list.groupBy(_.property).map(_._2.head)
Explanation: The groupBy method accepts a function that converts an element to a key for grouping. _.property
is just shorthand for elem: Object => elem.property
(the compiler generates a unique name, something like x$1
). So now we have a map Map[Property, List[Object]]
. A Map[K,V]
extends Traversable[(K,V)]
. So it can be traversed like a list, but elements are a tuple. This is similar to Java's Map#entrySet()
. The map method creates a new collection by iterating each element and applying a function to it. In this case the function is _._2.head
which is shorthand for elem: (Property, List[Object]) => elem._2.head
. _2
is just a method of Tuple that returns the second element. The second element is List[Object] and head
returns the first element
To get the result to be a type you want:
import collection.breakOut
val l2: List[Object] = list.groupBy(_.property).map(_._2.head)(breakOut)
To explain briefly, map
actually expects two arguments, a function and an object that is used to construct the result. In the first code snippet you don't see the second value because it is marked as implicit and so provided by the compiler from a list of predefined values in scope. The result is usually obtained from the mapped container. This is usually a good thing. map on List will return List, map on Array will return Array etc. In this case however, we want to express the container we want as result. This is where the breakOut method is used. It constructs a builder (the thing that builds results) by only looking at the desired result type. It is a generic method and the compiler infers its generic types because we explicitly typed l2 to be List[Object]
or, to preserve order (assuming Object#property
is of type Property
):
list.foldRight((List[Object](), Set[Property]())) {
case (o, cum@(objects, props)) =>
if (props(o.property)) cum else (o :: objects, props + o.property))
}._1
foldRight
is a method that accepts an initial result and a function that accepts an element and returns an updated result. The method iterates each element, updating the result according to applying the function to each element and returning the final result. We go from right to left (rather than left to right with foldLeft
) because we are prepending to objects
- this is O(1), but appending is O(N). Also observe the good styling here, we are using a pattern match to extract the elements.
In this case, the initial result is a pair (tuple) of an empty list and a set. The list is the result we're interested in and the set is used to keep track of what properties we already encountered. In each iteration we check if the set props
already contains the property (in Scala, obj(x)
is translated to obj.apply(x)
. In Set
, the method apply
is def apply(a: A): Boolean
. That is, accepts an element and returns true / false if it exists or not). If the property exists (already encountered), the result is returned as-is. Otherwise the result is updated to contain the object (o :: objects
) and the property is recorded (props + o.property
)
Update: @andreypopp wanted a generic method:
import scala.collection.IterableLike
import scala.collection.generic.CanBuildFrom
class RichCollection[A, Repr](xs: IterableLike[A, Repr]){
def distinctBy[B, That](f: A => B)(implicit cbf: CanBuildFrom[Repr, A, That]) = {
val builder = cbf(xs.repr)
val i = xs.iterator
var set = Set[B]()
while (i.hasNext) {
val o = i.next
val b = f(o)
if (!set(b)) {
set += b
builder += o
}
}
builder.result
}
}
implicit def toRich[A, Repr](xs: IterableLike[A, Repr]) = new RichCollection(xs)
to use:
scala> list.distinctBy(_.property)
res7: List[Obj] = List(Obj(1), Obj(2), Obj(3))
Also note that this is pretty efficient as we are using a builder. If you have really large lists, you may want to use a mutable HashSet instead of a regular set and benchmark the performance.