The Monad is like a bellows:
it is empty yet infinitely capable.
The more you use it, the more it produces;
the more you talk about it, the less you understand.

–Monad Te Ching

I don’t know if I’m exaggerating but it seems like every programmer who gets monads posts a tutorial about them. (And each post begins with: There’s already a lot of monad tutorials on the Internet, but…) The reason is that getting monads it’s like a spiritual experience that you want to share with others.

When facing a monad, people often behave like the three blind men describing an elephant. You’ll see monads described as containers and monads described as actions. Some people see them as a cover-up for side effects, others as examples of endofunctors in Category Theory.

Monads are hard to describe because they don’t correspond to anything in our everyday experience. Compare this with Objects in Object-Oriented programming. Even an infant knows what an object is (something you can put in your mouth). What do you do with a monad?

But first, let me answer the pertinent question:

Why Bother?

Monads enable pure functional programmers to implement mutation, state, I/O, and a plethora of other things that are not functions. Well, you might say, they brought it on themselves. They tied their hands behind their backs and now they’re bragging that they can type with their toes. Why should we pay attention?

The thing is, all those non-functional things that we are so used to do in imperative programming are also sources of a lot of troubles. Take side effects for instance. Smart programmers (read: the ones who burnt their fingers too many times) try to minimize the use of global and static variables for fear of side effects. That’s doable if you know what you’re doing. But the real game changer is multithreading. Controlling the sharing of state between threads is not just good programming practice– it’s a survival skill. Extreme programming models are in use that eliminate sharing altogether, like Erlang’s full isolation of processes and its restriction of message passing to values.

Monads stake the ground between total anarchy of imperative languages and the rigid dictatorship of Erlang-like isolationism. They don’t prohibit sharing or side effects but let you control them. And, since the control is exercised through the type system, a program that uses monads can be checked for correctness by the compiler. Considering how hard it it to test for data races in imperative programs, I think it’s worth investing some time to learn monads.

There is also a completely different motivation: metaprogramming. The template language used for metaprogramming in C++ is a pure functional language (see my blog post, What does Haskell have to do with C++?). If monads are so important in functional programming, they must also pop up in C++ metaprogramming. And indeed they do. I hope to discuss this topic in a future post.

So what’s a monad?

A Categorical Answer

If you don’t know anything about category theory, don’t get intimidated. This is really simple stuff and it will clarify a lot of things, not to mention earning you some bragging rights. My main goal is to share some intuitions from mathematics that will build foundations for a deeper understanding of monads in programming. In this installment I will explain categories, functors, and endofunctors, leading up to monads. I will give examples taken both from everyday life and from programming. I will really get into monads and their practical applications in the next installment, so be patient.


A category is a natural extension of our notion of sets and functions. The generalization of a set in a category is called an object (a pretty neutral term with little semantic ballast), and the generalization of a function is called a morphism. In fact, the standard example of a category is the category of sets and functions called (capital letter) Set.

A morphism (read “function”) goes from one object (read “set”) to another. Mathematical functions like sin or exp usually go from the set of real numbers to the set of real numbers. But you may also define functions like isPrime that go from natural numbers to Booleans, or a function price that goes from a set of goods to the set of numbers.

The only thing a mathematician needs to know about morphisms is that they can be composed. If you have a morphism from A to B, A->B, and another going from B to C, B->C, then they can be composed to a morphism from A to C, A->C. And just like the standard composition of functions, morphism composition must be associative, so we don’t need parentheses when composing more than two of them.

Actually, two things. There must be, for every object, a special morphism called identity that essentially does nothing and when composed with any other morphism reproduces the same morphism.

Just to throw you off the track, a category doesn’t have to be built on sets and functions. You can easily construct simple categories from blobs and arrows. Fig 1 shows such a category that contains two objects and four morphisms: arrows between them (formally, those arrows are ordered pairs of objects so, for instance, f is a pair (A, B)). You can easily check that any two morphisms can be composed and that the two moprphisms iA and iB serve as identities.

Fig 1. A simple category with two objects and four morphisms.

That’s it! Hopefully I have just convinced you that a category is not a big deal. But let’s get down to Earth. The one category that’s really important in programming languages is the category of types and functions, in particular its Haskell version called Hask. There usually is a finite set of basic types like integers or Booleans, and an infinite set of derived types, like lists of integers, functions from integers to Booleans, etc. In Hask, a type is just a set of values. For instance, the type Char is a set {‘a’, ‘b’, ‘c’, … }.

So, in the category Hask, types are objects and functions are morphisms. Indeed, a function maps one type into another (forget for a moment functions of multiple arguments– they can be modeled with currying– and polymorphic functions– they are families of functions). And these are functions in the functional-programming sense: called with the same values they return the same values–no side effects allowed.

Function composition is just passing the result of one function as an argument to another. The identity function takes x and immediately returns it back.

This is all fine, but what’s in it for me, you might ask. So here’s the first insight and a moment of Zen. If there is one thing that you can call the essence of programming, it’s composability. In any style of programming you always compose your program from smaller pieces, and those pieces from even smaller pieces, and so on. That’s why categories with their composable morphisms are so important. The essence of Lego blocks is the way they fit together, their composability, not the color or size. The essence of functional programming is how functions work together: how you can build larger functions from smaller ones.

Every category is defined by its choice of objects and morphisms. But is there something that can characterize a given category that’s independent of its choice of particular objects and morphisms? How do you expose the inner structure of a particular category? Mathematicians know exactly how to do that. You have to be able to map categories into other categories while preserving some constraints imposed by the way morphisms are attached to objects and the way they compose. Such maps let you find similarities between categories and catalog different kinds of categories. That’s when things get really interesting.


A functor, F, is a map from one category to another: it maps objects into objects and morphisms into morphisms. But it can’t do it in a haphazard way because that would destroy the very structures that we are after. So we must impose some “obvious” (mathematicians love that word) constraints.

First of all, if you have a morphism between two objects in the first category then it better be mapped into a morphism between the corresponding objects in the second category. Fig 2 explains this diagrammatically. Object A is mapped into F(A), object B into F(B). A morphism f from A to B is mapped into a morphism F(f) from F(A) to F(B). Mathematicians say that such diagram must commute, that is the result must be the same whether you go from A to F(A) and then apply F(f), or first apply f and then go from B to F(B).

Functor diagram

Fig 2. Diagram showing the action of a functor F on objects A and B and a morphism f. The bottom part lives in F's domain (source) category, the top part in its codomain (the target).

Moreover, such mapping should preserve the composition property of morphisms. So if morphism h is a composition of f and g, then F(h) must be a composition of F(f) and F(g). And, of course, the functor must map identity morphisms into identity morphisms.

To get a feel for how constrained functors are by these conditions, consider how you could map the category in Fig 1 into itself (such a functor just rearranges things inside one category). There are two trivial mappings that collapse both objects into one (either A or B), and turn all morphisms into identity. Then there is the identity functor that maps both objects into themselves and all morphisms into themselves. Finally, there is just one “interesting” functor that maps A into B and B into A with f and g switching roles. Now imagine a similar category but with the g arrow removed (yes, it’s still a category). Suddenly there is no functor other than the collapsing ones between Fig 1 and that new category. That’s because the two categories have completely different structure.

Let me now jump into more familiar territory. Since we are mostly interested in one category, Hask, let me define a functor that maps that category into itself (such functors are called endofunctors). An object in Hask is a type, so our functor must map types into types. The way to look at it is that a functor in Hask constructs one type from another– it’s a type constructor. Don’t get confused by the name: a type constructor creates a new type in your program, but that type has already existed in Hask.

A classical example is the list type constructor. Given any type it constructs a list of that type. Type Integer is mapped into list of integers or, in Haskell notation, [Integer]. Notice that this is not a map defined on integer values, like 1, 2, or 3. It also doesn’t add a new type to Hask– the type [Integer] is already there. It just maps one type into another. For C++ programmers: think of mapping type T into a container of T; for instance, std::vector<T>.

Mapping the types is the easy part, what about functions? We have to find a way to take a particular function and map it into a function on lists. That’s also easy: apply the function to each element of the list in turn. There is a (higher level) function in Haskel that does it. It’s called map and it takes a function and a list and returns a new list (or, because of currying, you may say that it takes a function and returns a function acting on lists). In C++ there is a corresponding template function called std::transform (well, it takes two iterators and a function object, but the idea is the same).

Mathematicians often use diagrams to illustrate the properties of morphisms and functors (see Fig 2). The arrows for morphisms are usually horizontal, while the arrows for functors are vertical (going up). That’s why the mapping of morphisms under a functor is often called lifting. You can take a function operating on integers and “lift it” (using a functor) to a function operating on lists of integers, and so on.

The list functor obviously preserves function composition and identity (I’ll leave it as an easy but instructive exercise for the reader).

And now for another moment of Zen. What’s the second most important property of programming? Reusability! Look what we have just done: We took all the functions we’ve implemented so far and lifted them to the level of lists. We’ve got functions operating on lists essentially for free (well, we’ve got a small but important subset of those functions). And the same trick may be applied to all kinds of containers, arrays, trees, queues, unique_ptrs and more.

It’s all beautiful, but you don’t really need category theory to apply functions to lists. Still it’s always good to see patterns in programming, and this one is definitely a keeper. The real revolution starts with monads. And, guess what, the list functor is actually a monad. You just need a few more ingredients.

What’s the intuition behind the statement that mappings expose the structure of the system? Consider the schematic of the London underground in Fig 3. It’s just a bunch of circles and lines. It’s only relevant because there is a mapping between the city of London and this schematic. The circles correspond to tube stations and the lines to train connections. Most importantly, if trains run between two stations, the corresponding circles in the diagram are connected by lines and vice versa: these are the constraints that the mapping preserves. The schematic shows a certain structure that exists in London (mostly hidden underground) which is made apparent by the mapping.

Fig 3. The schematic map of London underground system.

Interestingly, what I’m doing here is also mapping: London and the underground map correspond to two categories. Trains stations/circles are objects and train connections/lines are morphism. How’s that for an example?


Mathematicians love mappings that preserve “obvious” constraints. As I explained, such mappings abstract inner structures away from the details of implementation. But you can also learn a lot about structure by studying non-trivial mappings into itself. Functors that map a category into itself are called endofunctors (like endo-scopes they let you look inside things). If functors expose similarities, endofunctors expose self-similarities. Take one look at the fractal fern, Fig 4, and you’ll understand how powerful self-similarity can be.

Fractal Fern

Fig 4. This fractal fern was generated using just four endomorphisms.

With a little bit of imagination you can see the list functor exposing fern-like structures inside Hask (Fig 5). Chars fan out into lists of Chars, which then fan out into lists of lists of Chars, and so on, ad infinitum. Horizontal structures described by functions from Char to Bool are reflected at higher and higher levels as functions on lists, lists of lists, etc.

Fig 5. The action of the list type constructor reveals fractal-like structure inside Hask. The functor lifts things up, the functions act horizontally.

A C++ template that takes a type parameter could be considered a type constructor. How likely is it that it also defines a functor (loosely speaking– C++ is not as mathematized as Haskell)? You have to ask yourself: Is the type parameter constrained in any way? It’s often hard to say, because type constraints are implicit in the body of a template and are tested only during instantiation. For instance, the type parameter for a std::vector must be copyable. That eliminates, for instance, classes that have private or deleted (in C++0x) copy constructors. This is not a problem though, because copyable types form a subcategory (I’m speaking really loosely now). The important thing is that a vector of copyable is itself copyable, so the “endo-” part of the endomorphism holds. In general you want to be able to feed the type created by the type constructor back to the type constructor, as in std::vector<std::vector<Foo>>. And, of course, you have to be able to lift functions in a generic way too, as in std::transform.


Ooh, Monads!
–Haskell Simpson

It’s time to finally lift the veil. I’ll start with the definition of a monad that builds on the previous sections and is mostly used by mathematicians. There is another one that’s less intuitive but easier to use in programming. I’ll leave that one for later.

A monad is an endofunctor together with two special families of morphisms, both going vertically, one up and one down (for “directions” see Fig 5). The one going up is called unit and the one going down is called join.

Now we are juggling a lot of mappings so let’s slow down to build some intuition. Remember, a functor maps objects: in our case, types, which are sets of values. The functor doesn’t see what’s inside the objects; morphisms, in general, do. In our case, a morphism is a function that maps values of one type into values of another type. Our functors, which are defined by type constructors, usually map poorer types into richer types; in the sense that type Bool is a set that contains just two elements, True and False, but type [Bool] contains infinitely many lists of True and False.

Unit takes a value from the poorer type, then picks one value from the richer type, and pronounces the two roughly equivalent. Such a rough equivalent of True from the Bool object is the one-element list [True] from the [Bool] object. Similarly, unit would map False into [False]. It would also map integer 5 into [5] and so on.

Unit can be though of as immersing values from a lower level into the higher level in the most natural way possible. By the way, in programming we call a family of functions defined for any type a polymorphic function. In C++, we would express unit as a template, like this:

template<class T>
std::vector<T> unit(T value) {
    std::vector<T> vec;
    return vec;

To explain join, imagine the functor acting twice. For instance, from a given type T the list functor will first construct the type [T] (list of T), and then [[T]] (list of list of T). Join removes one layer of “listiness” by joining the sub-lists. Plainly speaking, it just concatenates the inner lists. Given, for instance, [[a, b], [c], [d, e]], it produces [a, b, c, d, e]. It’s a many-to-one mapping from the richer type to the poorer type and the type-parameterized family of joins also forms a polymorphic function (a template, in C++).

There are a few monadic axioms that define the properties of unit and join (for instance that unit and join cancel each other), but I’m not going to elaborate on them. The important part is that the existence of unit and join imposes new constraints on the endofunctor and thus exposes even more structure.

Mathematicians look at join as the grandfather of all multiplication with unit being its neutral element. It’s heaven for mathematicians because multiplication leads to algebraic structures and indeed monads are great for constructing algebras and finding their hidden properties.

Unlike mathematicians, we programmers are not that interested in algebraic structures. So there must be something else that makes monads such a hit. As I mentioned in the beginning, in programming we often face problems that don’t naturally translate into functional paradigm. There are some types of computations that are best expressed in imperative style. It doesn’t mean they can’t be translated into functions, it’s just that the translation is somewhat awkward and tedious. Monads provide an elegant tool to do this translation. Monads made possible the absorption and assimilation of imperative programming into functional programming, so much so that some people claim (tongue in cheek?) that Haskell is the best imperative language. And like all things functional monads are bound to turn around and find their place in imperative programming. But that’s material for my next blog post.


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