Can you send over your yarn logs along with the command you are using to submit your job?
*Alex Rovner* *Director, Data Engineering * *o:* 646.759.0052 * <http://www.magnetic.com/>* On Sat, Oct 3, 2015 at 9:07 AM, Umesh Kacha <umesh.ka...@gmail.com> wrote: > Hi Alex thanks much for the reply. Please read the following for more > details about my problem. > > > http://stackoverflow.com/questions/32317285/spark-executor-oom-issue-on-yarn > > My each container has 8 core and 30 GB max memory. So I am using > yarn-client mode using 40 executors with 27GB/2 cores. If I use more cores > then my job start loosing more executors. I tried to set > spark.yarn.executor.memoryOverhead around 2 GB even 8 GB but it does not > help I loose executors no matter what. The reason is my jobs shuffles lots > of data even 20 GB of data in every job in UI I have seen it. Shuffle > happens because of group by and I cant avoid it in my case. > > > > On Sat, Oct 3, 2015 at 6:27 PM, Alex Rovner <alex.rov...@magnetic.com> > wrote: > >> This sounds like you need to increase YARN overhead settings with the >> "spark.yarn.executor.memoryOverhead" >> parameter. See http://spark.apache.org/docs/latest/running-on-yarn.html >> for more information on the setting. >> >> If that does not work for you, please provide the error messages and the >> command line you are using to submit your jobs for further troubleshooting. >> >> >> *Alex Rovner* >> *Director, Data Engineering * >> *o:* 646.759.0052 >> >> * <http://www.magnetic.com/>* >> >> On Sat, Oct 3, 2015 at 6:19 AM, unk1102 <umesh.ka...@gmail.com> wrote: >> >>> Hi I have couple of Spark jobs which uses group by query which is getting >>> fired from hiveContext.sql() Now I know group by is evil but my use case >>> I >>> cant avoid group by I have around 7-8 fields on which I need to do group >>> by. >>> Also I am using df1.except(df2) which also seems heavy operation and does >>> lots of shuffling please see my UI snap >>> < >>> http://apache-spark-user-list.1001560.n3.nabble.com/file/n24914/IMG_20151003_151830218.jpg >>> > >>> >>> I have tried almost all optimisation including Spark 1.5 but nothing >>> seems >>> to be working and my job fails hangs because of executor will reach >>> physical >>> memory limit and YARN will kill it. I have around 1TB of data to process >>> and >>> it is skewed. Please guide. >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/How-to-optimize-group-by-query-fired-using-hiveContext-sql-tp24914.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> For additional commands, e-mail: user-h...@spark.apache.org >>> >>> >> >
