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From mvle <m...@us.ibm.com>
Subject Data locality running Spark on Mesos
Date Thu, 08 Jan 2015 19:44:58 GMT
Hi,

I've noticed running Spark apps on Mesos is significantly slower compared to
stand-alone or Spark on YARN.
I don't think it should be the case, so I am posting the problem here in
case someone has some explanation
or can point me to some configuration options i've missed.

I'm running the LinearRegression benchmark with a dataset of 48.8GB.
On a 10-node stand-alone Spark cluster (each node 4-core, 8GB of RAM),
I can finish the workload in about 5min (I don't remember exactly).
The data is loaded into HDFS spanning the same 10-node cluster.
There are 6 worker instances per node.

However, when running the same workload on the same cluster but now with
Spark on Mesos (course-grained mode), the execution time is somewhere around
15min. Actually, I tried with find-grained mode and giving each Mesos node 6
VCPUs (to hopefully get 6 executors like the stand-alone test), I still get
roughly 15min.

I've noticed that when Spark is running on Mesos, almost all tasks execute
with locality NODE_LOCAL (even in Mesos in coarse-grained mode). On
stand-alone, the locality is mostly PROCESS_LOCAL.

I think this locality issue might be the reason for the slow down but I
can't figure out why, especially for coarse-grained mode as the executors
supposedly do not go away until job completion.

Any ideas?

Thanks,
Mike



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