Scala 3 *Opaque type aliases* provide type abstractions without any **overhead**.

## Abstraction Overhead

Let us assume we want to define a module that offers arithmetic on numbers, which are represented by their logarithm. This can be useful to improve precision when the numerical values involved tend to be very large, or close to zero.

Since it is important to distinguish “regular” double values from numbers stored as their logarithm, we introduce a class `Logarithm`

:

```
class Logarithm(protected val underlying: Double):
def toDouble: Double = math.exp(underlying)
def + (that: Logarithm): Logarithm =
// here we use the apply method on the companion
Logarithm(this.toDouble + that.toDouble)
def * (that: Logarithm): Logarithm =
new Logarithm(this.underlying + that.underlying)
object Logarithm:
def apply(d: Double): Logarithm = new Logarithm(math.log(d))
```

The apply method on the companion object lets us create values of type `Logarithm`

which we can use as follows:

```
val l2 = Logarithm(2.0)
val l3 = Logarithm(3.0)
println((l2 * l3).toDouble) // prints 6.0
println((l2 + l3).toDouble) // prints 4.999...
```

While the class `Logarithm`

offers a nice abstraction for `Double`

values that are stored in this particular logarithmic form, it imposes severe performance overhead: For every single mathematical operation, we need to extract the underlying value and then wrap it again in a new instance of `Logarithm`

.

## Module Abstractions

Let us consider another approach to implement the same library.
This time instead of defining `Logarithm`

as a class, we define it using a *type alias*.
First, we define an abstract interface of our module:

```
trait Logarithms:
type Logarithm
// operations on Logarithm
def add(x: Logarithm, y: Logarithm): Logarithm
def mul(x: Logarithm, y: Logarithm): Logarithm
// functions to convert between Double and Logarithm
def make(d: Double): Logarithm
def extract(x: Logarithm): Double
// extension methods to use `add` and `mul` as "methods" on Logarithm
extension (x: Logarithm)
def toDouble: Double = extract(x)
def + (y: Logarithm): Logarithm = add(x, y)
def * (y: Logarithm): Logarithm = mul(x, y)
```

Now, let us implement this abstract interface by saying type `Logarithm`

is equal to `Double`

:

```
object LogarithmsImpl extends Logarithms:
type Logarithm = Double
// operations on Logarithm
def add(x: Logarithm, y: Logarithm): Logarithm = make(x.toDouble + y.toDouble)
def mul(x: Logarithm, y: Logarithm): Logarithm = x + y
// functions to convert between Double and Logarithm
def make(d: Double): Logarithm = math.log(d)
def extract(x: Logarithm): Double = math.exp(x)
```

Within the implementation of `LogarithmsImpl`

, the equation `Logarithm = Double`

allows us to implement the various methods.

#### Leaky Abstractions

However, this abstraction is slightly leaky.
We have to make sure to *only* ever program against the abstract interface `Logarithms`

and never directly use `LogarithmsImpl`

.
Directly using `LogarithmsImpl`

would make the equality `Logarithm = Double`

visible for the user, who might accidentally use a `Double`

where a logarithmic double is expected.
For example:

```
import LogarithmsImpl._
val l: Logarithm = make(1.0)
val d: Double = l // type checks AND leaks the equality!
```

Having to separate the module into an abstract interface and implementation can be useful, but is also a lot of effort, just to hide the implementation detail of `Logarithm`

.
Programming against the abstract module `Logarithms`

can be very tedious and often requires the use of advanced features like path-dependent types, as in the following example:

```
def someComputation(L: Logarithms)(init: L.Logarithm): L.Logarithm = ...
```

#### Boxing Overhead

Type abstractions, such as `type Logarithm`

erase to their bound (which is `Any`

in our case).
That is, although we do not need to manually wrap and unwrap the `Double`

value, there will be still some boxing overhead related to boxing the primitive type `Double`

.

## Opaque Types

Instead of manually splitting our `Logarithms`

component into an abstract part and into a concrete implementation, we can simply use opaque types in Scala 3 to achieve a similar effect:

```
object Logarithms:
//vvvvvv this is the important difference!
opaque type Logarithm = Double
object Logarithm:
def apply(d: Double): Logarithm = math.log(d)
extension (x: Logarithm)
def toDouble: Double = math.exp(x)
def + (y: Logarithm): Logarithm = Logarithm(math.exp(x) + math.exp(y))
def * (y: Logarithm): Logarithm = x + y
```

The fact that `Logarithm`

is the same as `Double`

is only known in the scope where `Logarithm`

is defined, which in the above example corresponds to the object `Logarithms`

.
The type equality `Logarithm = Double`

can be used to implement the methods (like `*`

and `toDouble`

).

However, outside of the module the type `Logarithm`

is completely encapsulated, or “opaque.” To users of `Logarithm`

it is not possible to discover that `Logarithm`

is actually implemented as a `Double`

:

```
import Logarithms._
val l2 = Logarithm(2.0)
val l3 = Logarithm(3.0)
println((l2 * l3).toDouble) // prints 6.0
println((l2 + l3).toDouble) // prints 4.999...
val d: Double = l2 // ERROR: Found Logarithm required Double
```

Even though we abstracted over `Logarithm`

, the abstraction comes for free:
Since there is only one implementation, at runtime there will be *no boxing overhead* for primitive types like `Double`

.

### Summary of Opaque Types

Opaque types offer a sound abstraction over implementation details, without imposing performance overhead. As illustrated above, opaque types are convenient to use, and integrate very well with the Extension Methods feature.