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Csongor Kiss

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The generic-lens library provides utilities for deriving various optics for your datatypes, using GHC.Generics. In this post I’ll go over some of the features and provide examples of using them.


Lenses have proven to be an exteremely powerful tool in the Haskell ecosystem. generic-lens uses GHC.Generics to derive lenses and prisms on the fly, only when they are needed. These optics are highly polymorphic, and can be used with all types that are of the right shape. Extra care has been taken to keep type errors readable.


To get started, we will need the following extensions:

{-# LANGUAGE DataKinds        #-}
{-# LANGUAGE DeriveGeneric    #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE TypeApplications #-}
{-# LANGUAGE TypeFamilies     #-}

And the following imports

import Control.Lens
import Data.Generics.Product
import GHC.Generics

Consider the following datatype:

data Human a
  = Human
    { name    :: String
    , age     :: Int
    , address :: String
    , other   :: a
    } deriving (Generic, Show)


We can access the name field:

>>> Human "John" 18 "London" True ^. field @"name"

We can update fields too, even changing types where possible (when the type of the field is a type parameter of the datatype):

>>> Human "John" 18 "London" True & field @"other" %~ show
Human {name = "John", age = 18, address = "London", other = "True"}

In case of sum types, it only makes sense to have a lens on the fields that appear in every constructor. Trying to use field to get a lens for a partial field is a type error.

Note that the field lens works with DuplicateRecordFields, which means that record fields can actually be shared, and we can get a reusuble lens for all cases without code duplication.


We can directly reference a field by its type, as long as the type is unique in the structure.

>>> Human "John" 18 "London" True ^. typed @Bool
>>> Human "John" 18 "London" True ^. typed @String

<interactive>:34:34: error:
    • The type Human Bool contains multiple values of type [Char].
      The choice of value is thus ambiguous. The offending constructors are:
      • Human

    • In the second argument of ‘(^.)’, namely ‘typed @String’
      In the expression: Human "John" 18 "London" True ^. typed @String
      In an equation for ‘it’:
          it = Human "John" 18 "London" True ^. typed @String


When the above two fail, and we have a product type, we can specify the field of interest by its position.

data MyTuple a b = MyTuple a b deriving (Generic, Show)
>>> MyTuple 10 20 & position @1 .~ "hello"
MyTuple "hello" 20

super (row polymorphism)

Given two records, where the set of fields of one is the subset of that of the other, we can talk about a structural subtype relationship. The super lens allows us to treat the subtype as the supertype - without forgetting the original structure.

data Small
  = Small
    { small :: Int
    } deriving (Generic, Show)

data Large
  = Large
    { small :: Int
    , large :: String
    } deriving (Generic, Show)

smallFun :: Small -> Small
smallFun (Small n) = Small (n + 10)

(Here, we need the {-# LANGUAGE DuplicateRecordFields #-} extension in addition to the previous ones.)

>>> Large 10 "foo" & super %~ smallFun
Large {small = 20, large = "foo"}

Or we can simply upcast:

>>> Large 10 "foo" ^. super :: Small
Small {small = 10}
>>> Small 10 ^. super :: Large

<interactive>:53:13: error:
    • The type 'Small' is not a subtype of 'Large'.
      The following fields are missing from 'Small':
      • large


We can also obtain prisms that focus on individual constructors:

>>> Human "John" 18 "London" True ^? _Ctor @"Human"
Just ("John",18,"London",True)
>>> Human "John" 18 "London" True ^? _Ctor @"Human" . position @3
Just "London"


So far, we haven’t provided any type signatures. Indeed, everything can be inferred by the compiler. However, because these combinators are highly polymorphic, it might be interesting to use them in a polymorphic context.

f :: (MonadReader env m, HasField' "username" env String) => m String
f = view (field @"username")

This function is now polymorphic not just in the monad stack it will eventually run in, but also in the type of the environment.

The type of field is

field :: HasField field s t a b => Lens s t a b

HasField' (similarly to Lens') is a type synonym for HasField field s s a a.

For a more comprehensive overview and more examples, please have a look at the library on hackage, or on github.


An important question when evaluating such high-level abstractions is whether the abstraction comes at the cost of performance. Fortunately, GHC optimises away all of the overhead of the generic transformations, leaving us with code that is equivalent to what we would’ve written manually.

This can be verified by comparing the generated core of both the manually written lens and the generated one. However, it happened multiple times during development that a small change (such as eta-reduction) broke the optimisation. Joachim Breitner’s excellent inspection-testing tool, which is now integrated into the automated test suite, is making sure that the optimisation happens by automatically doing this comparison. This tool has been invaluable in ensuring the performance guarantees, without having to manually inspect the generated core after every single commit. The tests can be found here.

It’s important to mention that as of this release, only the lenses are optimised away completely, the prisms still have some leftover overhead. This is planned to be fixed in a future release.

Quick note (migration)

In case you were already using the library, there are some breaking changes in Namely, all the Has* classes have been extended from 3 type parameters to 5. Auxiliary constraint synonyms are provided, and migration should be relatively simple:

f :: HasField field a record => ...


f :: HasField' field record a => ...

Notice the ' at the end of the class name, and the swapping of the last two arguments.


Thanks to Matthew Pickering for useful comments on a draft of this post.