain Rec0 (K1) would be used for both first and second arguments of Cons.

The Generic and Generic1 type classes

Type classes that map types to their representations are called Generic (for type classes that work with * kinds) and Generic1 (for type classes that work with * -> * kinds). They live in the module called GHC.Generics together with the types used to build the data type representations that we have just discussed.

Let’s see what these type classes have:

class Generic a where
  type Rep a :: * -> *
  from :: a -> Rep a p
  to   :: Rep a p -> x

class Generic1 f where
  type Rep1 f :: * -> *
  from1 :: f p -> Rep1 f p
  to1   :: Rep1 f p -> f p

from and from1 map values of data types to their generic representations. Rep and Rep1 are associated type functions (the feature is enabled by the TypeFamilies GHC extension) that take the type of data we want to manipulate and return the type of its representation. Of course, if we want to derive Functor instances, we need a way to go back from representation to actual value of target data type. This is done via to and to1. The good thing about this, of course, that GHC can derive Generic and Generic1 for us automatically when the DeriveGeneric language extension is enabled.

Now let’s open GHCi and try to infer Rep of some type:

λ> :t (undefined :: Rep (List a) p)
(undefined :: Rep (List a) p)
  :: D1
       ('MetaData "List" "GenericsTutorial" "main" 'False)
       (C1 ('MetaCons "Nil" 'PrefixI 'False) U1
        :+: C1
              ('MetaCons "Cons" 'PrefixI 'False)
              (S1
                 ('MetaSel
                    'Nothing 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy)
                 (Rec0 a)
               :*: S1
                     ('MetaSel
                        'Nothing 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy)
                     (Rec0 (List a))))
       p

OK, there is just a little bit more to this…

Metadata wrappers

A representation also has associated metadata. It may look messy and difficult to read, but I’ll explain the logic behind it in a moment. First of all, metadata should not get in our way if we don’t care about it. Thus all metadata is attached using the same simple wrapper:

newtype M1 i c f p = M1 { unM1 :: f p } -- ‘f p’ is what lives inside, U1 for example
--         ^ ^
--         | |
--         | +---- compiler-generated data type that allows us to get meta information
--         |
--  type-level tag, see below

The i type-level tag can be one of the three:

• D for data type metadata type D1 = M1 D
• C for constructor metadata type C1 = M1 C
• S for record selector metadata type S1 = M1 S

It should be understandable why metadata is attached this way. If we want to, we can ignore it:

f (M1 x) = f x

If we want to look at a particular type of metadata we can specify the i type-level tag, or we can leave it unspecified to deal with all metadata at once.

The c type is auto-generated by the compiler and encodes metadata on the type level. Why not store the metadata on the value level, in the M1 constructor? Well, if it were there, we would have to provide it when we wanted to generate some values, and providing metadata for already existing data type is certainly something that only the compiler can do properly. With the current approach, a given data type determines the type of its representation, including its metadata, so we don’t have to bother.

Let’s see what metadata wrappers are generated:

• The entire representation is wrapped in D1, which provides datatype-level information: datatype name, module name, and whether it’s a newtype.

• Every constructor is wrapped in C1, which provides information about the constructor such as its constructor name, fixity, and whether it’s a record.

• Every argument of a constructor is wrapped with S1 (even if it’s not actually a record selector), which tells us the selector name.

Look at the Haddock to find out names of functions that help extract the metadata. Using them is straightforward: feed the wrapped data to functions like datatypeName and get the information.

Example: deriving Functor

After talking so much about Functor instances and adding the clumsy p parameter to support them, we absolutely must derive a Functor instance now. In fact, Functor instance for generics is already defined in GHC.Generics, so instead of re-implementing it let’s just go through the code:

-- If we have a parameter ‘p’, we just map over it, as expected:

instance Functor Par1 where
  fmap f (Par1 p) = Par1 (f p)

-- The same with ‘Rec1’ (just use ‘fmap’ because the inner part is a ‘Functor’):

instance Functor f => Functor (Rec1 f) where
  fmap f (Rec1 a) = Rec 1 (fmap f a)

-- A constructor without fields only can be returned untouched:

instance Functor U1 where
  fmap _ U1 = U1

-- A field that is not ‘p’ parameter should not change:

instance Functor (K1 i c) where
  fmap _ (K1 a) = a

-- Metadata has no effect, just unwrap it and continue with the inner value,
-- if the inner value is an instance of ‘Functor’:

instance Functor f => Functor (M1 i c f) where
  fmap f (M1 a) = M1 (fmap f a)

-- When we have a sum, we should try to map what we get, provided that it
-- contains something that has ‘Functor’ instance:

instance (Functor l, Functor r) => Functor (l :+: r) where
  fmap f (L1 a) = L1 (fmap f a)
  fmap f (R1 a) = R1 (fmap f a)

-- The same for products:

instance (Functor a, Functor b) => Functor (a :*: b) where
  fmap f (a :*: b) = fmap f a :*: fmap f b

Example: counting constructor fields

We are ready to implement a simple and pretty useless type class that will count constructor fields of a given value.

The type class looks like this:

class CountFields a where
  -- | Return number of constuctor fields for a value.
  countFields :: a -> Natural

We will start by implementing countFields method that works on representations:

instance CountFields (V1 p) where
  countFields _ = 0

instance CountFields (U1 p) where
  countFields _ = 0

instance CountFields (K1 i c p) where
  countFields _ = 1

instance CountFields (f p) => CountFields (M1 i c f p) where
  countFields (M1 x) = countFields x

instance (CountFields (a p), CountFields (b p)) => CountFields ((a :+: b) p) where
  countFields (L1 x) = countFields x
  countFields (R1 x) = countFields x

instance (CountFields (a p), CountFields (b p)) => CountFields ((a :*: b) p) where
  countFields (a :*: b) = countFields a + countFields b

Let’s write a single function called, say, defaultCountFields that does the counting for any instance of Generic:

defaultCountFields :: (Generic a, CountFields (Rep a)) => a -> Natural
defaultCountFields = countFields . from

But here is a catch—the code above does not compile. CountFields has the kind CountFields :: * -> Constraint, but we give it Rep a of the kind * -> *.

The typical solution is to have a helper class that works with things of * -> * kind (this also removes the p parameters from signatures):

class CountFields1 f where
  countFields1 :: f p -> Natural

defaultCountFields :: (Generic a, CountFields1 (Rep a)) => a -> Natural
defaultCountFields = countFields1 . from

instance CountFields1 V1 where
  countFields1 _ = 0

instance CountFields1 U1 where
  countFields1 _ = 0

instance CountFields1 (K1 i c) where
  countFields1 _ = 1

instance CountFields1 f => CountFields1 (M1 i c f) where
  countFields1 (M1 x) = countFields1 x

instance (CountFields1 a, CountFields1 b) => CountFields1 (a :+: b) where
  countFields1 (L1 x) = countFields1 x
  countFields1 (R1 x) = countFields1 x

instance (CountFields1 a, CountFields1 b) => CountFields1 (a :*: b) where
  countFields1 (a :*: b) = countFields1 a + countFields1 b

You might have noticed that some data types like Par0, Rec1 did not get their definitions. This is OK because we work with Generic, not Generic1 here. As GHC.Generics docs say:

• If no :+: instance is given, the function may still work for empty datatypes or datatypes that have a single constructor, but will fail on datatypes with more than one constructor.

• If no :*: instance is given, the function may still work for datatypes where each constructor has just zero or one field, in particular for enumeration types.

• If no K1 instance is given, the function may still work for enumeration types, where no constructor has any fields.

• If no V1 instance is given, the function may still work for any datatype that is not empty.

• If no U1 instance is given, the function may still work for any datatype where each constructor has at least one field.

An M1 instance is always required, but it can just ignore the meta-information.

Packing it in the type classes

Having dealt with the generic implementation of the functionality of interest, let’s put it all together and use a special GHC extension to allow the user to derive type classes without knowing anything about generics.

For a generic implementation to work without user’s definition we need to provide it as the default definition. As you have already seen, a generic implementation often involves a Generic constraint. It would be ugly and overly restrictive to add it as a superclass to every type class just to make deriving easier. The default keyword, enabled by the DefaultSignatures language extension, allows us to give a different type signature for a default implementation of a method:

class Functor f where
  fmap :: (a -> b) -> f a -> f b
  default fmap :: (Generic1 f, Functor (Rep1 f)) => (a -> b) -> f a -> f b
  fmap = to1 . fmap . from1

class CountFields a where
  countFields :: a -> Natural
  default countFields :: (Generic a, CountFields1 (Rep a)) => a -> Natural
  countFields = defaultCountFields

This way we can have our cake and eat it too: deriving is easy and no ugly details are visible!

Conclusion

Generics is a powerful means of automating writing of error-prone and boring definitions. The feature is helpful beyond deriving type class instances, as with a bit of creativity it allows us to reason about data types generically and generate values in a type-safe way. Finally, there are quite a few very interesting packages that complement or build on top of GHC generics. Once you feel comfortable with vanilla generics, libraries like generic-sop may be of interest.