I tried below solution it works perfectly for me. 1) Change the Hadoop schedule type from capacity scheduler to fair scheduler. Because for small cluster each queue assign some memory size (2048MB) to complete single map reduce job. If more than one map reduce job run in single queue mean it met deadlock. Solution: add below property to yarn-site.xml <property> <name>yarn.resourcemanager.scheduler.class</name> <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value> </property> <property> <name>yarn.scheduler.fair.allocation.file</name> <value>file:/%HADOOP_HOME%/etc/hadoop/fair-scheduler.xml</value> </property> 2) By default Hadoop Total memory size was allot as 8GB. So if we run two mapreduce program memory used by Hadoop get more than 8GB so it met deadlock. Solution: Increase the size of Total Memory of nodemanager using following properties at yarn-site.xml <property> <name>yarn.nodemanager.resource.memory-mb</name> <value>20960</value> </property> <property> <name>yarn.scheduler.minimum-allocation-mb</name> <value>1024</value> </property> <property> <name>yarn.scheduler.maximum-allocation-mb</name> <value>2048</value> </property> So If user try to run more than two mapreduce program mean he need to increase nodemanager or he need to increase the size of total memory of Hadoop (note: Increasing the size will reduce the system usage memory. Above property file able to run 10 map reduce program concurrently.) ... View more