graphx - trianglecount of 2B edges

[vinodma]

I was attempting to use the graphx triangle count method on a 2B edge graph
(Friendster dataset on SNAP)   and running into out of memory issue. I have
access to a 60 node cluster with 90GB memory and 30v cores per node .

 I am using 1000 partitions and using the RandomVertexCut. Here’s my submit
script :

spark-submit --executor-cores 5 --num-executors 100 --executor-memory 32g
--driver-memory 6g --conf spark.yarn.executor.memoryOverhead=8000  --conf
"spark.executor.extraJavaOptions=-XX:-UseGCOverheadLimit” 
trianglecount_2.10-1.0.jar

There was one particular stage where it shuffled 3.7 TB

Active Stages (1)

Stage IdDescription Submitted   DurationTasks: 
Succeeded/Total  Input   Output
Shuffle ReadShuffle Write
11  (kill)mapPartitions at VertexRDDImpl.scala:218+details
2015/11/12 01:33:06 7.3 min 
316/344
22.6 GB 57.0 GB 3.7 TB
In this subsequent stage it fails reading the Shuffle around the half
terabyte mark with a java.lang.OutOfMemoryError: Java heap space


Active Stages (1)

Stage IdDescription Submitted   DurationTasks: 
Succeeded/Total  Input   Output
Shuffle ReadShuffle Write
12  (kill)mapPartitions at GraphImpl.scala:235+details
2015/11/12 01:41:25 5.2 min 
0/1000
26.3 GB 533.8 GB




Compared to the spark benchmarking (http://arxiv.org/pdf/1402.2394v1.pdf)
cluster used on the twitter dataset (2.5B edges) the resources i am
providing for the job seem to be reasonable. Can anyone point out any
optimization or other tweaks i need to perform to get this to work ?

Thanks!
Vinod



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graphx - trianglecount of 2B edges

[Vinod Mangipudi]

I was attempting to use the graphx triangle count method on a 2B edge graph 
(Friendster dataset on SNAP)  . I have access to a 60 node cluster with 90GB 
memory and 30v cores per node .
I am running into memory issues


 I am using 1000 partitions using the RandomVertexCut. Here’s my submit script :

spark-submit --executor-cores 5 --num-executors 100 --executor-memory 32g 
--driver-memory 6g --conf spark.yarn.executor.memoryOverhead=8000  --conf 
"spark.executor.extraJavaOptions=-XX:-UseGCOverheadLimit”  
trianglecount_2.10-1.0.jar

There was one particular stage where it shuffled 3.7 TB

Active Stages (1)

Stage IdDescription Submitted   DurationTasks: 
Succeeded/Total  Input   Output  Shuffle ReadShuffle Write
11  (kill 
)mapPartitions
 at VertexRDDImpl.scala:218 
+details
 

 

  2015/11/12 01:33:06 7.3 min 
316/344
22.6 GB 57.0 GB 3.7 TB
In this subsequent stage it fails reading the Shuffle around the half terabyte 
mark with a java.lang.OutOfMemoryError: Java heap space


Active Stages (1)

Stage IdDescription Submitted   DurationTasks: 
Succeeded/Total  Input   Output  Shuffle ReadShuffle Write
12  (kill 
)mapPartitions
 at GraphImpl.scala:235 
+details
2015/11/12 01:41:25 5.2 min 
0/1000
26.3 GB 533.8 GB




Compared to the benchmarking (http://arxiv.org/pdf/1402.2394v1.pdf 
) cluster used on the twitter dataset 
(2.5B edges) the resources i am providing for the job seem to be reasonable. 
Can anyone point out any optimization or other tweaks i need to perform to get 
this to work ?

Thanks!
Vinod