How many executors are you running with? How many nodes in your cluster? On Thursday, October 8, 2015, unk1102 <umesh.ka...@gmail.com> wrote:
> Hi as recommended I am caching my Spark job dataframe as > dataframe.persist(StorageLevels.MEMORY_AND_DISK_SER) but what I see in > Spark > job UI is this persist stage runs for so long showing 10 GB of shuffle read > and 5 GB of shuffle write it takes to long to finish and because of that > sometimes my Spark job throws timeout or throws OOM and hence executors > gets > killed by YARN. I am using Spark 1.4.1. I am using all sort of > optimizations > like Tungsten, Kryo I have given storage.memoryFraction as 0.2 and > storage.shuffle as 0.2 also. My data is huge around 1 TB I am using default > 200 partitions for spark.sql.shuffle.partitions. Please help me I am > clueless please guide. > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Why-dataframe-persist-StorageLevels-MEMORY-AND-DISK-SER-hangs-for-long-time-tp24981.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org <javascript:;> > For additional commands, e-mail: user-h...@spark.apache.org <javascript:;> > > -- *Alex Rovner* *Director, Data Engineering * *o:* 646.759.0052 * <http://www.magnetic.com/>*
