The issue was not related the configuration related to containers. Due to misconfiguration, the Application master was not able to contact resourcemanager causing in the 1 container problem.
However, the total containers allocated still is not as expected. The configuration settings should have resulted in 16 containers per node, but it is allocating 64 containers per node. Reiterating the config parameters here again: mapred-site.xml mapreduce.map.cpu.vcores = 1 mapreduce.reduce.cpu.vcores = 1 mapreduce.map.memory.mb = 1024 mapreduce.reduce.memory.mb = 1024 mapreduce.map.java.opts = -Xmx1024m mapreduce.reduce.java.opts = -Xmx1024m yarn.xml yarn.nodemanager.resource.memory-mb = 65536 yarn.nodemanager.resource.cpu-vcores = 16 yarn.scheduler.minimum-allocation-mb = 1024 yarn.scheduler.maximum-allocation-mb = 2048 yarn.scheduler.minimum-allocation-vcores = 1 yarn.scheduler.maximum-allocation-vcores = 1 Is there anything else that might be causing this problem ? thanks, hari On Tue, May 27, 2014 at 3:31 AM, hari <[email protected]> wrote: > Hi, > > When using YARN 2.2.0 version, only 1 container is created > for an application in the entire cluster. > The single container is created at an arbitrary node > for every run. This happens when running any application from > the examples jar (e.g., wordcount). Currently only one application is > run at a time. The input datasize is > 200GB. > > I am setting custom values that affect concurrent container count. > These config parameters were mostly taken from: > > http://blog.cloudera.com/blog/2014/04/apache-hadoop-yarn-avoiding-6-time-consuming-gotchas/ > These wasn't much description elsewhere on how the container count would > be > decided. > > The settings are: > > mapred-site.xml > mapreduce.map.cpu.vcores = 1 > mapreduce.reduce.cpu.vcores = 1 > mapreduce.map.memory.mb = 1024 > mapreduce.reduce.memory.mb = 1024 > mapreduce.map.java.opts = -Xmx1024m > mapreduce.reduce.java.opts = -Xmx1024m > > yarn.xml > yarn.nodemanager.resource.memory-mb = 65536 > yarn.nodemanager.resource.cpu-vcores = 16 > > From these settings, each node should be running 16 containers. > > Let me know if there might be something else affecting the container > count. > > thanks, > hari > > > > > >
