Compiling lenses

Posted on 2017-03-31 by Oleg Grenrus lens

To make my optics library for JavaScript – optika, faster, I need to avoid allocations. In Haskell we can rely on GHC to optimize code for us, fusing e.g. tuple creation in lens; in JavaScript we have to do it ourselves. We can prototype the solution in Haskell, as it's one of the best imperative languages.

This is the third post about lenses in this month, where previous are Affine Traversal and Why there is no AGetter?. And once again this is a literate Haskell file, so there are some imports:

{-# LANGUAGE RankNTypes #-}
module CompilingLenses where
import Control.Applicative (Const (..))
import Control.Arrow ((&&&))
import Control.Monad.ST
import Data.Foldable (for_)
import Data.Monoid (Sum (..))
import Data.Profunctor
import Data.Profunctor.Traversing
import Data.STRef
import Data.Tuple (swap)


optika is a profunctor optics library, so let's remind ourselves how profunctor optics work.

The profunctor optic is a profunctor transformer: it takes "smaller" p a b, and turns it into "bigger" p s t.

type Optic p s t a b = p a b -> p s t

Usually p a b is a function. For example a Forget profunctor used to defined view and foldOf operations is a newtype Forget r a b = Forget (a -> r). The ordinary arrow (->) is also a profunctor, and used to define over and set operations. Also Tagged a b is a profunctor (if you stare at it long enough), and is isomorphic to () -> b function. It's used to implement review.

It's not easy to build intuition on how this works. For example, Getter is essentially a function s -> a, but from above we get something like s ~> t. The idea is that the curly arrow – the profunctor, is not exactly the function s -> t, but may but something different. In Getter case, it's a ~> b = a -> r for some r (Forget r a b). As we start with a ~> b, we pick r to be a, i.e. a ~> b = a -> a. When the optic transforms a ~> b into s ~> t, it transforms "small" getter a -> a into "big" s -> a!

We'll define a non-function profunctor to implement sumOf. It will be a deep embedding of folds. Note, that we cannot perform arbitrary transformations while creating lenses or traversals. For example Lens is:

type Lens s t a b = forall p. Strong p => p a b -> p s t

so we can only dimap (from Profunctor) and first' (from Strong) the arbitrary profunctor. We can create a GADT where these operations are represented explicitly; it will sacrifice a bit of elegance (no more functions everywhere) for the sake of performance.

We'll define a small example data type and a value; to make following development more concrete. We'll show how sumOf is implemented using Forget r a b. After that we'll define a deep-embedding Fold-AST: Neglect, implement neglectSumOf using it; and discuss how it's useful for the JavaScript implementation. At the end, we'll inspect benchmarks of various implementations of sumOf in JavaScript: optika's old and new, as well as implementations from other optics libraries.


To make the development more concrete, Let's define two record types, where the second one is used in the first one.

data Foo = Foo { _fooX :: Bool, _fooYS :: [Bar] } deriving Show
data Bar = Bar { _barX :: Bool, _barZ  :: Int }   deriving Show

The example value we'll work with is:

foo :: Foo
foo = Foo True
    [ Bar True 3
    , Bar False 4
    , Bar False 5
    , Bar True 2

Next we'll need lenses to work with these types. First the lens helper, defined a bit differently than in purescript-profunctor-lenses

lens :: (s -> a) -> (s -> b -> t) -> Lens s t a b
lens getter setter = dimap (id &&& getter) (uncurry setter) . second'

and the actual lenses

fooYS :: Lens Foo Foo [Bar] [Bar]
fooYS = lens _fooYS (\s b -> s { _fooYS = b })

barZ :: Lens Bar Bar Int Int
barZ = lens _barZ (\s b -> s { _barZ = b })

If we want to calculate the sum of barZ ints inside Foo, we'll need to fold:

sumOf :: Optic (Forget (Sum a)) s s a a -> s -> a
sumOf l = getSum . runForget (l (Forget Sum))

And it works: 3 + 4 + 5 + 2 = 14:

λ> sumOf (fooYS . traverse' . barZ) foo


The first step in a solution to the allocation problem is to not transform functions (recall, newtype Forget r a b = Forget (a -> r)), but make a more concrete AST. For the lack of the imagination, we'll call it Neglect.

data Neglect r a b where
    End    ::                                   Neglect r r            x
    LMap   :: (c -> a) ->      Neglect r a b -> Neglect r c            x
    Second ::                  Neglect r a b -> Neglect r (c, a)       x
    Right' ::                  Neglect r a b -> Neglect r (Either c a) x 
    Wander :: Traversable f => Neglect r a b -> Neglect r (f a)        x

Note: We let the second argument change freely in the AST, this just highlights that b type parameter is a phantom.

We'll need a function to inspect the structure of Neglect, and change the last type parameter

debugNeglect :: Neglect r a b -> [String]
debugNeglect End        = ["End"]
debugNeglect (LMap _ x) = "LMap" : debugNeglect x
debugNeglect (Second x) = "Second" : debugNeglect x
debugNeglect (Right' x) = "Right'" : debugNeglect x
debugNeglect (Wander x) = "Wander" : debugNeglect x

retagNeglect :: Neglect r a b -> Neglect r a c
retagNeglect p = case p of 
    End      -> End
    LMap f x -> LMap f x
    Second x -> Second x
    Right' x -> Right' x
    Wander x -> Wander x

The Neglect is an instance of Traversing and all of the super-classes, at least with a lousy interpretation of the laws:

instance Profunctor (Neglect r) where
    lmap = LMap
    rmap _ = retagNeglect

instance Strong (Neglect r) where
    second' = Second

instance Choice (Neglect r) where
    right' = Right'

instance Traversing (Neglect r) where
    traverse' = Wander

The set-up is ready, and we can write imperative variant of sumOf. We allocate a mutable cell where we accumulate the result. The helper go,

neglectSumOf :: Num a => Optic (Neglect a) s s a a -> s -> a
neglectSumOf l s = runST $ do
    ref <- newSTRef 0
    go ref (l End) s
    readSTRef ref
    go :: Num r => STRef s r -> Neglect r a b -> a -> ST s ()
    go  ref End        x         = modifySTRef' ref (x +)
    go _ref (Right' _) (Left _)  = pure ()
    go  ref (Right' p) (Right x) = go ref p x
    go  ref (LMap f p) x         = go ref p (f x)
    go  ref (Second p) (_, x)    = go ref p x
    go  ref (Wander p) xs        = for_ xs $ go ref p

And this variant works too!

λ> neglectSumOf (fooYS . traverse' . barZ) foo

Next step is to manually fuse LMap and Second. As you can see they almost always come together, because of the way lens is defined.

λ> debugNeglect ((fooYS . traverse' . barZ) End)

As in optics library setting the classes are implementation details, let's add a method to Strong type-class:

class Profunctor p => StrongE p where
    firstE :: p a b  -> p (a, c) (b, c)
    firstE = dimap swap swap . secondE

    secondE :: p a b -> p (c, a) (c, b)
    secondE = dimap swap swap . firstE

    lensE :: (s -> a) -> (s -> b -> t) -> p a b -> p s t
    lensE getter setter = dimap (id &&& getter) (uncurry setter) . secondE

    {-# MINIMAL firstE | secondE #-}

Now we can redefine the optics to use the extended class

type LensE s t a b = forall p. StrongE p => p a b -> p s t

fooYSE :: LensE Foo Foo [Bar] [Bar]
fooYSE = lensE _fooYS (\s b -> s { _fooYS = b })

barZE :: LensE Bar Bar Int Int
barZE = lensE _barZ (\s b -> s { _barZ = b })

Forget is trivially instance of StrongE:

instance StrongE (Forget r) where
    firstE = first'
    secondE = second'

but with Neglect we can make a slight optimization:

instance StrongE (Neglect r) where
    firstE = first'
    secondE = second'
    lensE getter _ = LMap getter -- no tuple creation!

With those definition sumOf and neglectSumOf are still working:

λ> sumOf (fooYSE . traverse' . barZE) foo
λ> neglectSumOf (fooYSE . traverse' . barZE) foo

but in the latter case there are less commands to interpret

λ> debugNeglect ((fooYSE . traverse' . barZE) End)

In optika and JavaScript in general, the key (accessing field of an object) and idx optics are needed often, so we can add special cases for them to StrongE class. (idx is an affine traversal, where key can be thought as Lens' Record (Maybe Field).) There we'll end with a optic description like:

["fooYS", ["wander"], "barZ"]

where "wander" in array representing the Wander operation, and non-wrapped string a Key command. Note, we got a long way to represent an argument to functions like updateIn of immutable.js. If there are no wandering or prisms, we could use it directly to execute over! Otherwise, we can write interpreter which would something along

function (s) {
    let acc = 0;
    const s0 = s["fooYS"];      // "fooYS"
    for (const s1 of s0) {      // ["wander"]
        const s2 = s1["barZ"];  // "barZ"
        acc += s2;              // end
    return acc;

Unfortunately there are no quasiquoter in JavaScript, so we cannot eval such code, and plug-in the getter functions which aren't keys or indices. This is something where Lisps shine, as there we could. Yet, interpreting the commands directly seems to be fast enough anyway, especially with small data-sets, where different constant factors matter.


For completeness, let's also consider Prisms. So far the only prism I needed in JavaScript land is filtering.

type Prism s t a b = forall p. Choice p => p a b -> p s t

filtering :: (a -> Bool) -> Prism a a a a
filtering p = dimap getter (either id id) . right' 
    getter x | p x       = Right x
             | otherwise = Left x

This doesn't cause any problems for our current definition:

λ> sumOf (fooYSE . traverse' . filtering _barX . barZ) foo
λ> neglectSumOf (fooYSE . traverse' . filtering _barX . barZ) foo

Yet, we can notice a similar pattern of commands in Neglect: Right' is followed by LMap, because of the way filtering is defined.

*Main> debugNeglect ((fooYSE . traverse' . filtering _barX . barZ) End)

We can add filtering (and prism) methods to our variants of Choice, so the Neglect program would be smaller (but using more different commands). This reminds me of RISC vs CISC comparison!


There is no reason to benchmark Haskell version, as it exists only as a proof-of-concept version. The JavaScript version (WIP at the moment of writing) is easy to benchmark, thanks to the existing benchmark suite in partial.lenses.

   4,797,435/s     208.445ns     1.00x   L.sum(L.elems, xs100)
     866,526/s       1.154μs     5.54x   K.traversed().cheatSumOf(xs100)
     812,924/s       1.230μs     5.90x   K.traversed().compileSumOf(xs100)
     742,065/s       1.348μs     6.46x   K.traversed().sumOf(xs100)
     612,765/s       1.632μs     7.83x   L.concat(Sum, L.elems, xs100)
     582,574/s       1.717μs     8.23x   K.traversed().forgetSumOf(xs100)
     177,087/s       5.647μs    27.09x   xs100.reduce((a, b) => a + b, 0)
     163,278/s       6.125μs    29.38x   R.sum(xs100)
      23,082/s      43.324μs   207.84x   P.sumOf(P.traversed, xs100)
       3,597/s     278.027μs  1333.81x   O.Fold.sumOf(O.Traversal.traversed, xs100)

     181,058/s       5.523μs     1.00x   K.traversed().traversed().traversed().compileSumOf(xsss100)
     168,839/s       5.923μs     1.07x   L.sum([L.elems, L.elems, L.elems], xsss100)
     158,853/s       6.295μs     1.14x   L.concat(Sum, [L.elems, L.elems, L.elems], xsss100)
     133,148/s       7.510μs     1.36x   k_traversed3.sumOf(xsss100)
      97,361/s      10.271μs     1.86x   k_traversed3.forgetSumOf(xsss100)
      75,170/s      13.303μs     2.41x   xsss100.reduce((a0, xss) => a0 + xss.reduce((a1, xs) => a1 + xs.reduce((a2, x) => a2 + x, 0), 0), 0)
      74,948/s      13.343μs     2.42x   K.traversed().traversed().traversed().sumOf(xsss100)
      65,481/s      15.272μs     2.77x   K.traversed().traversed().traversed().forgetSumOf(xsss100)
      14,389/s      69.496μs    12.58x   k_traversed3.cheatSumOf(xsss100)
      10,196/s      98.080μs    17.76x   K.traversed().traversed().traversed().cheatSumOf(xsss100)
       4,159/s     240.416μs    43.53x   P.sumOf(R.compose(P.traversed, P.traversed, P.traversed), xsss100)
         682/s       1.466ms   265.48x   O.Fold.sumOf(R.compose(O.Traversal.traversed, O.Traversal.traversed, O.Traversal.traversed), xsss100)

And other constants are:

const xs100 = Array(100).fill(1);
const xs10 = Array(10).fill(1);
const xsss100 = Array(100).fill([[1]]);
const xsss100b = Array(100).fill([xs10]);
const xsss100c = Array(10).fill([xs100]);

const k_traversed3 = K.traversed().traversed().traversed();

I expected the new version (K...sumOf) to be much faster than older one (K...forgetSumOf). We are still slower than partial.lenses in the first benchmark (array of 100 numbers), and about the same in the second one (array of 100 of [[1]], i.e. 100-1-1 3-layer-array).

One reason is due more expensive compilation. partial.lenses doesn't have compilation step, but in optika each operation is in two-step: compilation and execution The first one is where GHC helps you in Haskell land, if you don't use dynamic lenses. The K...compileSumOf measures the time spend "compiling" the lens in optika.

Optic.prototype.compileSumOf = function (/* unused */) {
  var commands =, []).reverse();
  return commands.length; // incorrect!

I.e. we construct the commands array, but do not execute it.

Two more benchmarks are using xsss100b and xsss100c, 100-1-10 and 10-1-100 elements 3-layer-arrays:

      59,728/s      16.743μs     1.00x   k_traversed3.forgetSumOf(xsss100b)
      53,630/s      18.646μs     1.11x   k_traversed3.sumOf(xsss100b)
      51,232/s      19.519μs     1.17x   L.sum([L.elems, L.elems, L.elems], xsss100b)
      48,596/s      20.578μs     1.23x   K.traversed().traversed().traversed().forgetSumOf(xsss100b)
      47,026/s      21.265μs     1.27x   L.concat(Sum, [L.elems, L.elems, L.elems], xsss100b)
      41,254/s      24.240μs     1.45x   K.traversed().traversed().traversed().sumOf(xsss100b)
      12,820/s      78.004μs     4.66x   xsss100b.reduce((a0, xss) => a0 + xss.reduce((a1, xs) => a1 + xs.reduce((a2, x) => a2 + x, 0), 0), 0)
      12,530/s      79.810μs     4.77x   k_traversed3.cheatSumOf(xsss100b)
      11,303/s      88.472μs     5.28x   K.traversed().traversed().traversed().cheatSumOf(xsss100b)
       1,616/s     618.755μs    36.96x   P.sumOf(R.compose(P.traversed, P.traversed, P.traversed), xsss100b)
         242/s       4.129ms   246.60x   O.Fold.sumOf(R.compose(O.Traversal.traversed, O.Traversal.traversed, O.Traversal.traversed), xsss100b)

     103,170/s       9.693μs     1.00x   k_traversed3.forgetSumOf(xsss100c)
      87,922/s      11.374μs     1.17x   K.traversed().traversed().traversed().forgetSumOf(xsss100c)
      79,017/s      12.655μs     1.31x   k_traversed3.sumOf(xsss100c)
      67,469/s      14.822μs     1.53x   L.sum([L.elems, L.elems, L.elems], xsss100c)
      61,644/s      16.222μs     1.67x   L.concat(Sum, [L.elems, L.elems, L.elems], xsss100c)
      57,878/s      17.278μs     1.78x   K.traversed().traversed().traversed().sumOf(xsss100c)
      54,430/s      18.372μs     1.90x   k_traversed3.cheatSumOf(xsss100c)
      39,805/s      25.123μs     2.59x   K.traversed().traversed().traversed().cheatSumOf(xsss100c)
      15,588/s      64.151μs     6.62x   xsss100c.reduce((a0, xss) => a0 + xss.reduce((a1, xs) => a1 + xs.reduce((a2, x) => a2 + x, 0), 0), 0)
       2,429/s     411.670μs    42.47x   P.sumOf(R.compose(P.traversed, P.traversed, P.traversed), xsss100c)
         343/s       2.917ms   300.95x   O.Fold.sumOf(R.compose(O.Traversal.traversed, O.Traversal.traversed, O.Traversal.traversed), xsss100c)

With slightly bigger inputs, the difference between optika and partial.lenses diminishes. Looks like we go as fast as is reasonably possible with JavaScript.

One of performance design choices of partial-lenses is

Avoid optimizations that require large amounts of code
It's worth mentioning, that one could count current sumOf of optika as large amounts of code, the approach generalizes to every fold, hopefully without noticeable performance degradation.

The forth optika variant – K...cheatSumOf is cheating, as a name says, and compiles the optika lens to partial.lenses and runs it! This is the benefit of more concrete, Neglect based profunctor. Not all lenses can be converted, but some could. This let us compare the compilation overhead in one more way.

Another trick used in these benchmarks is k_traversed3, a lens created outside of the benchmark suite. As the lens object is now persistent, we can use it to cache compiled commands:

Optic.prototype.sumOf = function (v) {
  var commands;
  if (!this.commandsSumOf) {
    commands =, []).reverse();
    this.commandsSumOf = commands;
  } else {
    commands = this.commandsSumOf;

  // ...

This is questionable, even dirty, hack, but it doesn't cause memory-leaks. Each lens is now more memory heavy, but not significantly.


Mapping Haskell ideas to untyped and unoptimised language requires making not-so idiomatic choices; but Haskell can still help there. As optika is based on solid ideas of profunctor optics, adding new way to sumOf was relatively small and isolated patch. We have seen an example how to convert optics them to other library formats (one may try to convert them to and from purescript-profunctor-lenses!) These ideas are hopefully useful to someone who decides to make (yet another) optimized Clojure optics library, macros would let you do even cooler stuff.

This instance is not yet in any released version of profunctors, but it will be thanks to Phil Freeman.

haskell ignore instance Monoid m => Traversing (Forget m) where traverse' (Forget h) = Forget (foldMap h) wander f (Forget h) = Forget (getConst . f (Const . h))

You can run this file with

stack --resolver=nightly-2017-03-01 ghci --ghci-options='-pgmL markdown-unlit'
λ> :l compiling-lenses.lhs

fetch the source from

Site proudly generated by Hakyll