Whilst I can think of other ways to do it I don’t think they would be conceptually or syntactically any simpler. GraphX doesn’t have the concept of built-in vertex properties which would make this simpler - a vertex in GraphX is a Vertex ID (Long) and a bunch of custom attributes that you assign. This means you have to find a way of ‘pushing’ the vertex degree into the graph so you can do comparisons (cf a join in relational databases) or as you have done create a list and filter against that (cf filtering against a sub-query in relational database). 

One thing I would point out is that you probably want to avoid finalVerexes.collect() for a large-scale system - this will pull all the vertices into the driver and then push them out to the executors again as part of the filter operation. A better strategy for large graphs would be:

1. build a graph based on the existing graph where the vertex attribute is the vertex degree - the GraphX documentation shows how to do this
2. filter this “degrees” graph to just give you 0 degree vertices
3 use graph.mask passing in the 0-degree graph to get the original graph with just 0 degree vertices

Just one variation on several possibilities, the key point is that everything is just a graph transformation until you call an action on the resulting graph
-------------------------------------------------------------------------------
Robin East
Spark GraphX in Action Michael Malak and Robin East
Manning Publications Co.
http://www.manning.com/books/spark-graphx-in-action





On 26 Feb 2016, at 11:59, Guillermo Ortiz <konstt2000@gmail.com> wrote:

I'm new with graphX. I need to get the vertex without out edges..
I guess that it's pretty easy but I did it pretty complicated.. and inefficienct 

val vertices: RDD[(VertexId, (List[String], List[String]))] =
sc.parallelize(Array((1L, (List("a"), List[String]())),
(2L, (List("b"), List[String]())),
(3L, (List("c"), List[String]())),
(4L, (List("d"), List[String]())),
(5L, (List("e"), List[String]())),
(6L, (List("f"), List[String]()))))

// Create an RDD for edges
val relationships: RDD[Edge[Boolean]] =
sc.parallelize(Array(Edge(1L, 2L, true), Edge(2L, 3L, true), Edge(3L, 4L, true), Edge(5L, 2L, true)))
val out = minGraph.outDegrees.map(vertex => vertex._1)
val finalVertexes = minGraph.vertices.keys.subtract(out)
//It must be something better than this way..
val nodes = finalVertexes.collect()
val result = minGraph.vertices.filter(v => nodes.contains(v._1))

What's the good way to do this operation? It seems that it should be pretty easy.