# Notes on Types

## Type Systems

A good type system can eliminate entire categories of errors in a program and simply make invalid code not compile. Before digging into types, below are a few common distinctions between the type systems of various programming languages:

### Dynamic Typing vs Static Typing

In a statically typed language, the types are determined at compile time so if a function was declared as only accepting a certain type T but we attempt to pass it an unrelated type U, the program is considered invalid. This is invalid C++:

void sqr(int x)
{
return x * x;
}
...
sqr("not an int"); // does not compile


On the other hand, in a dynamically typed language, we do not perform any compile time checks and, if the data we get is of an unexpected type, we treat it as a runtime error. Below is a Python function that squares a number:

def sqr(x):
return x ** 2
...
sqr("not an int")
# runs but fails with TypeError: unsupported operand type(s)
# for ** or pow(): 'str' and 'int'


Some of the interesting features of dynamic languages are duck typing (“if it walks like a duck, and it quacks like a duck…”) and monkey patching. Duck typing means that accessing a member of an object works as long as that object has such a member, regardless of the type of the object. In Python:

class Foo:
def func(self):
print("foo")

class Bar:
def func(self):
print("bar")

[obj.func() for obj in [Foo(), Bar()]] # prints "foo" and "bar"


This can’t work in a statically typed language where, at the bare minimum, we would have to add some form of constraint for the types in the list to ensure they contain a func() method we can call.

Monkey patching refers to the ability to change the structure of an object at runtime. For example we can swap the func method from an instance of foo with another function like this:

class Foo:
def func(self):
print("foo")

def func_bar():
print("bar")

obj = Foo()
obj.func() # prints "foo"
obj.func = func_bar
obj.func() # prints "bar"


These are useful capabilities, but the tradeoff is a whole class of type errors which a statically typed language would’ve caught.

As a side note, the fact that dynamic languages don’t need to specify types makes them more terse. That being said, the Hindley-Milner algorithm W can infer the types of a program in linear time with respect to the source size. So while Python is starting to support type annotations for better static analysis and TypeScript provides a way for writing type-safe JavaScript, C++ has better and better type inference, while in Haskell (which has one of the strongest static type systems) type annotations are mostly optional.

### Strong Typing vs Weak Typing

At a high level, a strongly typed language does not implicitly convert between unrelated types. This is good in most situations as implicit conversions are often meaningless or have surprising effects - for example, adding a number to a list of characters. This can either result in runtime errors or garbage data. In contrast, a strongly typed language will not accept code that attempts to do this.

In Python, which is strongly typed, this doesn’t work:

foo = "foo" # foo is "foo"
foo = foo + " bar" # foo is "foo bar"
foo = foo + 5 # TypeError: Can't convert 'int' object to str implicitly


It works just fine in JavaScript though:

var foo = "foo"; // foo is "foo"
foo = foo + " bar"; // foo is "foo bar"
foo = foo + 5; // foo is "foo bar5"


Note type strength is not an either/or - C++, while considered strongly typed, still allows several implicit casts between types (eg. pointer to bool). Some languages are more strict about converting between types implicitly, others less so.

### Dynamic Polymorphism vs Static Polymorphism

Another difference to note is between static and dynamic polymorphism. Dynamic polymorphism happens at runtime, when calling a function on a base type gets resolved to the actual function of the deriving type:

struct base
{
virtual void func() = 0;
};

struct foo : base
{
void func() override
{
std::cout << "foo" << std::endl;
}
};

struct bar : base
{
void func() override
{
std::cout << "bar" << std::endl;
}
};

void call_func(base& obj)
{
obj.func();
}
...
call_func(foo{}); // prints "foo"
call_func(bar{}); // prints "bar"


In the above case, we effectively have a single function call_func which takes a reference to a base struct. The compiler generates a v-table for struct base and a call to func() on base involves a v-table jump to the actual implementation of the function, which is different between the inheriting types foo and bar.

Contrast this with the static alternative:

struct foo
{
void func()
{
std::cout << "foo" << std::endl;
}
};

struct bar
{
void func()
{
std::cout << "bar" << std::endl;
}
};

template <typename T> void call_func(T& obj)
{
obj.func();
}
...
call_func(foo{}); // prints "foo"
call_func(bar{}); // prints "bar"


In this case there is no relationship between foo and bar and no v-table is needed. On the other hand, we no longer have a single call_func, we have a templated function which is instantiated for both foo and bar types. This is all done at compile-time, the advantage being faster code, the drawback being compiler needs to be aware of all the types involved - we can no longer “inject” types implementing an interface at runtime. When calling call_func, we need to have both the definition of the function and the declaration of the type we’re passing in visible.

## Types

During the rest of this post, I will talk about types in the context of a statically and strongly typed language, with a focus on static polymorphism. This pushes as much as possible of the type checking to the compilation stage, so many of the runtime issues of less strict languages become invalid syntax.

I will focus on C++ and cover some of the new C++17 feature which enable or make some of these concepts easier to work with. That being said, since this post focuses on types, I will also provide examples in Haskell, as Haskell can express these concepts much more succinctly.

### Type Basics

Let’s start with the definition of a type: a type represents the set of possible values. For example, the C++ type uint8_t represents the set of integers from 0 to 255. Effectively this means that a variable of a given type can only have values from within that set.

### Interesting Types

Since we defined a type as a set of possible values, we can talk about the cardinality of a type, in other words the number of values in the set. Based on cardinality, there are a few interesting classes of types to talk about:

### Empty Type

The first interesting type to talk about is the type that represents the empty set, with |T| = 0.

In Haskell, this type is named Void. Since Haskell is a functional language, all functions must return a value, so it does not make sense to have a function that returns Void - the same way it doesn’t make sense to define a mathematical function with the empty set as its codomain. We do have an absurd function though, which maps the empty set to any value:

absurd :: Void -> a


This function cannot be called though.

In C++, the absence of a value is represented as the void type. Since C++ is not purely functional, we can define functions that don’t return anything. We can even say that a function does not take any arguments by putting a void between the parenthesis:

void foo(void);


This is the equivalent of:

void foo();


Note though that we cannot have a real argument of type void, that is a compile error as it doesn’t make any sense - we would be mandating the function takes a value from the empty set. So we can say foo(void) but not foo(void arg), or even foo(int arg, void).

### Unit Type

The next interesting class consists of types with cardinality 1. A type T with |T| = 1 is called a unit or singleton type. A variable of such a type can only ever have a single possible value. In Haskell, the anonymous representation is the empty tuple (). Here is an example of a function that maps anything to this type:

unit :: a -> ()
unit _ = ()


Of course, we can declare our own singleton types. Below is a custom Singleton type and an equivalent unit function:

data Singleton = Singleton
unit :: a -> Singleton
unit _ = Singleton


In C++, the anonymous representation of a singleton is an empty std::tuple:

template <typename T> std::tuple<> unit(T)
{
return { };
}


As can be seen from the above, Haskell makes it easier to define a function that takes an argument of any type, as it provides syntactic sugar for type parameters (a in our example). In C++, the equivalent declaration involves a template, but they boil down to the same thing. The non-anonymous C++ representation is a struct which doesn’t contain anything. All instances of such a struct are equivalent:

struct singleton { };

template <typename T> singleton unit(T)
{
return { };
}


### Sum Types

Here, things get a bit more interesting: a sum type is a type which can represent a value from any of the types it sums. So given type A and type B, the type S summing up A and B is S = { i : i A U B }. So a variable of type S could have any value in A or any value in B. S is called a sum type because its cardinality is the sum of the cardinalities of A and B, |S| = |A| + |B|.

Sum types are great, because they allow us to build up more complex types from simpler ones. Once we have unit types, we can build up more complex types out of them by summing them. For example, a boolean type which can be either true or false can be thought of as the sum of the singleton true type and the singleton false type. In Haskell, a boolean is defined as:

data Bool = True | False


Similarly, a Weekday type can be defined as:

data Weekday = Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday


Theoretically, numerical types could also be defined as huge sum types of every possible value they can represent. Of course, this is impractical, but we can reason about them the same way we reason about other sum types, we don’t have to treat them as a special case.

In C++, an equivalent of the above is an enum class. bool is a built-in type with special syntax, but we could define an equivalent as:

enum class Boolean
{
True,
False
};


It’s easy to see how a Weekday definition would look like. Things get more interesting when we throw type parameters into the mix. In Haskell, we have the Either type, which is declared as follows:

data Either a b = Left a | Right b


An instance of this could be either a Left a, where a is a type itself, which means it can be any of the values of a, or it can be Right b, with any of the values of b. In Haskell we use pattern-matching to operate on such a type, so we can declare a simple function that tells us whether we were given a Left a like this:

isLeft :: Either a b -> Bool
isLeft (Left a) = True
isLeft (Right b) = False


This might not look like much, but of course we can compose more complex functions. For example, say we have a function foo that takes an a and returns an a, a function bar that takes a b and returns a b. We can then write a transform function which takes an Either a b and, depending on the contained type, it applies the appropriate function:

-- Implementation of foo and bar not provided in this example
foo :: a -> a
bar :: b -> b

transform :: Either a b -> Either a b
transform (Left a) = Left (foo a)
transform (Right b) = Right (bar b)


This is way beyond the capabilities of a C++ enum class. The old way of implementing something like this in C++ was using a union and a tag enum to keep track of which is the actual type we’re working with:

// Declaration of A and B not provided in this example
struct A;
struct B;

struct Either
{
Either(A a)
{
ab.left = a;
tag = tag::isA;
}

Either(B b)
{
ab.right = b;
tag = tag::isB;
}

union
{
A left;
B right;
} ab;

enum class tag
{
isA,
isB
} tag;
};


Our implementation of transform would look like this:

// Implementation of A and B not provided in this example
A foo(A);
B bar(B);

Either transform(Either either)
{
switch (either.tag)
{
case Either::tag::isA:
return foo(either.ab.left);
case Either::tag::isB:
return bar(either.ab.right);
}
}


Our Either type definition is obviously much more verbose than what we had in Haskell, and it doesn’t even support type parameters - at this point it only works with struct A and struct B, while the Haskell version works for any a and b types. The other major problem is that, while unions provide efficient storage for different types (the size of the union is the size of the maximum contained type), it is up to the implementer to make sure we don’t try to read an A as a B or vice-versa. That means we need to keep our tag in sync with what we put in the type and respect it when accessing the value of the union.

C++17 introduces a better, safer, parameterized type for this: std::variant. Variant takes any number of types as template arguments and stores an instance of any one of those types. Using variant, we can re-write the above as:

std::variant<A, B> transform(std::variant<A, B> either)
{
return std::visit([](auto e&&) {
if constexpr (std::is_same_v<decltype(e), A>)
return foo(std::get<A>(e));
else
return bar(std::get<B>(e));
}, either);
}


This is a lot of new syntax, so let’s break it down: std::variant<A, B> is the new C++17 sum type. In this case, we specify it holds either A or B (but it can hold an arbitrary number of types).

std::visit is a function that applies the visitor function given as its first argument to the variants given as its subsequent arguments. In our example, this effectively expands to applying the lambda to std::get<0>(either) and std::get<1>(either).

if constexpr is also a new C++17 construct which evaluates the if expression at compile time and discards the else branch from the final object code. So in this example, we determine at compile time whether the type we are being called with is A or B and apply the correct function based on that. Something very similar can be achieved with templates and enable_if, but this syntax makes for more readable code.

Note that with this version we can simply prepend a template <typename A, typename B> and make the whole function generic, as in the Haskell example. It doesn’t read as pretty (because we don’t have good pattern matching syntax in the language), but this is the new, type safe way of implementing and working with sum types, which is a major improvement.

### Product Types

With sum types out of the way, the remaining interesting category is that of product types. Product types combine the values of several other types into one. For types A and B, we have P = { (a, b) : a A, b B }, so |P| = |A| x |B|.

In Haskell, the anonymous version of product types is represented by tuples, while the named version is represented by records. An example of a perimeter function which computes the perimeter of a rectangle defined by two points, where each point is a tuple of numbers:

perimeter :: (Num n) => (n, n) -> (n, n) -> n
perimeter (x1, y1) (x2, y2) = 2 * (abs(x1 - x2) + abs(y1 - y2))


The named version would declare a Point type with Int coordinates and use that instead:

data Point = Point { x, y :: Int }

perimeter :: Point -> Point -> Int
perimeter (Point x1 y1) (Point x2 y2) = 2 * (abs(x1 - x2) + abs(y1 - y2))


The C++ equivalents are std::tuple for anonymous product types and struct for named types:

// Anonymous
int perimeter(std::tuple<int, int> p1, std::tuple<int, int> p2)
{
return 2 * ((abs(std::get<0>(p1) - std::get<0>(p2))
+ abs(std::get<1>(p1) - std::get<1>(p2)));
}

// Named
struct point
{
int x;
int y;
};

int perimeter(point p1, point p2)
{
return 2 * (abs(p1.x - p2.x) + abs(p1.y - p2.y));
}


While sum types allow us to express values from multiple types into one, product types allow us to express values from several types together. Empty, unit, sum, and product types are the building blocks of a type system.

### Bonus: Optional Types

An optional type is a type that can hold any of the values of another type, or not hold any value, which is usually represented as a singleton. So an optional is effectively a sum type between a given type and a singleton representing “doesn’t hold a value”. In other words, the cardinality of an optional for a type T is |O| = |T| + 1.

In Haskell, an optional is the famous Maybe type:

data Maybe a = Just a | Nothing


A function that operates on Maybe could say “only apply foo if the optional contains an a”:

-- Implementation of foo not provided in this example
foo :: a -> a

transform :: Maybe a -> Maybe a
transform (Just a) = Just (foo a)
transform Nothing = Nothing


The new C++17 equivalent is the optional type:

// Implementation of foo not provided in this example
A foo(A);

std::optional<A> transform(std::optional<A> a)
{
return a != nullopt ? foo(*a) : nullopt;
}


This might read a bit like the pointer implementation:

A* transform(A* a)
{
return a != nullptr ? foo(*a) : nullptr;
}


There is a key difference though: the type contained in the optional is part of the object, so it is not allocated dynamically the way we would allocate a pointer. nullopt is a helper object of the singleton type nullopt_t.

Types are important because a big part of programming effectively consits of designing and composing types. Having a good understanding of the fundamentals leads to better, safer, and saner code.

## Summary

We started by outlining some of the basic principles of type systems:

• Static and dynamic typing
• Weak and strong typing
• Static and dynamic polymorphism

Then we went over the building block types of a type system, with Haskell and C++ examples:

• Empty types (cardinality 0)
• Unit types (cardinality 1)
• Sum types (S of A and B has cardinality A + B)
• Product types (P of A and B has cardinality A x B)
• Optional types (O of A has cardinality A + 1)