Spark Random Forest training cost same time on yarn as on standalone

[陈哲]

I'm training random forest model using spark2.0 on yarn with cmd like:
$SPARK_HOME/bin/spark-submit \
  --class com.netease.risk.prediction.HelpMain --master yarn --deploy-mode
client --driver-cores 1 --num-executors 32 --executor-cores 2 --driver-memory
10g --executor-memory 6g \
  --conf spark.rpc.askTimeout=3000 --conf spark.rpc.lookupTimeout=3000
--conf spark.rpc.message.maxSize=2000  --conf spark.driver.maxResultSize=0
    \
....

the training process cost almost 8 hours

And I tried training model on local machine with master(local[4]) , the
whole process still cost 8 - 9 hours.

My question is why running on yarn doesn't save time ? is this suppose to
be distributed, with 32 executors ? And am I missing anything or what I can
do to improve this and save more time ?

Thanks