The worker nodes on my version 2.2 cluster won't use more than 11 of the 30 total (24 allocated) for mapreduce jobs running in Yarn. Does anyone have an idea what might be constraining the usage of Ram?
I followed the steps listed here: http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html , and http://hortonworks.com/blog/how-to-plan-and-configure-yarn-in-hdp-2-0/. to set various memory configuration, but no matter what I try, the nodes on the cluster don't use more than 11GB of the allocated 26GB. The yarn resource manager reports that it is using all of the allocated memory in the status across the top, but according to TOP and other such, it is not. I see org.apache.hadoop.mapred.YarnChild processes being created with -Xmx756m, but I can't find this anywhere in mapreduce or yarn configurations. yarn.nodemanager.resource.memory-mb = 24576 yarn.scheduler.minimum-allocation-mb = 3072 yarn_heapsize=20000 (not really clear to me what this does...?) mapreduce2 config: mapreduce.map.memory.mb = 4096 mapreduce.reduce.memory.mb = 8192 mapreduce.map.java.opts = -Xmx3500 mapreduce.reduce.java.opts = -Xmx7000 Thanks! Aaron Zimmerman
