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 pradhandeep1...@gmail.com
wrote:
This is just
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
At 2014-12-03 02:13:49 -0800, Deep Pradhan pradhandeep1...@gmail.com wrote:
We cannot do sc.parallelize(List(VertexRDD)), can we?
There's no need to do this, because every VertexRDD is also a pair RDD:
class VertexRDD[VD] extends RDD[(VertexId, VD)]
You can simply use graph.vertices in
At 2014-12-02 22:01:20 -0800, Deep Pradhan pradhandeep1...@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
To get that function in scope you have to import
org.apache.spark.SparkContext._
Ankur
On Wednesday, December 3, 2014, Deep Pradhan pradhandeep1...@gmail.com
wrote:
But groupByKey() gives me the error saying that it is not a member of
org.apache.spark,rdd,RDD[(Double,
