And one more thing, the given tupes
(1, 1.0)
(2, 1.0)
(3, 2.0)
(4, 2.0)
(5, 0.0)

are a part of RDD and they are not just tuples.
graph.vertices return me the above tuples which is a part of VertexRDD.


On Wed, Dec 3, 2014 at 3:43 PM, Deep Pradhan <pradhandeep1991@gmail.com> wrote:
This is just an example but if my graph is big, there will be so many tuples to handle. I cannot manually do 
val a: RDD[(Int, Double)] = sc.parallelize(List(
      (1, 1.0),
      (2, 1.0),
      (3, 2.0),
      (4, 2.0),
      (5, 0.0)))
for all the vertices in the graph.
What should I do in that case?
We cannot do sc.parallelize(List(VertexRDD)), can we?

On Wed, Dec 3, 2014 at 3:32 PM, Ankur Dave <ankurdave@gmail.com> wrote:
At 2014-12-02 22:01:20 -0800, Deep Pradhan <pradhandeep1991@gmail.com> wrote:
> I have a graph which returns the following on doing graph.vertices
> (1, 1.0)
> (2, 1.0)
> (3, 2.0)
> (4, 2.0)
> (5, 0.0)
>
> I want to group all the vertices with the same attribute together, like into
> one RDD or something. I want all the vertices with same attribute to be
> together.

You can do this by flipping the tuples so the values become the keys, then using one of the by-key functions in PairRDDFunctions:

    val a: RDD[(Int, Double)] = sc.parallelize(List(
      (1, 1.0),
      (2, 1.0),
      (3, 2.0),
      (4, 2.0),
      (5, 0.0)))

    val b: RDD[(Double, Int)] = a.map(kv => (kv._2, kv._1))

    val c: RDD[(Double, Iterable[Int])] = b.groupByKey(numPartitions = 5)

    c.collect.foreach(println)
    // (0.0,CompactBuffer(5))
    // (1.0,CompactBuffer(1, 2))
    // (2.0,CompactBuffer(3, 4))

Ankur