The out-degree explains how many vertices are going out. This time, we'll be calculating the sources of our edges, relationships, and not destinations, like we did in the in-degree method.

To get the out-degree, we will use the following code:

val degrees = graph.outDegrees.collect().toList

The outDegrees method contains both RDD and VertexRDD, which we have collected to a list using the collect and toList methods.

Here, VertexId 1L should have two outbound vertices because there is a relationship between 1L, 2L and 1L, 3L:

  test("should calculate out-degree of vertices") {
    //given
    val users: RDD[(VertexId, (String))] =
      spark.parallelize(Array(
        (1L, "a"),
        (2L, "b"),
        (3L, "c"),
        (4L, "d")
      ))


    val relationships =
      spark.parallelize(Array(
        Edge(1L, 2L, "friend"),
        Edge(1L, 3L, "friend"),
        Edge(2L, 4L, "wife")
      ))

    val graph = Graph(users, relationships)

    //when
    val degrees = graph.outDegrees.collect().toList

    //then
    degrees should contain theSameElementsAs List(
      (1L, 2L),
      (2L, 1L)
    )
  }

}

Also, VertexId 2L should have one outbound vertex as there is a relationship between 2L and 4L and not the other way around, as shown in the preceding code.

We will run this test and get the following output: