This is part 24 of Categories for Programmers. Previously: Comonads. See the Table of Contents.

We’ve seen several formulations of a monoid: as a set, as a single-object category, as an object in a monoidal category. How much more juice can we squeeze out of this simple concept?

Let’s try. Take this definition of a monoid as a set m with a pair of functions:

μ :: m × m -> m
η :: 1 -> m

Here, 1 is the terminal object in Set — the singleton set. The first function defines multiplication (it takes a pair of elements and returns their product), the second selects the unit element from m. Not every choice of two functions with these signatures results in a monoid. For that we need to impose additional conditions: associativity and unit laws. But let’s forget about that for a moment and just consider “potential monoids.” A pair of functions is an element of a cartesian product of two sets of functions. We know that these sets may be represented as exponential objects:

μ ∈ m m×m
η ∈ m1

The cartesian product of these two sets is:

m m×m × m1

Using some high-school algebra (which works in every cartesian closed category), we can rewrite it as:

m m×m + 1

The plus sign stands for the coproduct in Set. We have just replaced a pair of functions with a single function — an element of the set:

m × m + 1 -> m

Any element of this set of functions is a potential monoid.

The beauty of this formulation is that it leads to interesting generalizations. For instance, how would we describe a group using this language? A group is a monoid with one additional function that assigns the inverse to every element. The latter is a function of the type m->m. As an example, integers form a group with addition as a binary operation, zero as the unit, and negation as the inverse. To define a group we would start with a triple of functions:

m × m -> m
m -> m
1 -> m

As before, we can combine all these triples into one set of functions:

m × m + m + 1 -> m

We started with one binary operator (addition), one unary operator (negation), and one nullary operator (identity — here zero). We combined them into one function. All functions with this signature define potential groups.

We can go on like this. For instance, to define a ring, we would add one more binary operator and one nullary operator, and so on. Each time we end up with a function type whose left-hand side is a sum of powers (possibly including the zeroth power — the terminal object), and the right-hand side being the set itself.

Now we can go crazy with generalizations. First of all, we can replace sets with objects and functions with morphisms. We can define n-ary operators as morphisms from n-ary products. It means that we need a category that supports finite products. For nullary operators we require the existence of the terminal object. So we need a cartesian category. In order to combine these operators we need exponentials, so that’s a cartesian closed category. Finally, we need coproducts to complete our algebraic shenanigans.

Alternatively, we can just forget about the way we derived our formulas and concentrate on the final product. The sum of products on the left hand side of our morphism defines an endofunctor. What if we pick an arbitrary endofunctor F instead? In that case we don’t have to impose any constraints on our category. What we obtain is called an F-algebra.

An F-algebra is a triple consisting of an endofunctor F, an object a, and a morphism

F a -> a

The object is often called the carrier, an underlying object or, in the context of programming, the carrier type. The morphism is often called the evaluation function or the structure map. Think of the functor F as forming expressions and the morphism as evaluating them.

Here’s the Haskell definition of an F-algebra:

type Algebra f a = f a -> a

It identifies the algebra with its evaluation function.

In the monoid example, the functor in question is:

data MonF a = MEmpty | MAppend a a

This is Haskell for 1 + a × a (remember algebraic data structures).

A ring would be defined using the following functor:

data RingF a = RZero
             | ROne
             | RAdd a a 
             | RMul a a
             | RNeg a

which is Haskell for 1 + 1 + a × a + a × a + a.

An example of a ring is the set of integers. We can choose Integer as the carrier type and define the evaluation function as:

evalZ :: Algebra RingF Integer
evalZ RZero      = 0
evalZ ROne       = 1
evalZ (RAdd m n) = m + n
evalZ (RMul m n) = m * n
evalZ (RNeg n)   = -n

There are more F-algebras based on the same functor RingF. For instance, polynomials form a ring and so do square matrices.

As you can see, the role of the functor is to generate expressions that can be evaluated using the evaluator of the algebra. So far we’ve only seen very simple expressions. We are often interested in more elaborate expressions that can be defined using recursion.


One way to generate arbitrary expression trees is to replace the variable a inside the functor definition with recursion. For instance, an arbitrary expression in a ring is generated by this tree-like data structure:

data Expr = RZero
          | ROne
          | RAdd Expr Expr 
          | RMul Expr Expr
          | RNeg Expr

We can replace the original ring evaluator with its recursive version:

evalZ :: Expr -> Integer
evalZ RZero        = 0
evalZ ROne         = 1
evalZ (RAdd e1 e2) = evalZ e1 + evalZ e2
evalZ (RMul e1 e2) = evalZ e1 * evalZ e2
evalZ (RNeg e)     = -(evalZ e)

This is still not very practical, since we are forced to represent all integers as sums of ones, but it will do in a pinch.

But how can we describe expression trees using the language of F-algebras? We have to somehow formalize the process of replacing the free type variable in the definition of our functor, recursively, with the result of the replacement. Imagine doing this in steps. First, define a depth-one tree as:

type RingF1 a = RingF (RingF a)

We are filling the holes in the definition of RingF with depth-zero trees generated by RingF a. Depth-2 trees are similarly obtained as:

type RingF2 a = RingF (RingF (RingF a))

which we can also write as:

type RingF2 a = RingF (RingF1 a)

Continuing this process, we can write a symbolic equation:

type RingFn+1 a = RingF (RingFn a)

Conceptually, after repeating this process infinitely many times, we end up with our Expr. Notice that Expr does not depend on a. The starting point of our journey doesn’t matter, we always end up in the same place. This is not always true for an arbitrary endofunctor in an arbitrary category, but in the category Set things are nice.

Of course, this is a hand-waving argument, and I’ll make it more rigorous later.

Applying an endofunctor infinitely many times produces a fixed point, an object defined as:

Fix f = f (Fix f)

The intuition behind this definition is that, since we applied f infinitely many times to get Fix f, applying it one more time doesn’t change anything. In Haskell, the definition of a fixed point is:

newtype Fix f = Fix (f (Fix f))

Arguably, this would be more readable if the constructor’s name were different than the name of the type being defined, as in:

newtype Fix f = In (f (Fix f))

but I’ll stick with the accepted notation. The constructor Fix (or In, if you prefer) can be seen as a function:

Fix :: f (Fix f) -> Fix f

There is also a function that peels off one level of functor application:

unFix :: Fix f -> f (Fix f)
unFix (Fix x) = x

The two functions are the inverse of each other. We’ll use these functions later.

Category of F-Algebras

Here’s the oldest trick in the book: Whenever you come up with a way of constructing some new objects, see if they form a category. Not surprisingly, algebras over a given endofunctor F form a category. Objects in that category are algebras — pairs consisting of a carrier object a and a morphism F a -> a, both from the original category C.

To complete the picture, we have to define morphisms in the category of F-algebras. A morphism must map one algebra (a, f) to another algebra (b, g). We’ll define it as a morphism m that maps the carriers — it goes from a to b in the original category. Not any morphism will do: we want it to be compatible with the two evaluators. (We call such a structure-preserving morphism a homomorphism.) Here’s how you define a homomorphism of F-algebras. First, notice that we can lift m to the mapping:

F m :: F a -> F b

we can then follow it with g to get to b. Equivalently, we can use f to go from F a to a and then follow it with m. We want the two paths to be equal:

g ∘ F m = m ∘ f


It’s easy to convince yourself that this is indeed a category (hint: identity morphisms from C work just fine, and a composition of homomorphisms is a homomorphism).

An initial object in the category of F-algebras, if it exists, is called the initial algebra. Let’s call the carrier of this initial algebra i and its evaluator j :: F i -> i. It turns out that j, the evaluator of the initial algebra, is an isomorphism. This result is known as Lambek’s theorem. The proof relies on the definition of the initial object, which requires that there be a unique homomorphism m from it to any other F-algebra. Since m is a homomorphism, the following diagram must commute:


Now let’s construct an algebra whose carrier is F i. The evaluator of such an algebra must be a morphism from F (F i) to F i. We can easily construct such an evaluator simply by lifting j:

F j :: F (F i) -> F i

Because (i, j) is the initial algebra, there must be a unique homomorphism m from it to (F i, F j). The following diagram must commute:


But we also have this trivially commuting diagram (both paths are the same!):


which can be interpreted as showing that j is a homomorphism of algebras, mapping (F i, F j) to (i, j). We can glue these two diagrams together to get:


This diagram may, in turn, be interpreted as showing that j ∘ m is a homomorphism of algebras. Only in this case the two algebras are the same. Moreover, because (i, j) is initial, there can only be one homomorphism from it to itself, and that’s the identity morphism idi — which we know is a homomorphism of algebras. Therefore j ∘ m = idi. Similarly, we can prove that m ∘ j = idF i by stacking the two diagrams in the reverse order. This shows that m is the inverse of j and therefore j is an isomorphism between F i and i:

F i ≅ i

But that is just saying that i is a fixed point of F. That’s the formal proof behind the original hand-waving argument.

Back to Haskell: We recognize i as our Fix f, j as our constructor Fix, and its inverse as unFix. The isomorphism in Lambek’s theorem tells us that, in order to get the initial algebra, we take the functor f and replace its argument a with Fix f. We also see why the fixed point does not depend on a.

Natural Numbers

Natural numbers can also be defined as an F-algebra. The starting point is the pair of morphisms:

zero :: 1 -> N
succ :: N -> N

The first one picks the zero, and the second one maps all numbers to their successors. As before, we can combine the two into one:

1 + N -> N

The left hand side defines a functor which, in Haskell, can be written like this:

data NatF a = ZeroF | SuccF a

The fixed point of this functor (the initial algebra that it generates) can be encoded in Haskell as:

data Nat = Zero | Succ Nat

A natural number is either zero or a successor of another number. This is known as the Peano representation for natural numbers.


Let’s rewrite the initiality condition using Haskell notation. We call the initial algebra Fix f. Its evaluator is the contructor Fix. There is a unique morphism m from the initial algebra to any other algebra over the same functor. Let’s pick an algebra whose carrier is a and the evaluator is alg.

By the way, notice what m is: It’s an evaluator for the fixed point, an evaluator for the whole recursive expression tree. Let’s find a general way of implementing it.

Lambek’s theorem tells us that the constructor Fix is an isomorphism. We called its inverse unFix. We can therefore flip one arrow in this diagram to get:


Let’s write down the commutation condition for this diagram:

m = alg . fmap m . unFix

We can interpret this equation as a recursive definition of m. The recursion is bound to terminate for any finite tree created using the functor f. We can see that by noticing that fmap m operates underneath the top layer of the functor f. In other words, it works on the children of the original tree. The children are always one level shallower than the original tree.

Here’s what happens when we apply m to a tree constructed using Fix f. The action of unFix peels off the constructor, exposing the top level of the tree. We then apply m to all the children of the top node. This produces results of type a. Finally, we combine those results by applying the non-recursive evaluator alg. The key point is that our evaluator alg is a simple non-recursive function.

Since we can do this for any algebra alg, it makes sense to define a higher order function that takes the algebra as a parameter and gives us the function we called m. This higher order function is called a catamorphism:

cata :: Functor f => (f a -> a) -> Fix f -> a
cata alg = alg . fmap (cata alg) . unFix

Let’s see an example of that. Take the functor that defines natural numbers:

data NatF a = ZeroF | SuccF a

Let’s pick (Int, Int) as the carrier type and define our algebra as:

fib :: NatF (Int, Int) -> (Int, Int)
fib ZeroF = (1, 1)
fib (SuccF (m, n)) = (n, m + n)

You can easily convince yourself that the catamorphism for this algebra, cata fib, calculates Fibonacci numbers.

In general, an algebra for NatF defines a recurrence relation: the value of the current element in terms of the previous element. A catamorphism then evaluates the n-th element of that sequence.


A list of e is the initial algebra of the following functor:

data ListF e a = NilF | ConsF e a

Indeed, replacing the variable a with the result of recursion, which we’ll call List e, we get:

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

An algebra for a list functor picks a particular carrier type and defines a function that does pattern matching on the two constructors. Its value for NilF tells us how to evaluate an empty list, and its value for ConsF tells us how to combine the current element with the previously accumulated value.

For instance, here’s an algebra that can be used to calculate the length of a list (the carrier type is Int):

lenAlg :: ListF e Int -> Int
lenAlg (ConsF e n) = n + 1
lenAlg NilF = 0

Indeed, the resulting catamorphism cata lenAlg calculates the length of a list. Notice that the evaluator is a combination of (1) a function that takes a list element and an accumulator and returns a new accumulator, and (2) a starting value, here zero. The type of the value and the type of the accumulator are given by the carrier type.

Compare this to the traditional Haskell definition:

length = foldr (\e n -> n + 1) 0

The two arguments to foldr are exactly the two components of the algebra.

Let’s try another example:

sumAlg :: ListF Double Double -> Double
sumAlg (ConsF e s) = e + s
sumAlg NilF = 0.0

Again, compare this with:

sum = foldr (\e s -> e + s) 0.0

As you can see, foldr is just a convenient specialization of a catamorphism to lists.


As usual, we have a dual construction of an F-coagebra, where the direction of the morphism is reversed:

a -> F a

Coalgebras for a given functor also form a category, with homomorphisms preserving the coalgebraic structure. The terminal object (t, u) in that category is called the terminal (or final) coalgebra. For every other algebra (a, f) there is a unique homomorphism m that makes the following diagram commute:


A terminal colagebra is a fixed point of the functor, in the sense that the morphism u :: t -> F t is an isomorphism (Lambek’s theorem for coalgebras):

F t ≅ t

A terminal coalgebra is usually interpreted in programming as a recipe for generating (possibly infinite) data structures or transition systems.

Just like a catamorphism can be used to evaluate an initial algebra, an anamorphism can be used to coevaluate a terminal coalgebra:

ana :: Functor f => (a -> f a) -> a -> Fix f
ana coalg = Fix . fmap (ana coalg) . coalg

A canonical example of a coalgebra is based on a functor whose fixed point is an infinite stream of elements of type e. This is the functor:

data StreamF e a = StreamF e a
  deriving Functor

and this is its fixed point:

data Stream e = Stream e (Stream e)

A coalgebra for StreamF e is a function that takes the seed of type a and produces a pair (StreamF is a fancy name for a pair) consisting of an element and the next seed.

You can easily generate simple examples of coalgebras that produce infinite sequences, like the list of squares, or reciprocals.

A more interesting example is a coalgebra that produces a list of primes. The trick is to use an infinite list as a carrier. Our starting seed will be the list [2..]. The next seed will be the tail of this list with all multiples of 2 removed. It’s a list of odd numbers starting with 3. In the next step, we’ll take the tail of this list and remove all multiples of 3, and so on. You might recognize the makings of the sieve of Eratosthenes. This coalgebra is implemented by the following function:

era :: [Int] -> StreamF Int [Int]
era (p : ns) = StreamF p (filter (notdiv p) ns)
    where notdiv p n = n `mod` p /= 0

The anamorphism for this coalgebra generates the list of primes:

primes = ana era [2..]

A stream is an infinite list, so it should be possible to convert it to a Haskell list. To do that, we can use the same functor StreamF to form an algebra, and we can run a catamorphism over it. For instance, this is a catamorphism that converts a stream to a list:

toListC :: Fix (StreamF e) -> [e]
toListC = cata al
   where al :: StreamF e [e] -> [e]
         al (StreamF e a) = e : a

Here, the same fixed point is simultaneously an initial algebra and a terminal coalgebra for the same endofunctor. It’s not always like this, in an arbitrary category. In general, an endofunctor may have many (or no) fixed points. The initial algebra is the so called least fixed point, and the terminal coalgebra is the greatest fixed point. In Haskell, though, both are defined by the same formula, and they coincide.

The anamorphism for lists is called unfold. To create finite lists, the functor is modified to produce a Maybe pair:

unfoldr :: (b -> Maybe (a, b)) -> b -> [a]

The value of Nothing will terminate the generation of the list.

An interesting case of a coalgebra is related to lenses. A lens can be represented as a pair of a getter and a setter:

set :: a -> s -> a
get :: a -> s

Here, a is usually some product data type with a field of type s. The getter retrieves the value of that field and the setter replaces this field with a new value. These two functions can be combined into one:

a -> (s, s -> a)

We can rewrite this function further as:

a -> Store s a

where we have defined a functor:

data Store s a = Store (s -> a) s

Notice that this is not a simple algebraic functor constructed from sums of products. It involves an exponential as.

A lens is a coalgebra for this functor with the carrier type a. We’ve seen before that Store s is also a comonad. It turns out that a well-behaved lens corresponds to a coalgebra that is compatible with the comonad structure. We’ll talk about this in the next section.


  1. Implement the evaluation function for a ring of polynomials of one variable. You can represent a polynomial as a list of coefficients in front of powers of x. For instance, 4x2-1 would be represented as (starting with the zero’th power) [-1, 0, 4].
  2. Generalize the previous construction to polynomials of many independent variables, like x2y-3y3z.
  3. Implement the algebra for the ring of 2×2 matrices.
  4. Define a coalgebra whose anamorphism produces a list of squares of natural numbers.
  5. Use unfoldr to generate a list of the first n primes.

Next: Algebras for Monads.

Unlike monads, which came into programming straight from category theory, applicative functors have their origins in programming. McBride and Paterson introduced applicative functors as a programming pearl in their paper Applicative programming with effects. They also provided a categorical interpretation of applicatives in terms of strong lax monoidal functors. It’s been accepted that, just like “a monad is a monoid in the category of endofunctors,” so “an applicative is a strong lax monoidal functor.”

The so called “tensorial strength” seems to be important in categorical semantics, and in his seminal paper Notions of computation and monads, Moggi argued that effects should be described using strong monads. It makes sense, considering that a computation is done in a context, and you should be able to make the global context available under the monad. The fact that we don’t talk much about strong monads in Haskell is due to the fact that all functors in the category Set, which underlies the Haskell’s type system, have canonical strength. So why do we talk about strength when dealing with applicative functors? I have looked into this question and came to the conclusion that there is no fundamental reason, and that it’s okay to just say:

An applicative is a lax monoidal functor

In this post I’ll discuss different equivalent categorical definitions of the applicative functor. I’ll start with a lax closed functor, then move to a lax monoidal functor, and show the equivalence of the two definitions. Then I’ll introduce the calculus of ends and show that the third definition of the applicative functor as a monoid in a suitable functor category equipped with Day convolution is equivalent to the previous ones.

Applicative as a Lax Closed Functor

The standard definition of the applicative functor in Haskell reads:

class Functor f => Applicative f where
    (<*>) :: f (a -> b) -> (f a -> f b)
    pure :: a -> f a

At first sight it doesn’t seem to involve a monoidal structure. It looks more like preserving function arrows (I added some redundant parentheses to suggest this interpretation).

Categorically, functors that “preserve arrows” are known as closed functors. Let’s look at a definition of a closed functor f between two categories C and D. We have to assume that both categories are closed, meaning that they have internal hom-objects for every pair of objects. Internal hom-objects are also called function objects or exponentials. They are normally defined through the right adjoint to the product functor:

C(z × a, b) ≅ C(z, a => b)

To distinguish between sets of morphisms and function objects (they are the same thing in Set), I will temporarily use double arrows for function objects.

We can take a functor f and act with it on the function object a=>b in the category C. We get an object f (a=>b) in D. Or we can map the two objects a and b from C to D and then construct the function object in D: f a => f b.


We call a functor closed if the two results are isomorphic (I have subscripted the two arrows with the categories where they are defined):

f (a =>C b) ≅ (f a =>D f b)

and if the functor preserves the unit object:

iD ≅ f iC

What’s the unit object? Normally, this is the unit with respect to the same product that was used to define the function object using the adjunction. I’m saying “normally,” because it’s possible to define a closed category without a product.

Note: The two arrows and the two is are defined with respect to two different products. The first isomorphism must be natural in both a and b. Also, to complete the picture, there are some diagrams that must commute.

The two isomorphisms that define a closed functor can be relaxed and replaced by unidirectional morphisms. The result is a lax closed functor:

f (a => b) -> (f a => f b)
i -> f i

This looks almost like the definition of Applicative, except for one problem: how can we recover the natural transformation we call pure from a single morphism i -> f i.

One way to do it is from the position of strength. An endofunctor f has tensorial strength if there is a natural transformation:

stc a :: c ⊗ f a -> f (c ⊗ a)

Think of c as the context in which the computation f a is performed. Strength means that we can use this external context inside the computation.

In the category Set, with the tensor product replaced by cartesian product, all functors have canonical strength. In Haskell, we would define it as:

st (c, fa) = fmap ((,) c) fa

The morphism in the definition of the lax closed functor translates to:

unit :: () -> f ()

Notice that, up to isomorphism, the unit type () is the unit with respect to cartesian product. The relevant isomorphisms are:

λa :: ((), a) -> a
ρa :: (a, ()) -> a

Here’s the derivation from Rivas and Jaskelioff’s Notions of Computation as Monoids:

≅  (a, ())   -- unit law, ρ-1
-> (a, f ()) -- lax unit
-> f (a, ()) -- strength
≅  f a       -- lifted unit law, f ρ

Strength is necessary if you’re starting with a lax closed (or monoidal — see the next section) endofunctor in an arbitrary closed (or monoidal) category and you want to derive pure within that category — not after you restrict it to Set.

There is, however, an alternative derivation using the Yoneda lemma:

f ()
≅ forall a. (() -> a) -> f a  -- Yoneda
≅ forall a. a -> f a -- because: (() -> a) ≅ a

We recover the whole natural transformation from a single value. The advantage of this derivation is that it generalizes beyond endofunctors and it doesn’t require strength. As we’ll see later, it also ties nicely with the Day-convolution definition of applicative. The Yoneda lemma only works for Set-valued functors, but so does Day convolution (there are enriched versions of both Yoneda and Day convolution, but I’m not going to discuss them here).

We can define the categorical version of the Haskell’s applicative functor as a lax closed functor going from a closed category C to Set. It’s a functor equipped with a natural transformation:

f (a => b) -> (f a -> f b)

where a=>b is the internal hom-object in C (the second arrow is a function type in Set), and a function:

1 -> f i

where 1 is the singleton set and i is the unit object in C.

The importance of a categorical definition is that it comes with additional identities or “axioms.” A lax closed functor must be compatible with the structure of both categories. I will not go into details here, because we are really only interested in closed categories that are monoidal, where these axioms are easier to express.

The definition of a lax closed functor is easily translated to Haskell:

class Functor f => Closed f where
    (<*>) :: f (a -> b) -> f a -> f b
    unit :: f ()

Applicative as a Lax Monoidal Functor

Even though it’s possible to define a closed category without a monoidal structure, in practice we usually work with monoidal categories. This is reflected in the equivalent definition of the Haskell’s applicative functor as a lax monoidal functor. In Haskell, we would write:

class Functor f => Monoidal f where
    (>*<) :: (f a, f b) -> f (a, b)
    unit :: f ()

This definition is equivalent to our previous definition of a closed functor. That’s because, as we’ve seen, a function object in a monoidal category is defined in terms of a product. We can show the equivalence in a more general categorical setting.

This time let’s start with a symmetric closed monoidal category C, in which the function object is defined through the right adjoint to the tensor product:

C(z ⊗ a, b) ≅ C(z, a => b)

As usual, the tensor product is associative and unital — with the unit object i — up to isomorphism. The symmetry is defined through natural isomorphism:

γ :: a ⊗ b -> b ⊗ a

A functor f between two monoidal categories is lax monoidal if there exist: (1) a natural transformation

f a ⊗ f b -> f (a ⊗ b)

and (2) a morphism

i -> f i

Notice that the products and units on either side of the two mappings are from different categories.

A (lax-) monoidal functor must also preserve associativity and unit laws.

For instance a triple product

f a ⊗ (f b ⊗ f c)

may be rearranged using an associator α to give

(f a ⊗ f b) ⊗ f c

then converted to

f (a ⊗ b) ⊗ f c

and then to

f ((a ⊗ b) ⊗ c)

Or it could be first converted to

f a ⊗ f (b ⊗ c)

and then to

f (a ⊗ (b ⊗ c))

These two should be equivalent under the associator in C.


Similarly, f a ⊗ i can be simplified to f a using the right unitor ρ in D. Or it could be first converted to f a ⊗ f i, then to f (a ⊗ i), and then to f a, using the right unitor in C. The two paths should be equivalent. (Similarly for the left identity.)


We will now consider functors from C to Set, with Set equipped with the usual cartesian product, and the singleton set as unit. A lax monoidal functor is defined by: (1) a natural transformation:

(f a, f b) -> f (a ⊗ b)

and (2) a choice of an element of the set f i (a function from 1 to f i picks an element from that set).

We need the target category to be Set because we want to be able to use the Yoneda lemma to show equivalence with the standard definition of applicative. I’ll come back to this point later.

The Equivalence

The definitions of a lax closed and a lax monoidal functors are equivalent when C is a closed symmetric monoidal category. The proof relies on the existence of the adjunction, in particular the unit and the counit of the adjunction:

ηa :: a -> (b => (a ⊗ b))
εb :: (a => b) ⊗ a -> b

For instance, let’s assume that f is lax-closed. We want to construct the mapping

(f a, f b) -> f (a ⊗ b)

First, we apply the lifted pair (unit, identity), (f η, f id)

(f a -> f (b => a ⊗ b), f id)

to the left hand side. We get:

(f (b => a ⊗ b), f b)

Now we can use (the uncurried version of) the lax-closed morphism:

(f (b => x), f b) -> f x

to get:

f (a ⊗ b)

Conversely, assuming the lax-monoidal property we can show that the functor is lax-closed, that is to say, implement the following function:

(f (a => b), f a) -> f b

First we use the lax monoidal morphism on the left hand side:

f ((a => b) ⊗ a)

and then use the counit (a.k.a. the evaluation morphism) to get the desired result f b

There is yet another presentation of applicatives using Day convolution. But before we get there, we need a little refresher on calculus.

Calculus of Ends

Ends and coends are very useful constructs generalizing limits and colimits. They are defined through universal constructions. They have a few fundamental properties that are used over and over in categorical calculations. I’ll just introduce the notation and a few important identities. We’ll be working in a symmetric monoidal category C with functors from C to Set and profunctors from Cop×C to Set. The end of a profunctor p is a set denoted by:

a p a a

The most important thing about ends is that a set of natural transformations between two functors f and g can be represented as an end:

[C, Set](f, g) = ∫a C(f a, g a)

In Haskell, the end corresponds to universal quantification over a functor of mixed variance. For instance, the natural transformation formula takes the familiar form:

forall a. f a -> g a

The Yoneda lemma, which deals with natural transformations, can also be written using an end:

z (C(a, z) -> f z) ≅ f a

In Haskell, we can write it as the equivalence:

forall z. ((a -> z) -> f z) ≅ f a

which is a generalization of the continuation passing transform.

The dual notion of coend is similarly written using an integral sign, with the “integration variable” in the superscript position:

a p a a

In pseudo-Haskell, a coend is represented by an existential quantifier. It’s possible to define existential data types in Haskell by converting existential quantification to universal one. The relevant identity in terms of coends and ends reads:

(∫ z p z z) -> y ≅ ∫ z (p z z -> y)

In Haskell, this formula is used to turn functions that take existential types to functions that are polymorphic:

(exists z. p z z) -> y ≅ forall z. (p z z -> y)

Intuitively, it makes perfect sense. If you want to define a function that takes an existential type, you have to be prepared to handle any type.

The equivalent of the Yoneda lemma for coends reads:

z f z × C(z, a) ≅ f a

or, in pseudo-Haskell:

exists z. (f z, z -> a) ≅ f a

(The intuition is that the only thing you can do with this pair is to fmap the function over the first component.)

There is also a contravariant version of this identity:

z C(a, z) × f z ≅ f a

where f is a contravariant functor (a.k.a., a presheaf). In pseudo-Haskell:

exists z. (a -> z, f z) ≅ f a

(The intuition is that the only thing you can do with this pair is to apply the contramap of the first component to the second component.)

Using coends we can define a tensor product in the category of functors [C, Set]. This product is called Day convolution:

(f ★ g) a = ∫ x y f x × g y × C(x ⊗ y, a)

It is a bifunctor in that category (read, it can be used to lift natural transformations). It’s associative and symmetric up to isomorphism. It also has a unit — the hom-functor C(i, -), where i is the monoidal unit in C. In other words, Day convolution imbues the category [C, Set] with monoidal structure.

Let’s verify the unit laws.

(C(i, -) ★ g) a = ∫ x y C(i, x) × g y × C(x ⊗ y, a)

We can use the contravariant Yoneda to “integrate over x” to get:

y g y × C(i ⊗ y, a)

Considering that i is the unit of the tensor product in C, we get:

y g y × C(y, a)

Covariant Yoneda lets us “integrate over y” to get the desired g a. The same method works for the right unit law.

Applicative as a Monoid

Given a monoidal category, we can always define a monoid as an object m equipped with two morphisms:

μ :: m ⊗ m -> m
η :: i -> m

satisfying the laws of associativity and unitality.

We have shown that the functor category [C, Set] (with C a symmetric monoidal category) is monoidal under Day convolution. An object in this category is a functor f. The two morphisms that would make it a candidate for a monoid are natural transformations:

μ :: f ★ f -> f
η :: C(i, -) -> f

The a component of the natural transformation μ can be rewritten as:

(∫ x y f x × f y × C(x ⊗ y, a)) -> f a

which is equivalent to:

x y (f x × f y × C(x ⊗ y, a) -> f a)

or, upon currying:

x y (f x, f y) -> C(x ⊗ y, a) -> f a

It turns out that so defined monoid is equivalent to a lax monoidal functor. This was shown by Rivas and Jaskelioff. The following derivation is due to Bob Atkey.

The trick is to start with the whole set of natural transformation from f★f to f. The multiplication μ is just one of them. We’ll express the set of natural transformations as an end:

a ((f ★ f) a -> f a)

Plugging in the formula for the a component of μ, we get:

a x y (f x, f y) -> C(x ⊗ y, a) -> f a

The end over a does not involve the first argument, so we can move the integral sign:

x y (f x, f y) -> ∫ a C(x ⊗ y, a) -> f a

Then we use the Yoneda lemma to “perform the integration” over a:

x y (f x, f y) -> f (x ⊗ y)

You may recognize this as a set of natural transformations that define a lax monoidal functor. We have established a one-to-one correspondence between these natural transformations and the ones defining monoidal multiplication using Day convolution.

The remaining part is to show the equivalence between the unit with respect to Day convolution and the second part of the definition of the lax monoidal functor, the morphism:

1 -> f i

We start with the set of natural transformations that contains our η:

a (i -> a) -> f a

By Yoneda, this is just f i. Picking an element from a set is equivalent to defining a morphism from the singleton set 1, so for any choice of η we get:

1 -> f i

and vice versa. The two definitions are equivalent.

Notice that the monoidal unit η under Day convolution becomes the definition of pure in the Haskell version of applicative. Indeed, when we replace the category C with Set, f becomes and endofunctor, and the unit of Day convolution C(i, -) becomes the identity functor Id. We get:

η :: Id -> f

or, in components:

pure :: a -> f a

So, strictly speaking, the Haskell definition of Applicative mixes the elements of the lax closed functor and the monoidal unit under Day convolution.


I’m grateful to Mauro Jaskelioff and Exequiel Rivas for correspondence and to Bob Atkey, Dimitri Chikhladze, and Make Shulman for answering my questions on Math Overflow.

This is part 23 of Categories for Programmers. Previously: Monads Categorically. See the Table of Contents.

Now that we have covered monads, we can reap the benefits of duality and get comonads for free simply by reversing the arrows and working in the opposite category.

Recall that, at the most basic level, monads are about composing Kleisli arrows:

a -> m b

where m is a functor that is a monad. If we use the letter w (upside down m) for the comonad, we can define co-Kleisli arrows as morphism of the type:

w a -> b

The analog of the fish operator for co-Kleisli arrows is defined as:

(=>=) :: (w a -> b) -> (w b -> c) -> (w a -> c)

For co-Kleisli arrows to form a category we also have to have an identity co-Kleisli arrow, which is called extract:

extract :: w a -> a

This is the dual of return. We also have to impose the laws of associativity as well as left- and right-identity. Putting it all together, we could define a comonad in Haskell as:

class Functor w => Comonad w where
    (=>=) :: (w a -> b) -> (w b -> c) -> (w a -> c)
    extract :: w a -> a

In practice, we use slightly different primitives, as we’ll see shortly.

The question is, what’s the use for comonads in programming?

Programming with Comonads

Let’s compare the monad with the comonad. A monad provides a way of putting a value in a container using return. It doesn’t give you access to a value or values stored inside. Of course, data structures that implement monads might provide access to their contents, but that’s considered a bonus. There is no common interface for extracting values from a monad. And we’ve seen the example of the IO monad that prides itself in never exposing its contents.

A comonad, on the other hand, provides the means of extracting a single value from it. It does not give the means to insert values. So if you want to think of a comonad as a container, it always comes pre-filled with contents, and it lets you peek at it.

Just as a Kleisli arrow takes a value and produces some embellished result — it embellishes it with context — a co-Kleisli arrow takes a value together with a whole context and produces a result. It’s an embodiment of contextual computation.

The Product Comonad

Remember the reader monad? We introduced it to tackle the problem of implementing computations that need access to some read-only environment e. Such computations can be represented as pure functions of the form:

(a, e) -> b

We used currying to turn them into Kleisli arrows:

a -> (e -> b)

But notice that these functions already have the form of co-Kleisli arrows. Let’s massage their arguments into the more convenient functor form:

data Product e a = P e a
  deriving Functor

We can easily define the composition operator by making the same environment available to the arrows that we are composing:

(=>=) :: (Product e a -> b) -> (Product e b -> c) -> (Product e a -> c)
f =>= g = \(P e a) -> let b = f (P e a)
                          c = g (P e b)
                       in c

The implementation of extract simply ignores the environment:

extract (P e a) = a

Not surprisingly, the product comonad can be used to perform exactly the same computations as the reader monad. In a way, the comonadic implementation of the environment is more natural — it follows the spirit of “computation in context.” On the other hand, monads come with the convenient syntactic sugar of the do notation.

The connection between the reader monad and the product comonad goes deeper, having to do with the fact that the reader functor is the right adjoint of the product functor. In general, though, comonads cover different notions of computation than monads. We’ll see more examples later.

It’s easy to generalize the Product comonad to arbitrary product types including tuples and records.

Dissecting the Composition

Continuing the process of dualization, we could go ahead and dualize monadic bind and join. Alternatively, we can repeat the process we used with monads, where we studied the anatomy of the fish operator. This approach seems more enlightening.

The starting point is the realization that the composition operator must produce a co-Kleisli arrow that takes w a and produces a c. The only way to produce a c is to apply the second function to an argument of the type w b:

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

But how can we produce a value of type w b that could be fed to g? We have at our disposal the argument of type w a and the function f :: w a -> b. The solution is to define the dual of bind, which is called extend:

extend :: (w a -> b) -> w a -> w b

Using extend we can implement composition:

f =>= g = g . extend f

Can we next dissect extend? You might be tempted to say, why not just apply the function w a -> b to the argument w a, but then you quickly realize that you’d have no way of converting the resulting b to w b. Remember, the comonad provides no means of lifting values. At this point, in the analogous construction for monads, we used fmap. The only way we could use fmap here would be if we had something of the type w (w a) at our disposal. If we coud only turn w a into w (w a). And, conveniently, that would be exactly the dual of join. We call it duplicate:

duplicate :: w a -> w (w a)

So, just like with the definitions of the monad, we have three equivalent definitions of the comonad: using co-Kleisli arrows, extend, or duplicate. Here’s the Haskell definition taken directly from Control.Comonad library:

class Functor w => Comonad w where
  extract :: w a -> a
  duplicate :: w a -> w (w a)
  duplicate = extend id
  extend :: (w a -> b) -> w a -> w b
  extend f = fmap f . duplicate

Provided are the default implementations of extend in terms of duplicate and vice versa, so you only need to override one of them.

The intuition behind these functions is based on the idea that, in general, a comonad can be thought of as a container filled with values of type a (the product comonad was a special case of just one value). There is a notion of the “current” value, one that’s easily accessible through extract. A co-Kleisli arrow performs some computation that is focused on the current value, but it has access to all the surrounding values. Think of the Conway’s game of life. Each cell contains a value (usually just True or False). A comonad corresponding to the game of life would be a grid of cells focused on the “current” cell.

So what does duplicate do? It takes a comonadic container w a and produces a container of containers w (w a). The idea is that each of these containers is focused on a different a inside w a. In the game of life, you would get a grid of grids, each cell of the outer grid containing an inner grid that’s focused on a different cell.

Now look at extend. It takes a co-Kleisli arrow and a comonadic container w a filled with as. It applies the computation to all of these as, replacing them with bs. The result is a comonadic container filled with bs. extend does it by shifting the focus from one a to another and applying the co-Kleisli arrow to each of them in turn. In the game of life, the co-Kleisli arrow would calculate the new state of the current cell. To do that, it would look at its context — presumably its nearest neighbors. The default implementation of extend illustrates this process. First we call duplicate to produce all possible foci and then we apply f to each of them.

The Stream Comonad

This process of shifting the focus from one element of the container to another is best illustrated with the example of an infinite stream. Such a stream is just like a list, except that it doesn’t have the empty constructor:

data Stream a = Cons a (Stream a)

It’s trivially a Functor:

instance Functor Stream where
    fmap f (Cons a as) = Cons (f a) (fmap f as)

The focus of a stream is its first element, so here’s the implementation of extract:

extract (Cons a _) = a

duplicate produces a stream of streams, each focused on a different element.

duplicate (Cons a as) = Cons (Cons a as) (duplicate as)

The first element is the original stream, the second element is the tail of the original stream, the third element is its tail, and so on, ad infinitum.

Here’s the complete instance:

instance Comonad Stream where
    extract (Cons a _) = a
    duplicate (Cons a as) = Cons (Cons a as) (duplicate as)

This is a very functional way of looking at streams. In an imperative language, we would probably start with a method advance that shifts the stream by one position. Here, duplicate produces all shifted streams in one fell swoop. Haskell’s laziness makes this possible and even desirable. Of course, to make a Stream practical, we would also implement the analog of advance:

tail :: Stream a -> Stream a
tail (Cons a as) = as

but it’s never part of the comonadic interface.

If you had any experience with digital signal processing, you’ll see immediately that a co-Kleisli arrow for a stream is just a digital filter, and extend produces a filtered stream.

As a simple example, let’s implement the moving average filter. Here’s a function that sums n elements of a stream:

sumS :: Num a => Int -> Stream a -> a
sumS n (Cons a as) = if n <= 0 then 0 else a + sumS (n - 1) as

Here’s the function that calculates the average of the first n elements of the stream:

average :: Fractional a => Int -> Stream a -> a
average n stm = (sumS n stm) / (fromIntegral n)

Partially applied average n is a co-Kleisli arrow, so we can extend it over the whole stream:

movingAvg :: Fractional a => Int -> Stream a -> Stream a
movingAvg n = extend (average n)

The result is the stream of running averages.

A stream is an example of a unidirectional, one-dimensional comonad. It can be easily made bidirectional or extended to two or more dimensions.

Comonad Categorically

Defining a comonad in category theory is a straightforward exercise in duality. As with the monad, we start with an endofunctor T. The two natural transformations, η and μ, that define the monad are simply reversed for the comonad:

ε :: T -> I
δ :: T -> T2

The components of these transformations correspond to extract and duplicate. Comonad laws are the mirror image of monad laws. No big surprise here.

Then there is the derivation of the monad from an adjunction. Duality reverses an adjunction: the left adjoint becomes the right adjoint and vice versa. And, since the composition R ∘ L defines a monad, L ∘ R must define a comonad. The counit of the adjunction:

ε :: L ∘ R -> I

is indeed the same ε that we see in the definition of the comonad — or, in components, as Haskell’s extract. We can also use the unit of the adjunction:

η :: I -> R ∘ L

to insert an R ∘ L in the middle of L ∘ R and produce L ∘ R ∘ L ∘ R. Making T2 from T defines the δ, and that completes the definition of the comonad.

We’ve also seen that the monad is a monoid. The dual of this statement would require the use of a comonoid, so what’s a comonoid? The original definition of a monoid as a single-object category doesn’t dualize to anything interesting. When you reverse the direction of all endomorphisms, you get another monoid. Recall, however, that in our approach to a monad, we used a more general definition of a monoid as an object in a monoidal category. The construction was based on two morphisms:

μ :: m ⊗ m -> m
η :: i -> m

The reversal of these morphisms produces a comonoid in a monoidal category:

δ :: m -> m ⊗ m
ε :: m -> i

One can write a definition of a comonoid in Haskell:

class Comonoid m where
  split   :: m -> (m, m)
  destroy :: m -> ()

but it is rather trivial. Obviously destroy ignores its argument.

destroy _ = ()

split is just a pair of functions:

split x = (f x, g x)

Now consider comonoid laws that are dual to the monoid unit laws.

lambda . bimap destroy id . split = id
rho . bimap id destroy . split = id

Here, lambda and rho are the left and right unitors, respectively (see the definition of monoidal categories). Plugging in the definitions, we get:

lambda (bimap destroy id (split x))
= lambda (bimap destroy id (f x, g x))
= lambda ((), g x)
= g x

which proves that g = id. Similarly, the second law expands to f = id. In conclusion:

split x = (x, x)

which shows that in Haskell (and, in general, in the category Set) every object is a trivial comonoid.

Fortunately there are other more interesting monoidal categories in which to define comonoids. One of them is the category of endofunctors. And it turns out that, just like the monad is a monoid in the category of endofunctors,

The comonad is a comonoid in the category of endofunctors.

The Store Comonad

Another important example of a comonad is the dual of the state monad. It’s called the costate comonad or, alternatively, the store comonad.

We’ve seen before that the state monad is generated by the adjunction that defines the exponentials:

L z = z × s
R a = s ⇒ a

We’ll use the same adjunction to define the costate comonad. A comonad is defined by the composition L ∘ R:

L (R a) = (s ⇒ a) × s

Translating this to Haskell, we start with the adjunction between the Prod functor on the left and the Reader functor or the right. Composing Prod after Reader is equivalent to the following definition:

data Store s a = Store (s -> a) s

The counit of the adjunction taken at the object a is the morphism:

εa :: ((s ⇒ a) × s) -> a

or, in Haskell notation:

counit (Prod (Reader f, s)) = f s

This becomes our extract:

extract (Store f s) = f s

The unit of the adjunction:

unit a = Reader (\s -> Prod (a, s))

can be rewritten as partially applied data constructor:

Store f :: s -> Store f s

We construct δ, or duplicate, as the horizontal composition:

δ :: L ∘ R -> L ∘ R ∘ L ∘ R
δ = L ∘ η ∘ R

We have to sneak η through the leftmost L, which is the Prod functor. It means acting with η, or Store f, on the left component of the pair (that’s what fmap for Prod would do). We get:

duplicate (Store f s) = Store (Store f) s

(Remember that, in the formula for δ, L and R stand for identity natural transformations whose components are identity morphisms.)

Here’s the complete definition of the Store comonad:

instance Comonad (Store s) where
  extract (Store f s) = f s
  duplicate (Store f s) = Store (Store f) s

You may think of the Reader part of Store as a generalized container of as that are keyed using elements of the type s. For instance, if s is Int, Reader Int a is an infinite bidirectional stream of as. Store pairs this container with a value of the key type. For instance, Reader Int a is paired with an Int. In this case, extract uses this integer to index into the infinite stream. You may think of the second component of Store as the current position.

Continuing with this example, duplicate creates a new infinite stream indexed by an Int. This stream contains streams as its elements. In particular, at the current position, it contains the original stream. But if you use some other Int (positive or negative) as the key, you’d obtain a shifted stream positioned at that new index.

In general, you can convince yourself that when extract acts on the duplicated Store it produces the original Store (in fact, the identity law for the comonad states that extract . duplicate = id).

The Store comonad plays an important role as the theoretical basis for the Lens library. Conceptually, the Store s a comonad encapsulates the idea of “focusing” (like a lens) on a particular substructure of the date type a using the type s as an index. In particular, a function of the type:

a -> Store s a

is equivalent to a pair of functions:

set :: a -> s -> a
get :: a -> s

If a is a product type, set could be implemented as setting the field of type s inside of a while returning the modified version of a. Similarly, get could be implemented to read the value of the s field from a. We’ll explore these ideas more in the next section.


  1. Implement the Conway’s Game of Life using the Store comonad. Hint: What type do you pick for s?


I’m grateful to Edward Kmett for reading the draft of this post and pointing out flaws in my reasoning.

Next: F-Algebras.

This is part 22 of Categories for Programmers. Previously: Monads and Effects. See the Table of Contents.

If you mention monads to a programmer, you’ll probably end up talking about effects. To a mathematician, monads are about algebras. We’ll talk about algebras later — they play an important role in programming — but first I’d like to give you a little intuition about their relation to monads. For now, it’s a bit of a hand-waving argument, but bear with me.

Algebra is about creating, manipulating, and evaluating expressions. Expressions are built using operators. Consider this simple expression:

x2 + 2 x + 1

This expression is formed using variables like x, and constants like 1 or 2, bound together with operators like plus or times. As programmers, we often think of expressions as trees.


Trees are containers so, more generally, an expression is a container for storing variables. In category theory, we represent containers as endofunctors. If we assign the type a to the variable x, our expression will have the type m a, where m is an endofunctor that builds expression trees. (Nontrivial branching expressions are usually created using recursively defined endofunctors.)

What’s the most common operation that can be performed on an expression? It’s substitution: replacing variables with expressions. For instance, in our example, we could replace x with y - 1 to get:

(y - 1)2 + 2 (y - 1) + 1

Here’s what happened: We took an expression of type m a and applied a transformation of type a -> m b (b represents the type of y). The result is an expression of type m b. Let me spell it out:

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

Yes, that’s the signature of monadic bind.

That was a bit of motivation. Now let’s get to the math of the monad. Mathematicians use different notation than programmers. They prefer to use the letter T for the endofunctor, and Greek letters: μ for join and η for return. Both join and return are polymorphic functions, so we can guess that they correspond to natural transformations.

Therefore, in category theory, a monad is defined as an endofunctor T equipped with a pair of natural transformations μ and η.

μ is a natural transformation from the square of the functor T2 back to T. The square is simply the functor composed with itself, T ∘ T (we can only do this kind of squaring for endofunctors).

μ :: T2 -> T

The component of this natural transformation at an object a is the morphism:

μa :: T (T a) -> T a

which, in Hask, translates directly to our definition of join.

η is a natural transformation between the identity functor I and T:

η :: I -> T

Considering that the action of I on the object a is just a, the component of η is given by the morphism:

ηa :: a -> T a

which translates directly to our definition of return.

These natural transformations must satisfy some additional laws. One way of looking at it is that these laws let us define a Kleisli category for the endofunctor T. Remember that a Kleisli arrow between a and b is defined as a morphism a -> T b. The composition of two such arrows (I’ll write it as a circle with the subscript T) can be implemented using μ:

g ∘T f = μc ∘ (T g) ∘ f


f :: a -> T b
g :: b -> T c

Here T, being a functor, can be applied to the morphism g. It might be easier to recognize this formula in Haskell notation:

f >=> g = join . fmap g . f

or, in components:

(f >=> g) a = join (fmap g (f a))

In terms of the algebraic interpretation, we are just composing two successive substitutions.

For Kleisli arrows to form a category we want their composition to be associative, and ηa to be the identity Kleisli arrow at a. This requirement can be translated to monadic laws for μ and η. But there is another way of deriving these laws that makes them look more like monoid laws. In fact μ is often called multiplication, and η unit.

Roughly speaking, the associativity law states that the two ways of reducing the cube of T, T3, down to T must give the same result. Two unit laws (left and right) state that when η is applied to T and then reduced by μ, we get back T.

Things are a little tricky because we are composing natural transformations and functors. So a little refresher on horizontal composition is in order. For instance, T3 can be seen as a composition of T after T2. We can apply to it the horizontal composition of two natural transformations:

IT ∘ μ


and get T∘T; which can be further reduced to T by applying μ. IT is the identity natural transformation from T to T. You will often see the notation for this type of horizontal composition IT ∘ μ shortened to T∘μ. This notation is unambiguous because it makes no sense to compose a functor with a natural transformation, therefore T must mean IT in this context.

We can also draw the diagram in the (endo-) functor category [C, C]:


Alternatively, we can treat T3 as the composition of T2∘T and apply μ∘T to it. The result is also T∘T which, again, can be reduced to T using μ. We require that the two paths produce the same result.


Similarly, we can apply the horizontal composition η∘T to the composition of the identity functor I after T to obtain T2, which can then be reduced using μ. The result should be the same as if we applied the identity natural transformation directly to T. And, by analogy, the same should be true for T∘η.


You can convince yourself that these laws guarantee that the composition of Kleisli arrows indeed satisfies the laws of a category.

The similarities between a monad and a monoid are striking. We have multiplication μ, unit η, associativity, and unit laws. But our definition of a monoid is too narrow to describe a monad as a monoid. So let’s generalize the notion of a monoid.

Monoidal Categories

Let’s go back to the conventional definition of a monoid. It’s a set with a binary operation and a special element called unit. In Haskell, this can be expressed as a typeclass:

class Monoid m where
    mappend :: m -> m -> m
    mempty  :: m

The binary operation mappend must be associative and unital (i.e., multiplication by the unit mempty is a no-op).

Notice that, in Haskell, the definition of mappend is curried. It can be interpreted as mapping every element of m to a function:

mappend :: m -> (m -> m)

It’s this interpretation that gives rise to the definition of a monoid as a single-object category where endomorphisms (m -> m) represent the elements of the monoid. But because currying is built into Haskell, we could as well have started with a different definition of multiplication:

mu :: (m, m) -> m

Here, the cartesian product (m, m) becomes the source of pairs to be multiplied.

This definition suggests a different path to generalization: replacing the cartesian product with categorical product. We could start with a category where products are globally defined, pick an object m there, and define multiplication as a morphism:

μ :: m × m -> m

We have one problem though: In an arbitrary category we can’t peek inside an object, so how do we pick the unit element? There is a trick to it. Remember how element selection is equivalent to a function from the singleton set? In Haskell, we could replace the definition of mempty with a function:

eta :: () -> m

The singleton is the terminal object in Set, so it’s natural to generalize this definition to any category that has a terminal object t:

η :: t -> m

This lets us pick the unit “element” without having to talk about elements.

Unlike in our previous definition of a monoid as a single-object category, monoidal laws here are not automatically satisfied — we have to impose them. But in order to formulate them we have to establish the monoidal structure of the underlying categorical product itself. Let’s recall how monoidal structure works in Haskell first.

We start with associativity. In Haskell, the corresponding equational law is:

mu x (mu y z) = mu (mu x y) z

Before we can generalize it to other categories, we have to rewrite it as an equality of functions (morphisms). We have to abstract it away from its action on individual variables — in other words, we have to use point-free notation. Knowning that the cartesian product is a bifunctor, we can write the left hand side as:

(mu . bimap id mu)(x, (y, z))

and the right hand side as:

(mu . bimap mu id)((x, y), z)

This is almost what we want. Unfortunately, the cartesian product is not strictly associative — (x, (y, z)) is not the same as ((x, y), z) — so we can’t just write point-free:

mu . bimap id mu = mu . bimap mu id

On the other hand, the two nestings of pairs are isomorphic. There is an invertible function called the associator that converts between them:

alpha :: ((a, b), c) -> (a, (b, c))
alpha ((x, y), z) = (x, (y, z))

With the help of the associator, we can write the point-free associativity law for mu:

mu . bimap id mu . alpha = mu . bimap mu id

We can apply a similar trick to unit laws which, in the new notation, take the form:

mu (eta (), x) = x
mu (x, eta ()) = x

They can be rewritten as:

(mu . bimap eta id) ((), x) = lambda ((), x)
(mu . bimap id eta) (x, ()) = rho (x, ())

The isomorphisms lambda and rho are called the left and right unitor, respectively. They witness the fact that the unit () is the identity of the cartesian product up to isomorphism:

lambda :: ((), a) -> a
lambda ((), x) = x
rho :: (a, ()) -> a
rho (x, ()) = x

The point-free versions of the unit laws are therefore:

mu . bimap id eta = lambda
mu . bimap eta id = rho

We have formulated point-free monoidal laws for mu and eta using the fact that the underlying cartesian product itself acts like a monoidal multiplication in the category of types. Keep in mind though that the associativity and unit laws for the cartesian product are valid only up to isomorphism.

It turns out that these laws can be generalized to any category with products and a terminal object. Categorical products are indeed associative up to isomorphism and the terminal object is the unit, also up to isomorphism. The associator and the two unitors are natural isomorphisms. The laws can be represented by commuting diagrams.


Notice that, because the product is a bifunctor, it can lift a pair of morphisms — in Haskell this was done using bimap.

We could stop here and say that we can define a monoid on top of any category with categorical products and a terminal object. As long as we can pick an object m and two morphisms μ and η that satisfy monoidal laws, we have a monoid. But we can do better than that. We don’t need a full-blown categorical product to formulate the laws for μ and η. Recall that a product is defined through a universal construction that uses projections. We haven’t used any projections in our formulation of monoidal laws.

A bifunctor that behaves like a product without being a product is called a tensor product, often denoted by the infix operator ⊗. A definition of a tensor product in general is a bit tricky, but we won’t worry about it. We’ll just list its properties — the most important being associativity up to isomorphism.

Similarly, we don’t need the object t to be terminal. We never used its terminal property — namely, the existence of a unique morphism from any object to it. What we require is that it works well in concert with the tensor product. Which means that we want it to be the unit of the tensor product, again, up to isomorphism. Let’s put it all together:

A monoidal category is a category C equipped with a bifunctor called the tensor product:

⊗ :: C × C -> C

and a distinct object i called the unit object, together with three natural isomorphisms called, respectively, the associator and the left and right unitors:

αa b c :: (a ⊗ b) ⊗ c -> a ⊗ (b ⊗ c)
λa :: i ⊗ a -> a
ρa :: a ⊗ i -> a

(There is also a coherence condition for simplifying a quadruple tensor product.)

What’s important is that a tensor product describes many familiar bifunctors. In particular, it works for a product, a coproduct and, as we’ll see shortly, for the composition of endofunctors (and also for some more esoteric products like Day convolution). Monoidal categories will play an essential role in the formulation of enriched categories.

Monoid in a Monoidal Category

We are now ready to define a monoid in a more general setting of a monoidal category. We start by picking an object m. Using the tensor product we can form powers of m. The square of m is m ⊗ m. There are two ways of forming the cube of m, but they are isomorphic through the associator. Similarly for higher powers of m (that’s where we need the coherence conditions). To form a monoid we need to pick two morphisms:

μ :: m ⊗ m -> m
η :: i -> m

where i is the unit object for our tensor product.


These morphisms have to satisfy associativity and unit laws, which can be expressed in terms of the following commuting diagrams:



Notice that it’s essential that the tensor product be a bifunctor because we need to lift pairs of morphisms to form products such as μ ⊗ id or η ⊗ id. These diagrams are just a straightforward generalization of our previous results for categorical products.

Monads as Monoids

Monoidal structures pop up in unexpected places. One such place is the functor category. If you squint a little, you might be able to see functor composition as a form of multiplication. The problem is that not any two functors can be composed — the target category of one has to be the source category of the other. That’s just the usual rule of composition of morphisms — and, as we know, functors are indeed morphisms in the category Cat. But just like endomorphisms (morphisms that loop back to the same object) are always composable, so are endofunctors. For any given category C, endofunctors from C to C form the functor category [C, C]. Its objects are endofunctors, and morphisms are natural transformations between them. We can take any two objects from this category, say endofunctors F and G, and produce a third object F ∘ G — an endofunctor that’s their composition.

Is endofunctor composition a good candidate for a tensor product? First, we have to establish that it’s a bifunctor. Can it be used to lift a pair of morphisms — here, natural transformations? The signature of the analog of bimap for the tensor product would look something like this:

bimap :: (a -> b) -> (c -> d) -> (a ⊗ c -> b ⊗ d)

If you replace objects by endofunctors, arrows by natural transformations, and tensor products by composition, you get:

(F -> F') -> (G -> G') -> (F ∘ G -> F' ∘ G')

which you may recognize as the special case of horizontal composition.


We also have at our disposal the identity endofunctor I, which can serve as the identity for endofunctor composition — our new tensor product. Moreover, functor composition is associative. In fact associativity and unit laws are strict — there’s no need for the associator or the two unitors. So endofunctors form a strict monoidal category with functor composition as tensor product.

What’s a monoid in this category? It’s an object — that is an endofunctor T; and two morphisms — that is natural transformations:

μ :: T ∘ T -> T
η :: I -> T

Not only that, here are the monoid laws:



They are exactly the monad laws we’ve seen before. Now you understand the famous quote from Saunders Mac Lane:

All told, monad is just a monoid in the category of endofunctors.

You might have seen it emblazoned on some t-shirts at functional programming conferences.

Monads from Adjunctions

An adjunction, L ⊣ R, is a pair of functors going back and forth between two categories C and D. There are two ways of composing them giving rise to two endofunctors, R ∘ L and L ∘ R. As per an adjunction, these endofunctors are related to identity functors through two natural transformations called unit and counit:

η :: ID -> R ∘ L
ε :: L ∘ R -> IC

Immediately we see that the unit of an adjunction looks just like the unit of a monad. It turns out that the endofunctor R ∘ L is indeed a monad. All we need is to define the appropriate μ to go with the η. That’s a natural transformation between the square of our endofunctor and the endofunctor itself or, in terms of the adjoint functors:

R ∘ L ∘ R ∘ L -> R ∘ L

And, indeed, we can use the counit to collapse the L ∘ R in the middle. The exact formula for μ is given by the horizontal composition:

μ = R ∘ ε ∘ L

Monadic laws follow from the identities satisfied by the unit and counit of the adjunction and the interchange law.

We don’t see a lot of monads derived from adjunctions in Haskell, because an adjunction usually involves two categories. However, the definitions of an exponential, or a function object, is an exception. Here are the two endofunctors that form this adjunction:

L z = z × s
R b = s ⇒ b

You may recognize their composition as the familiar state monad:

R (L z) = s ⇒ (z × s)

We’ve seen this monad before in Haskell:

newtype State s a = State (s -> (a, s))

Let’s also translate the adjunction to Haskell. The left functor is the product functor:

newtype Prod s a = Prod (a, s)

and the right functor is the reader functor:

newtype Reader s a = Reader (s -> a)

They form the adjunction:

instance Adjunction (Prod s) (Reader s) where
  counit (Prod (Reader f, s)) = f s
  unit a = Reader (\s -> Prod (a, s))

You can easily convince yourself that the composition of the reader functor after the product functor is indeed equivalent to the state functor:

newtype State s a = State (s -> (a, s))

As expected, the unit of the adjunction is equivalent to the return function of the state monad. The counit acts by evaluating a function acting on its argument. This is recognizable as the uncurried version of the function runState:

runState :: State s a -> s -> (a, s)
runState (State f) s = f s

(uncurried, because in counit it acts on a pair).

We can now define join for the state monad as a component of the natural transformation μ. For that we need a horizontal composition of three natural transformations:

μ = R ∘ ε ∘ L

In other words, we need to sneak the counit ε across one level of the reader functor. We can’t just call fmap directly, because the compiler would pick the one for the State functor, rather than the Reader functor. But recall that fmap for the reader functor is just left function composition. So we’ll use function composition directly.

We have to first peel off the data constructor State to expose the function inside the State functor. This is done using runState:

ssa :: State s (State s a)
runState ssa :: s -> (State s a, s)

Then we left-compose it with the counit, which is defined by uncurry runState. Finally, we clothe it back in the State data constructor:

join :: State s (State s a) -> State s a
join ssa = State (uncurry runState . runState ssa)

This is indeed the implementation of join for the State monad.

It turns out that not only every adjunction gives rise to a monad, but the converse is also true: every monad can be factorized into a composition of two adjoint functors. Such factorization is not unique though.

We’ll talk about the other endofunctor L ∘ R in the next section.

Next: Comonads.

This is part 21 of Categories for Programmers. Previously: Monads: Programmer’s Definition. See the Table of Contents.

Now that we know what the monad is for — it lets us compose embellished functions — the really interesting question is why embellished functions are so important in functional programming. We’ve already seen one example, the Writer monad, where embellishment let us create and accumulate a log across multiple function calls. A problem that would otherwise be solved using impure functions (e.g., by accessing and modifying some global state) was solved with pure functions.

The Problem

Here is a short list of similar problems, copied from Eugenio Moggi’s seminal paper, all of which are traditionally solved by abandoning the purity of functions.

  • Partiality: Computations that may not terminate
  • Nondeterminism: Computations that may return many results
  • Side effects: Computations that access/modify state
    • Read-only state, or the environment
    • Write-only state, or a log
    • Read/write state
  • Exceptions: Partial functions that may fail
  • Continuations: Ability to save state of the program and then restore it on demand
  • Interactive Input
  • Interactive Output

What really is mind blowing is that all these problems may be solved using the same clever trick: turning to embellished functions. Of course, the embellishment will be totally different in each case.

You have to realize that, at this stage, there is no requirement that the embellishment be monadic. It’s only when we insist on composition — being able to decompose a single embellished function into smaller embellished functions — that we need a monad. Again, since each of the embellishments is different, monadic composition will be implemented differently, but the overall pattern is the same. It’s a very simple pattern: composition that is associative and equipped with identity.

The next section is heavy on Haskell examples. Feel free to skim or even skip it if you’re eager to get back to category theory or if you’re already familiar with Haskell’s implementation of monads.

The Solution

First, let’s analyze the way we used the Writer monad. We started with a pure function that performed a certain task — given arguments, it produced a certain output. We replaced this function with another function that embellished the original output by pairing it with a string. That was our solution to the logging problem.

We couldn’t stop there because, in general, we don’t want to deal with monolithic solutions. We needed to be able to decompose one log-producing function into smaller log-producing functions. It’s the composition of those smaller functions that led us to the concept of a monad.

What’s really amazing is that the same pattern of embellishing the function return types works for a large variety of problems that normally would require abandoning purity. Let’s go through our list and identify the embellishment that applies to each problem in turn.


We modify the return type of every function that may not terminate by turning it into a “lifted” type — a type that contains all values of the original type plus the special “bottom” value . For instance, the Bool type, as a set, would contain two elements: True and False. The lifted Bool contains three elements. Functions that return the lifted Bool may produce True or False, or execute forever.

The funny thing is that, in a lazy language like Haskell, a never-ending function may actually return a value, and this value may be passed to the next function. We call this special value the bottom. As long as this value is not explicitly needed (for instance, to be pattern matched, or produced as output), it may be passed around without stalling the execution of the program. Because every Haskell function may be potentially non-terminating, all types in Haskell are assumed to be lifted. This is why we often talk about the category Hask of Haskell (lifted) types and functions rather than the simpler Set. It is not clear, though, that Hask is a real category (see this Andrej Bauer post).


If a function can return many different results, it may as well return them all at once. Semantically, a non-deterministic function is equivalent to a function that returns a list of results. This makes a lot of sense in a lazy garbage-collected language. For instance, if all you need is one value, you can just take the head of the list, and the tail will never be evaluated. If you need a random value, use a random number generator to pick the n-th element of the list. Laziness even allows you to return an infinite list of results.

In the list monad — Haskell’s implementation of nondeterministic computations — join is implemented as concat. Remember that join is supposed to flatten a container of containers — concat concatenates a list of lists into a single list. return creates a singleton list:

instance Monad [] where
    join = concat
    return x = [x]

The bind operator for the list monad is given by the general formula: fmap followed by join which, in this case gives:

as >>= k = concat (fmap k as)

Here, the function k, which itself produces a list, is applied to every element of the list as. The result is a list of lists, which is flattened using concat.

From the programmer’s point of view, working with a list is easier than, for instance, calling a non-deterministic function in a loop, or implementing a function that returns an iterator (although, in modern C++, returning a lazy range would be almost equivalent to returning a list in Haskell).

A good example of using non-determinism creatively is in game programming. For instance, when a computer plays chess against a human, it can’t predict the opponent’s next move. It can, however, generate a list of all possible moves and analyze them one by one. Similarly, a non-deterministic parser may generate a list of all possible parses for a given expression.

Even though we may interpret functions returning lists as non-deterministic, the applications of the list monad are much wider. That’s because stitching together computations that produce lists is a perfect functional substitute for iterative constructs — loops — that are used in imperative programming. A single loop can be often rewritten using fmap that applies the body of the loop to each element of the list. The do notation in the list monad can be used to replace complex nested loops.

My favorite example is the program that generates Pythagorean triples — triples of positive integers that can form sides of right triangles.

triples = do
    z <- [1..]
    x <- [1..z]
    y <- [x..z]
    guard (x^2 + y^2 == z^2)
    return (x, y, z)

The first line tells us that z gets an element from an infinite list of positive numbers [1..]. Then x gets an element from the (finite) list [1..z] of numbers between 1 and z. Finally y gets an element from the list of numbers between x and z. We have three numbers 1 <= x <= y <= z at our disposal. The function guard takes a Bool expression and returns a list of units:

guard :: Bool -> [()]
guard True  = [()]
guard False = []

This function (which is a member of a larger class called MonadPlus) is used here to filter out non-Pythagorean triples. Indeed, if you look at the implementation of bind (or the related operator >>), you’ll notice that, when given an empty list, it produces an empty list. On the other hand, when given a non-empty list (here, the singleton list containing unit [()]), bind will call the continuation, here return (x, y, z), which produces a singleton list with a verified Pythagorean triple. All those singleton lists will be concatenated by the enclosing binds to produce the final (infinite) result. Of course, the caller of triples will never be able to consume the whole list, but that doesn’t matter, because Haskell is lazy.

The problem that normally would require a set of three nested loops has been dramatically simplified with the help of the list monad and the do notation. As if that weren’t enough, Haskell let’s you simplify this code even further using list comprehension:

triples = [(x, y, z) | z

This is just further syntactic sugar for the list monad (strictly speaking, MonadPlus).

You might see similar constructs in other functional or imperative languages under the guise of generators and coroutines.

Read-Only State

A function that has read-only access to some external state, or environment, can be always replaced by a function that takes that environment as an additional argument. A pure function (a, e) -> b (where e is the type of the environment) doesn’t look, at first sight, like a Kleisli arrow. But as soon as we curry it to a -> (e -> b) we recognize the embellishment as our old friend the reader functor:

newtype Reader e a = Reader (e -> a)

You may interpret a function returning a Reader as producing a mini-executable: an action that given an environment produces the desired result. There is a helper function runReader to execute such an action:

runReader :: Reader e a -> e -> a
runReader (Reader f) e = f e

It may produce different results for different values of the environment.

Notice that both the function returning a Reader, and the Reader action itself are pure.

To implement bind for the Reader monad, first notice that you have to produce a function that takes the environment e and produces a b:

ra >>= k = Reader (\e -> ...)

Inside the lambda, we can execute the action ra to produce an a:

ra >>= k = Reader (\e -> let a = runReader ra e
                         in ...)

We can then pass the a to the continuation k to get a new action rb:

ra >>= k = Reader (\e -> let a  = runReader ra e
                             rb = k a
                         in ...)

Finally, we can run the action rb with the environment e:

ra >>= k = Reader (\e -> let a  = runReader ra e
                             rb = k a
                         in runReader rb e)

To implement return we create an action that ignores the environment and returns the unchanged value.

Putting it all together, after a few simplifications, we get the following definition:

instance Monad (Reader e) where
    ra >>= k = Reader (\e -> runReader (k (runReader ra e)) e)
    return x = Reader (\e -> x)

Write-Only State

This is just our initial logging example. The embellishment is given by the Writer functor:

newtype Writer w a = Writer (a, w)

For completeness, there’s also a trivial helper runWriter that unpacks the data constructor:

runWriter :: Writer w a -> (a, w)
runWriter (Writer (a, w)) = (a, w)

As we’ve seen before, in order to make Writer composable, w has to be a monoid. Here’s the monad instance for Writer written in terms of the bind operator:

instance (Monoid w) => Monad (Writer w) where 
    (Writer (a, w)) >>= k = let (a', w') = runWriter (k a)
                            in Writer (a', w `mappend` w')
    return a = Writer (a, mempty)


Functions that have read/write access to state combine the embellishments of the Reader and the Writer. You may think of them as pure functions that take the state as an extra argument and produce a pair value/state as a result: (a, s) -> (b, s). After currying, we get them into the form of Kleisli arrows a -> (s -> (b, s)), with the embellishment abstracted in the State functor:

newtype State s a = State (s -> (a, s))

Again, we can look at a Kleisli arrow as returning an action, which can be executed using the helper function:

runState :: State s a -> s -> (a, s)
runState (State f) s = f s

Different initial states may not only produce different results, but also different final states.

The implementation of bind for the State monad is very similar to that of the Reader monad, except that care has to be taken to pass the correct state at each step:

sa >>= k = State (\s -> let (a, s') = runState sa s
                            sb = k a
                        in runState sb s')

Here’s the full instance:

instance Monad (State s) where
    sa >>= k = State (\s -> let (a, s') = runState sa s 
                            in runState (k a) s')
    return a = State (\s -> (a, s))

There are also two helper Kleisli arrows that may be used to manipulate the state. One of them retrieves the state for inspection:

get :: State s s
get = State (\s -> (s, s))

and the other replaces it with a completely new state:

put :: s -> State s ()
put s' = State (\s -> ((), s'))


An imperative function that throws an exception is really a partial function — it’s a function that’s not defined for some values of its arguments. The simplest implementation of exceptions in terms of pure total functions uses the Maybe functor. A partial function is extended to a total function that returns Just a whenever it makes sense, and Nothing when it doesn’t. If we want to also return some information about the cause of the failure, we can use the Either functor instead (with the first type fixed, for instance, to String).

Here’s the Monad instance for Maybe:

instance Monad Maybe where
    Nothing >>= k = Nothing
    Just a  >>= k = k a
    return a = Just a

Notice that monadic composition for Maybe correctly short-circuits the computation (the continuation k is never called) when an error is detected. That’s the behavior we expect from exceptions.


It’s the “Don’t call us, we’ll call you!” situation you may experience after a job interview. Instead of getting a direct answer, you are supposed to provide a handler, a function to be called with the result. This style of programming is especially useful when the result is not known at the time of the call because, for instance, it’s being evaluated by another thread or delivered from a remote web site. A Kleisli arrow in this case returns a function that accepts a handler, which represents “the rest of the computation”:

data Cont r a = Cont ((a -> r) -> r)

The handler a -> r, when it’s eventually called, produces the result of type r, and this result is returned at the end. A continuation is parameterized by the result type. (In practice, this is often some kind of status indicator.)

There is also a helper function for executing the action returned by the Kleisli arrow. It takes the handler and passes it to the continuation:

runCont :: Cont r a -> (a -> r) -> r
runCont (Cont k) h = k h

The composition of continuations is notoriously difficult, so its handling through a monad and, in particular, the do notation, is of extreme advantage.

Let’s figure out the implementation of bind. First let’s look at the stripped down signature:

(>>=) :: ((a -> r) -> r) -> 
         (a -> (b -> r) -> r) -> 
         ((b -> r) -> r)

Our goal is to create a function that takes the handler (b -> r) and produces the result r. So that’s our starting point:

ka >>= kab = Cont (\hb -> ...)

Inside the lambda, we want to call the function ka with the appropriate handler that represents the rest of the computation. We’ll implement this handler as a lambda:

runCont ka (\a -> ...)

In this case, the rest of the computation involves first calling kab with a, and then passing hb to the resulting action kb:

runCont ka (\a -> let kb = kab a
                  in runCont kb hb)

As you can see, continuations are composed inside out. The final handler hb is called from the innermost layer of the computation. Here’s the full instance:

instance Monad (Cont r) where
    ka >>= kab = Cont (\hb -> runCont ka (\a -> runCont (kab a) hb))
    return a = Cont (\ha -> ha a)

Interactive Input

This is the trickiest problem and a source of a lot of confusion. Clearly, a function like getChar, if it were to return a character typed at the keyboard, couldn’t be pure. But what if it returned the character inside a container? As long as there was no way of extracting the character from this container, we could claim that the function is pure. Every time you call getChar it would return exactly the same container. Conceptually, this container would contain the superposition of all possible characters.

If you’re familiar with quantum mechanics, you should have no problem understanding this analogy. It’s just like the box with the Schrödinger’s cat inside — except that there is no way to open or peek inside the box. The box is defined using the special built-in IO functor. In our example, getChar could be declared as a Kleisli arrow:

getChar :: () -> IO Char

(Actually, since a function from the unit type is equivalent to picking a value of the return type, the declaration of getChar is simplified to getChar :: IO Char.)

Being a functor, IO lets you manipulate its contents using fmap. And, as a functor, it can store the contents of any type, not just a character. The real utility of this approach comes to light when you consider that, in Haskell, IO is a monad. It means that you are able to compose Kleisli arrows that produce IO objects.

You might think that Kleisli composition would allow you to peek at the contents of the IO object (thus “collapsing the wave function,” if we were to continue the quantum analogy). Indeed, you could compose getChar with another Kleisli arrow that takes a character and, say, converts it to an integer. The catch is that this second Kleisli arrow could only return this integer as an (IO Int). Again, you’ll end up with a superposition of all possible integers. And so on. The Schrödinger’s cat is never out of the bag. Once you are inside the IO monad, there is no way out of it. There is no equivalent of runState or runReader for the IO monad. There is no runIO!

So what can you do with the result of a Kleisli arrow, the IO object, other than compose it with another Kleisli arrow? Well, you can return it from main. In Haskell, main has the signature:

main :: IO ()

and you are free to think of it as a Kleisli arrow:

main :: () -> IO ()

From that perspective, a Haskell program is just one big Kleisli arrow in the IO monad. You can compose it from smaller Kleisli arrows using monadic composition. It’s up to the runtime system to do something with the resulting IO object (also called IO action).

Notice that the arrow itself is a pure function — it’s pure functions all the way down. The dirty work is relegated to the system. When it finally executes the IO action returned from main, it does all kinds of nasty things like reading user input, modifying files, printing obnoxious messages, formatting a disk, and so on. The Haskell program never dirties its hands (well, except when it calls unsafePerformIO, but that’s a different story).

Of course, because Haskell is lazy, main returns almost immediately, and the dirty work begins right away. It’s during the execution of the IO action that the results of pure computations are requested and evaluated on demand. So, in reality, the execution of a program is an interleaving of pure (Haskell) and dirty (system) code.

There is an alternative interpretation of the IO monad that is even more bizarre but makes perfect sense as a mathematical model. It treats the whole Universe as an object in a program. Notice that, conceptually, the imperative model treats the Universe as an external global object, so procedures that perform I/O have side effects by virtue of interacting with that object. They can both read and modify the state of the Universe.

We already know how to deal with state in functional programming — we use the state monad. Unlike simple state, however, the state of the Universe cannot be easily described using standard data structures. But we don’t have to, as long as we never directly interact with it. It’s enough that we assume that there exists a type RealWorld and, by some miracle of cosmic engineering, the runtime is able to provide an object of this type. An IO action is just a function:

type IO a  =  RealWorld -> (a, RealWorld)

Or, in terms of the State monad:

type IO = State RealWorld

However, >=> and return for the IO monad have to be built into the language.

Interactive Output

The same IO monad is used to encapsulate interactive output. RealWorld is supposed to contain all output devices. You might wonder why we can’t just call output functions from Haskell and pretend that they do nothing. For instance, why do we have:

putStr :: String -> IO ()

rather than the simpler:

putStr :: String -> ()

Two reasons: Haskell is lazy, so it would never call a function whose output — here, the unit object — is not used for anything. And, even if it weren’t lazy, it could still freely change the order of such calls and thus garble the output. The only way to force sequential execution of two functions in Haskell is through data dependency. The input of one function must depend on the output of another. Having RealWorld passed between IO actions enforces sequencing.

Conceptually, in this program:

main :: IO ()
main = do
    putStr "Hello "
    putStr "World!"

the action that prints “World!” receives, as input, the Universe in which “Hello ” is already on the screen. It outputs a new Universe, with “Hello World!” on the screen.


Of course I have just scratched the surface of monadic programming. Monads not only accomplish, with pure functions, what normally is done with side effects in imperative programming, but they also do it with a high degree of control and type safety. They are not without drawbacks, though. The major complaint about monads is that they don’t easily compose with each other. Granted, you can combine most of the basic monads using the monad transformer library. It’s relatively easy to create a monad stack that combines, say, state with exceptions, but there is no formula for stacking arbitrary monads together.

Next: Monads Categorically.

This is part 20 of Categories for Programmers. Previously: Free/Forgetful Adjunctions. See the Table of Contents.

Programmers have developed a whole mythology around monads. It’s supposed to be one of the most abstract and difficult concepts in programming. There are people who “get it” and those who don’t. For many, the moment when they understand the concept of the monad is like a mystical experience. The monad abstracts the essence of so many diverse constructions that we simply don’t have a good analogy for it in everyday life. We are reduced to groping in the dark, like those blind men touching different parts of the elephant end exclaiming triumphantly: “It’s a rope,” “It’s a tree trunk,” or “It’s a burrito!”

Let me set the record straight: The whole mysticism around the monad is the result of a misunderstanding. The monad is a very simple concept. It’s the diversity of applications of the monad that causes the confusion.

As part of research for this post I looked up duct tape (a.k.a., duck tape) and its applications. Here’s a little sample of things that you can do with it:

  • sealing ducts
  • fixing CO2 scrubbers on board Apollo 13
  • wart treatment
  • fixing Apple’s iPhone 4 dropped call issue
  • making a prom dress
  • building a suspension bridge

Now imagine that you didn’t know what duct tape was and you were trying to figure it out based on this list. Good luck!

So I’d like to add one more item to the collection of “the monad is like…” clichés: The monad is like duct tape. Its applications are widely diverse, but its principle is very simple: it glues things together. More precisely, it composes things.

This partially explains the difficulties a lot of programmers, especially those coming from the imperative background, have with understanding the monad. The problem is that we are not used to thinking of programing in terms of function composition. This is understandable. We often give names to intermediate values rather than pass them directly from function to function. We also inline short segments of glue code rather than abstract them into helper functions. Here’s an imperative-style implementation of the vector-length function in C:

double vlen(double * v) {
  double d = 0.0;
  int n;
  for (n = 0; n < 3; ++n)
    d += v[n] * v[n];
  return sqrt(d);

Compare this with the (stylized) Haskell version that makes function composition explicit:

vlen = sqrt . sum . fmap  (flip (^) 2)

(Here, to make things even more cryptic, I partially applied the exponentiation operator (^) by setting its second argument to 2.)

I’m not arguing that Haskell’s point-free style is always better, just that function composition is at the bottom of everything we do in programming. And even though we are effectively composing functions, Haskell does go to great lengths to provide imperative-style syntax called the do notation for monadic composition. We’ll see its use later. But first, let me explain why we need monadic composition in the first place.

The Kleisli Category

We have previously arrived at the writer monad by embellishing regular functions. The particular embellishment was done by pairing their return values with strings or, more generally, with elements of a monoid. We can now recognize that such embellishment is a functor:

newtype Writer w a = Writer (a, w)

instance Functor (Writer w) where
  fmap f (Writer (a, w)) = Writer (f a, w)

We have subsequently found a way of composing embellished functions, or Kleisli arrows, which are functions of the form:

a -> Writer w b

It was inside the composition that we implemented the accumulation of the log.

We are now ready for a more general definition of the Kleisli category. We start with a category C and an endofunctor m. The corresponding Kleisli category K has the same objects as C, but its morphisms are different. A morphism between two objects a and b in K is implemented as a morphism:

a -> m b

in the original category C. It’s important to keep in mind that we treat a Kleisli arrow in K as a morphism between a and b, and not between a and m b.

In our example, m was specialized to Writer w, for some fixed monoid w.

Kleisli arrows form a category only if we can define proper composition for them. If there is a composition, which is associative and has an identity arrow for every object, then the functor m is called a monad, and the resulting category is called the Kleisli category.

In Haskell, Kleisli composition is defined using the fish operator >=>, and the identity arrrow is a polymorphic function called return. Here’s the definition of a monad using Kleisli composition:

class Monad m where
  (>=>) :: (a -> m b) -> (b -> m c) -> (a -> m c)
  return :: a -> m a

Keep in mind that there are many equivalent ways of defining a monad, and that this is not the primary one in the Haskell ecosystem. I like it for its conceptual simplicity and the intuition it provides, but there are other definitions that are more convenient when programming. We’ll talk about them momentarily.

In this formulation, monad laws are very easy to express. They cannot be enforced in Haskell, but they can be used for equational reasoning. They are simply the standard composition laws for the Kleisli category:

(f >=> g) >=> h = f >=> (g >=> h) -- associativity
return >=> f = f                  -- left unit
f >=> return = f                  -- right unit

This kind of a definition also expresses what a monad really is: it’s a way of composing embellished functions. It’s not about side effects or state. It’s about composition. As we’ll see later, embellished functions may be used to express a variety of effects or state, but that’s not what the monad is for. The monad is the sticky duct tape that ties one end of an embellished function to the other end of an embellished function.

Going back to our Writer example: The logging functions (the Kleisli arrows for the Writer functor) form a category because Writer is a monad:

instance Monoid w => Monad (Writer w) where
    f >=> g = \a -> 
        let Writer (b, s)  = f a
            Writer (c, s') = g b
        in Writer (c, s `mappend` s')
    return a = Writer (a, mempty)

Monad laws for Writer w are satisfied as long as monoid laws for w are satisfied (they can’t be enforced in Haskell either).

There’s a useful Kleisli arrow defined for the Writer monad called tell. It’s sole purpose is to add its argument to the log:

tell :: w -> Writer w ()
tell s = Writer ((), s)

We’ll use it later as a building block for other monadic functions.

Fish Anatomy

When implementing the fish operator for different monads you quickly realize that a lot of code is repeated and can be easily factored out. To begin with, the Kleisli composition of two functions must return a function, so its implementation may as well start with a lambda taking an argument of type a:

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

The only thing we can do with this argument is to pass it to f:

f >=> g = \a -> let mb = f a
                in ...

At this point we have to produce the result of type m c, having at our disposal an object of type m b and a function g :: b -> m c. Let’s define a function that does that for us. This function is called bind and is usually written in the form of an infix operator:

(>>=) :: m a -> (a -> m b) -> m b

For every monad, instead of defining the fish operator, we may instead define bind. In fact the standard Haskell definition of a monad uses bind:

class Monad m where
    (>>=) :: m a -> (a -> m b) -> m b
    return :: a -> m a

Here’s the definition of bind for the Writer monad:

(Writer (a, w)) >>= f = let Writer (b, w') = f a
                        in  Writer (b, w `mappend` w')

It is indeed shorter than the definition of the fish operator.

It’s possible to further dissect bind, taking advantage of the fact that m is a functor. We can use fmap to apply the function a -> m b to the contents of m a. This will turn a into m b. The result of the application is therefore of type m (m b). This is not exactly what we want — we need the result of type m b — but we’re close. All we need is a function that collapses or flattens the double application of m. Such function is called join:

join :: m (m a) -> m a

Using join, we can rewrite bind as:

ma >>= f = join (fmap f ma)

That leads us to the third option for defining a monad:

class Functor m => Monad m where
    join :: m (m a) -> m a
    return :: a -> m a

Here we have explicitly requested that m be a Functor. We didn’t have to do that in the previous two definitions of the monad. That’s because any type constructor m that either supports the fish or bind operator is automatically a functor. For instance, it’s possible to define fmap in terms of bind and return:

fmap f ma = ma >>= \a -> return (f a)

For completeness, here’s join for the Writer monad:

join :: Monoid w => Writer w (Writer w a) -> Writer w a
join (Writer ((Writer (a, w')), w)) = Writer (a, w `mappend` w')

The do Notation

One way of writing code using monads is to work with Kleisli arrows — composing them using the fish operator. This mode of programming is the generalization of the point-free style. Point-free code is compact and often quite elegant. In general, though, it can be hard to understand, bordering on cryptic. That’s why most programmers prefer to give names to function arguments and intermediate values.

When dealing with monads it means favoring the bind operator over the fish operator. Bind takes a monadic value and returns a monadic value. The programmer may chose to give names to those values. But that’s hardly an improvement. What we really want is to pretend that we are dealing with regular values, not the monadic containers that encapsulate them. That’s how imperative code works — side effects, such as updating a global log, are mostly hidden from view. And that’s what the do notation emulates in Haskell.

You might be wondering then, why use monads at all? If we want to make side effects invisible, why not stick to an imperative language? The answer is that the monad gives us much better control over side effects. For instance, the log in the Writer monad is passed from function to function and is never exposed globally. There is no possibility of garbling the log or creating a data race. Also, monadic code is clearly demarcated and cordoned off from the rest of the program.

The do notation is just syntactic sugar for monadic composition. On the surface, it looks a lot like imperative code, but it translates directly to a sequence of binds and lambda expressions.

For instance, take the example we used previously to illustrate the composition of Kleisli arrows in the Writer monad. Using our current definitions, it could be rewritten as:

process :: String -> Writer String [String]
process = upCase >=> toWords

This function turns all characters in the input string to upper case and splits it into words, all the while producing a log of its actions.

In the do notation it would look like this:

process s = do
    upStr <- upCase s
    toWords upStr

Here, upStr is just a String, even though upCase produces a Writer:

upCase :: String -> Writer String String
upCase s = Writer (map toUpper s, "upCase ")

This is because the do block is desugared by the compiler to:

process s = 
   upCase s >>= \ upStr ->
       toWords upStr

The monadic result of upCase is bound to a lambda that takes a String. It’s the name of this string that shows up in the do block. When reading the line:

upStr <- upCase s

we say that upStr gets the result of upCase s.

The pseudo-imperative style is even more pronounced when we inline toWords. We replace it with the call to tell, which logs the string "toWords ", followed by the call to return with the result of splitting the string upStr using words. Notice that words is a regular function working on strings.

process s = do
    upStr <- upStr s
    tell "toWords "
    return (words upStr)

Here, each line in the do block introduces a new nested bind in the desugared code:

process s = 
    upCase s >>= \upStr ->
      tell "toWords " >>= \() ->
        return (words upStr)

Notice that tell produces a unit value, so it doesn’t have to be passed to the following lambda. Ignoring the contents of a monadic result (but not its effect — here, the contribution to the log) is quite common, so there is a special operator to replace bind in that case:

(>>) :: m a -> m b -> m b
m >> k = m >>= (\_ -> k)

The actual desugaring of our code looks like this:

process s = 
    upCase s >>= \upStr ->
      tell "toWords " >>
        return (words upStr)

In general, do blocks consist of lines (or sub-blocks) that either use the left arrow to introduce new names that are then available in the rest of the code, or are executed purely for side-effects. Bind operators are implicit between the lines of code. Incidentally, it is possible, in Haskell, to replace the formatting in the do blocks with braces and semicolons. This provides the justification for describing the monad as a way of overloading the semicolon.

Notice that the nesting of lambdas and bind operators when desugaring the do notation has the effect of influencing the execution of the rest of the do block based on the result of each line. This property can be used to introduce complex control structures, for instance to simulate exceptions.

Interestingly, the equivalent of the do notation has found its application in imperative languages, C++ in particular. I’m talking about resumable functions or coroutines. It’s not a secret that C++ futures form a monad. It’s an example of the continuation monad, which we’ll discuss shortly. The problem with continuations is that they are very hard to compose. In Haskell, we use the do notation to turn the spaghetti of “my handler will call your handler” into something that looks very much like sequential code. Resumable functions make the same transformation possible in C++. And the same mechanism can be applied to turn the spaghetti of nested loops into list comprehensions or “generators,” which are essentially the do notation for the list monad. Without the unifying abstraction of the monad, each of these problems is typically addressed by providing custom extensions to the language. In Haskell, this is all dealt with through libraries.

Next: Monads and Effects.

In the previous post I explored the application of the Yoneda lemma in the functor category to derive some results from the Haskell lens library. In particular I derived the profunctor representation of isos. There is one more trick that is used in the lens library: combining the Yoneda lemma with adjunctions. Jaskelioff and O’Connor used this trick in the context of free/forgetful adjunctions, but it can be easily generalized to any pair of adjoint higher order functors.


An adjunction between two functors, L and R (left and right functor) is a natural isomorphism between hom-sets:

C(L d, c) ≅ D(d, R c)

The left functor L goes from the category D to C, and the right functor R goes in the opposite direction. Formally, having an adjunction allows us to shift the action of the functor from one end of the hom-set to the other. The shortcut notation for an adjunction is L ⊣ R.

Since adjunctions can be defined for arbitrary categories, they will also work between functor categories. In that case objects are functors and hom-sets are sets of natural transformations. For instance, Let’s consider an adjunction between two higher order functors:

ρ :: [C, C'] -> [D, D']
λ :: [D, D'] -> [C, C']

Here, [C, C'] is a category of functors between two categories C and C’, [D, D'] is a category of functors between D and D’, and ρ maps functors (and natural transformations) between these two categories. λ goes in the opposite direction. The adjunction λ ⊣ ρ is expressed as a natural isomorphism between sets of natural transformations:

[C, C'](λ g, h)  ≅  [D, D'](g, ρ h)

The two objects in functor categories are themselves functors:

h :: C -> C'
g :: D -> D'

Here’s the same adjunction written using ends:

x∈C C'((λ g) x, h x)  ≅  ∫y∈D D'(g y, (ρ h) y)

The end notation is easily translatable to Haskell. The end corresponds to a universal quantifier forall, and hom-sets become function types:

forall x. (lambda g) x -> h x ≅ forall y. g y -> (rho h) y

Since lambda and rho act on functors, they have kinds (*->*)->(*->*).

Yoneda with Adjunctions

Let’s recall the formula for the Yoneda embedding of the functor category:

f Set(∫x D(g x, f x), ∫y D(h y, f y))
  ≅ ∫z D(h z, g z)

Here, g, h, and f, are functors — objects in the functor category [C, D]. The ends represent natural transformations — morphisms in the functor category. The end over f is a higher order natural transformation.

Since g and h are arbitrary, let’s replace them with the results of the action of some higher order functors, λ g and λ' h. The idea is that λ and λ' are left halves of some higher order adjunctions.

f Set(∫x D'((λ g) x, f x), ∫y D'((λ' h) y, f y))
  ≅ ∫z D'((λ' h) z, (λ g) z)

The right halves of these adjunctions are, respectively, ρ and ρ'.

λ  ⊣ ρ
λ' ⊣ ρ'

Let’s apply these adjunctions inside the hom-sets:

f Set(∫x D(g x, (ρ f) x), ∫y D(h y, (ρ' f) y))
  ≅ ∫z D(h z, (ρ' (λ g)) z)

Let’s focus our attention on the category of sets. If we replace D with Set, we can pick g and h to be hom-functors (which are the simplest representable functors) parameterized by some arbitrary objects b and t:

g = C(b, -)
h = C(t, -)

We get:

f Set(∫x Set(C(b, x), (ρ f) x), ∫y Set(C(t, y), (ρ' f) y)
  ≅ ∫z Set(C(t, z), (ρ' (λ C(b, -))) z)

Remember, hom-functors behave like Dirac delta functions under the integration sign. That is to say, we can use the Yoneda lemma to “integrate” over x, y, and z:

f Set((ρ f) b, (ρ' f) t)
  ≅ (ρ' (λ C(b, -))) t

We are now free to pick a pair of adjoint higher order functors to suit our goal. Here’s one such choice for ρ: the functor that maps a functor f (an endofunctor in C) to a hom-functor in the Kleisli category. This higher-order functor is parameterized by the choice of the object a in C:

κa f = C(a, f -)

It can also be written in terms of the exponential object:

κa f = (f -)a

This functor has an obvious left adjoint:

λa g = a × g -

This follows from the standard adjunction between the product and the exponential.

Our pick for ρ' is the same Kleisli functor but taken at a different point, s:

ρ' = κs

With those choices, the left side of the identity

f Set((ρ f) b, (ρ' f) t)
  ≅ (ρ' (λ C(b, -))) t


f Set(C(a, f b), C(s, f t))

This is the categorical version of the van Laarhoven lens.

Let’s now evaluate the right hand side. First we apply λa to the hom-functor C(b, -) to get:

λa C(b, -) = a × C(b, -)

The action of ρ' produces the result:

C(s, (a × C(b, t)))

This, in turn, is the categorical version of the getter/setter representation of the lens.


In Haskell, our formula derived from the higher-order Yoneda lemma with the adjoint pair:

f Set((ρ f) b, (ρ' f) t)
  ≅ (ρ' (λ C(b, -))) t

takes the form:

forall f. Functor f => (rho f) b -> (rho' f) t 
  ≅ (rho' (lambda ((->)b))) t

With our choice for ρ as the Kleisli functor:

rho  f = a -> f -
rho' f = s -> f -

or, in proper Haskell:

type Rho  a f b = a -> f b
type Rho' s f t = s -> f t

we get:

forall f. Functor f => (a -> f b) -> (s -> f t) 
  ≅ (rho' (lambda ((->)b))) t

To get the λ, we plug our ρ into the adjunction formula. We get:

forall x. (lambda g) x -> h x ≅ forall x. g x -> a -> h x

which has the obvious solution:

lambda g = (a, g -)

or, in proper Haskell,

type Lambda a g x = (a, g x)

Indeed, with the currying and flipping of arguments, we get the adjunction:

forall x. (a, g x) -> h x ≅ forall x. g x -> a -> h x

Now let’s evaluate the right hand side:

(rho' (lambda ((->) b))) t

We start with:

lambda (b -> -) = (a, b -> -)

The action of rho' gives us:

rho' (a, b -> -) = s -> (a, b -> -)


(rho' (lambda ((->) b))) t = s -> (a, b -> t)

So the right hand side is just the getter/setter pair:

(s -> a, s -> b -> t)

The final result is the well known van Laarhoven representation of the lens:

forall f. Functor f => (a -> f b) -> (s -> f t) 
  ≅ (s -> a, s -> b -> t)

This is not a new result, but I like the elegance of this derivation — especially the role played by the exponential adjunction in the Kleisli category. This formulation has the additional advantage of being generalizable towards the profunctor formulation of lenses.

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