Hi thanks I cant share yarn logs because of privacy in my company but I can tell you I have seen yarn logs there I have not found anything except YARN killing container because it is exceeds physical memory capacity.
I am using the following command line script Above job launches around 1500 ExecutorService threads from a driver with a thread pool of 15 so at a time 15 jobs will be running as showing in UI. ./spark-submit --class com.xyz.abc.MySparkJob --conf "spark.executor.extraJavaOptions=-XX:MaxPermSize=512M" - -driver-java-options -XX:MaxPermSize=512m - -driver-memory 4g --master yarn-client --executor-memory 27G --executor-cores 2 --num-executors 40 --jars /path/to/others-jars /path/to/spark-job.jar On Sat, Oct 3, 2015 at 7:11 PM, Alex Rovner <alex.rov...@magnetic.com> wrote: > 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 >>>> >>>> >>> >> >
