I'm interested in determining the correct YARN memory settings for our Hadoop cluster configured with nodes containing 32GB RAM. We have applied the recommended settings (via Ambari) using yarn-utils script referenced in the "Determining HDP Memory Configuration Settings" article (see link below). It seems to be working fine (most of the time). However I started to question the settings a bit when one of my more experienced team members thought the settings looked too high. I noticed there are no values recommended for nodes with 32GB of RAM in the Reserved Memory Allocations Table. Is there a specific reason for this? Also, I was looking at the yarn-utils python script. It has two tables that are used to calculate reserved memory for OS/HBase etc. Although there are no values for 32GB in those tables either. I was wondering if anyone else has come across this and what settings they applied for 32GB or RAM? Any suggestions are very much appreciated! 🙂 Thanks! https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.6.0/bk_command-line-installation/content/determine-hdp-memory-config.html Here are the outputs from running the yarn-utils script (Note I modified it slightly to add some log statements): $ python yarn-utils.py -c 4 -m 32 -d 1 -k True Using cores=4 memory=32GB disks=1 hbase=True Min Container Size:2048 Reserved Stack Memory:1 Reserved Hbase Memory:2 Profile: cores=4 memory=29696MB reserved=3GB usableMem=29GB disks=1 Num Container=3 Container Ram=9728MB Used Ram=28GB Unused Ram=3GB yarn.scheduler.minimum-allocation-mb=9728 yarn.scheduler.maximum-allocation-mb=29184 yarn.nodemanager.resource.memory-mb=29184 mapreduce.map.memory.mb=9728 mapreduce.map.java.opts=-Xmx7782m mapreduce.reduce.memory.mb=9728 mapreduce.reduce.java.opts=-Xmx7782m yarn.app.mapreduce.am.resource.mb=9728 yarn.app.mapreduce.am.command-opts=-Xmx7782m mapreduce.task.io.sort.mb=3891 ... View more