Polymorphic Types

Remember the definition of IntSet (in section Object Oriented Programming):

abstract class IntSet {
  def incl(x: Int): IntSet
  def contains(x: Int): Boolean

Type Parameters

It seems too narrow to define only sets with Int elements.

We'd need another class hierarchy for Double lists, and so on, one for each possible element type.

We can generalize the definition using a type parameter:

abstract class Set[A] {
  def incl(a: A): Set[A]
  def contains(a: A): Boolean
class Empty[A] extends Set[A] {
class NonEmpty[A](elem: A, left: Set[A], right: Set[A]) extends Set[A] {

Type parameters are written in square brackets, e.g. [A].

Generic Functions

Like classes, functions can have type parameters.

For instance, here is a function that creates a set consisting of a single element.

def singleton[A](elem: A) = new NonEmpty[A](elem, new Empty[A], new Empty[A])

We can then write:


Type Inference

In fact, the Scala compiler can usually deduce the correct type parameters from the value arguments of a function call.

So, in most cases, type parameters can be left out. You could also write:


Types and Evaluation

Type parameters do not affect evaluation in Scala.

We can assume that all type parameters and type arguments are removed before evaluating the program.

This is also called type erasure.

Languages that use type erasure include Java, Scala, Haskell, ML, OCaml.

Some other languages keep the type parameters around at run time, these include C++, C#, F#.


Polymorphism means that a function type comes "in many forms".

In programming it means that

  • the function can be applied to arguments of many types, or
  • the type can have instances of many types.

We have seen two principal forms of polymorphism:

  • subtyping: instances of a subclass can be passed to a base class
  • generics: instances of a function or class are created by type parameterization.

The remaining subsections compare their interaction.

Consider the following class hierarchy:

trait Animal {
  def fitness: Int

trait Reptile extends Animal

trait Mammal extends Animal

trait Zebra extends Mammal {
  def zebraCount: Int

trait Giraffe extends Mammal

Type Bounds

Consider the method selection that takes two animals as parameters and returns the one with the highest fitness value:

What would be the best type you can give to selection? Maybe:

def selection(a1: Animal, a2: Animal): Animal

In most situations this is fine, but can one be more precise?

One might want to express that selection takes Zebras to Zebras and Reptiles to Reptiles.

Upper Bounds

A way to express this is:

def selection[A <: Animal](a1: A, a2: A): A =
  if (a1.fitness > a2.fitness) a1 else a2

Here, “<: Animal” is an upper bound of the type parameter A.

It means that A can be instantiated only to types that conform to Animal.

Generally, the notation

  • A <: B means: A is a subtype of B, and
  • A >: B means: A is a supertype of B, or B is a subtype of A.

Lower Bounds

You can also use a lower bound for a type variable.

A >: Reptile

The type parameter A that can range only over supertypes of Reptile.

So A could be one of Reptile, Animal, AnyRef, or Any.

(We will see later on in this section where lower bounds are useful).

Mixed Bounds

Finally, it is also possible to mix a lower bound with an upper bound.

For instance,

A >: Zebra <: Animal

would restrict A any type on the interval between Zebra and Animal.


There's another interaction between subtyping and type parameters we need to consider.

Consider the following type modeling a field containing an animal:

trait Field[A] {
  def get: A // returns the animal that lives in this field


Zebra <: Mammal


Field[Zebra] <: Field[Mammal]


Intuitively, this makes sense: a field containing a zebra is a special case of a field containing an arbitrary mammal.

We call types for which this relationship holds covariant because their subtyping relationship varies with the type parameter.

Does covariance make sense for all types, not just for Field?


For perspective, let's look at arrays in Java (and C#).


  • An array of T elements is written T[] in Java.
  • In Scala we use parameterized type syntax Array[T] to refer to the same type.

Arrays in Java are covariant, so one would have:

Zebra[] <: Mammal[]

But covariant array typing causes problems.

To see why, consider the Java code below:

Zebra[] zebras = new Zebra[]{ new Zebra() }  // Array containing 1 `Zebra`
Mammal[] mammals = zebras      // Allowed because arrays are covariant in Java
mammals[0] = new Giraffe()     // Allowed because a `Giraffe` is a subtype of `Mammal`
Zebra zebra = zebras[0]        // Get the first `Zebra` … which is actually a `Giraffe`!

It looks like we assigned in the last line a Giraffe to a variable of type Zebra!

What went wrong?

The Liskov Substitution Principle

The following principle, stated by Barbara Liskov, tells us when a type can be a subtype of another.

If A <: B, then everything one can to do with a value of type B one should also be able to do with a value of type A.

The problematic array example would be written as follows in Scala:

val zebras: Array[Zebra] = Array(new Zebra)
val mammals: Array[Mammal] = zebras
mammals(0) = new Giraffe
val zebra: Zebra = zebras(0)

If you try to compile this example you will get a compile error at line 2:

type mismatch;
found: Array[Zebra]
required: Array[Mammal]


We have seen that some types should be covariant whereas others should not.

Roughly speaking, a type that accepts mutations of its elements should not be covariant.

But immutable types can be covariant, if some conditions on methods are met.

Definition of Variance

Say C[T] is a parameterized type and A, B are types such that A <: B.

In general, there are three possible relationships between C[A] and C[B]:

  • C[A] <: C[B], C is covariant,
  • C[A] >: C[B], C is contravariant,
  • neither C[A] nor C[B] is a subtype of the other, C is nonvariant.

Scala lets you declare the variance of a type by annotating the type parameter:

  • class C[+A] { … }, C is covariant,
  • class C[-A] { … }, C is contravariant,
  • class C[A] { … }, C is nonvariant.

Typing Rules for Functions

Generally, we have the following rule for subtyping between function types:

If A2 <: A1 and B1 <: B2, then

A1 => B1 <: A2 => B2

So functions are contravariant in their argument type(s) and covariant in their result type.

This leads to the following revised definition of the Function1 trait:

trait Function1[-T, +U] {
  def apply(x: T): U

Contravariance Example

Consider the following type modeling a veterinary:

trait Vet[A] {
  def treat(a: A): Unit // Treats an animal of type `A`

In such a case, intuitively, it makes sense to have Vet[Mammal] <: Vet[Zebra] because a vet that can treat any mammal is able to treat a zebra in particular. This is an example of a contravariant type.

Variance Checks

We have seen in the array example that the combination of covariance with certain operations is unsound.

In the case of Array, the problematic combination is:

  • the covariant type parameter T
  • which appears in parameter position of the method update.

The Scala compiler will check that there are no problematic combinations when compiling a class with variance annotations.


  • covariant type parameters can only appear in method results.
  • contravariant type parameters can only appear in method parameters.
  • invariant type parameters can appear anywhere.

The precise rules are a bit more involved, fortunately the Scala compiler performs them for us.

Variance-Checking the Function Trait

Let's have a look again at Function1:

trait Function1[-T, +U] {
  def apply(x: T): U


  • T is contravariant and appears only as a method parameter type
  • U is covariant and appears only as a method result type

So the method is checks out OK.

Making Classes Covariant

Sometimes, we have to put in a bit of work to make a class covariant.

Consider adding a prepend method to Stream which prepends a given element, yielding a new stream.

A first implementation of prepend could look like this:

trait Stream[+T] {
  def prepend(elem: T): Stream[T] = Stream.cons(elem, this)

But that does not work!

Why does the above code not type-check?

prepend fails variance checking.

Indeed, the compiler is right to throw out Stream with prepend, because it violates the Liskov Substitution Principle:

Here's something one can do with a stream mammals of type Stream[Mammal]:

mammals.prepend(new Giraffe)

But the same operation on a list zebras of type Stream[Zebra] would lead to a type error:

zebras.prepend(new Giraffe)
               ^ type mismatch
               required: Zebra
               found: Giraffe

So, Stream[Zebra] cannot be a subtype of Stream[Mammal].

But prepend is a natural method to have on immutable lists!

How can we make it variance-correct?

We can use a lower bound:

def prepend[U >: T](elem: U): Stream[U] = Stream.cons(elem, this)

This passes variance checks, because:

  • covariant type parameters may appear in lower bounds of method type parameters
  • contravariant type parameters may appear in upper bounds of method type parameters


Complete the following implementation of the size function that returns the size of a given list.

def size[A](xs: List[A]): Int =
  xs match {
    case Nil => res0
    case y :: ys => res1 + size(ys)
size(Nil) shouldBe 0
size(List(1, 2)) shouldBe 2
size(List("a", "b", "c")) shouldBe 3