Found the exact issue. If the vertex attribute is a complex object with
mutable objects the edge triplet does not update the new state once already
the vertex attributes are shipped but if the vertex attributes are immutable
objects then there is no issue. below is a code for the same. Just
Created a JIRA for the same
https://issues.apache.org/jira/browse/SPARK-18568
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Hi
I am facing a similar issue. It's not that the message is getting lost or
something. The vertex 1 attributes changes in super step 1 but when the
sendMsg gets the vertex attribute from the edge triplet in the 2nd superstep
it stills has the old value of vertex 1 and not the latest value. So
How to do graph partition in GraphFrames similar to the partitionBy feature
in GraphX? Can we use the Dataframe's repartition feature in 1.6 to provide
a graph partitioning in graphFrames?
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Hi
I have a scenario where in the graph I am doing graph.vertices.collect() and
getting the 5 vertex i added each of my vertex is an scala object as shown
below
class NodeExact(nodeId: Long, summ: Array[collection.mutable.Map[Long,
Long]]) extends Serializable {
var node: Long = nodeId
var
Hi
I am also working on the same area where the graph evolves over time and the
current approach of rebuilding the graph again and again is very slow and
memory consuming did you find any workaround?
What was your usecase?
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