What is performance overhead caused by YARN, or what configurations are
being changed when the app is ran through YARN?
The following example:
sqlContext.sql("SELECT dayStamp(date),
count(distinct deviceId) AS c
FROM full
GROUP BY dayStamp(date)
ORDER BY c
DESC LIMIT 10")
.collect()
runs on shell when we use standalone scheduler:
./spark-shell --master sparkmaster:7077 --executor-memory 20g
--executor-cores 10 --driver-memory 10g --num-executors 8
and fails due to losing an executor, when we run it through YARN.
./spark-shell --master yarn-client --executor-memory 20g --executor-cores
10 --driver-memory 10g --num-executors 8
There are no evident logs, just messages that executors are being lost, and
connection refused errors, (apparently due to executor failures)
The cluster is the same, 8 nodes, 64Gb RAM each.
Format is parquet.
--
RGRDZ Harut
