hadoop cluster is not running map reduce jobs - issue with scheduler - hadoop

Question

(this is a follow-up on a discussion I had regarding an earlier question I had on this matter)
I set up a small Hadoop cluster following these instructions but using Hadoop version 2.7.4. The cluster seems to work OK, but I cannot run mapreduce jobs. In particular, when trying the following
$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar randomwriter outdenter code here
the job prints
17/11/27 16:35:21 INFO client.RMProxy: Connecting to ResourceManager at
ec2-yyy.eu-central-
1.compute.amazonaws.com/xxx:8032
Running 0 maps.
Job started: Mon Nov 27 16:35:22 UTC 2017
17/11/27 16:35:22 INFO client.RMProxy: Connecting to ResourceManager at
ec2-yyy.eu-central-
1.compute.amazonaws.com/xxx:8032
17/11/27 16:35:22 INFO mapreduce.JobSubmitter: number of splits:0
17/11/27 16:35:22 INFO mapreduce.JobSubmitter: Submitting tokens for
job: job_1511799491035_0006
17/11/27 16:35:22 INFO impl.YarnClientImpl: Submitted application
application_1511799491035_0006
17/11/27 16:35:22 INFO mapreduce.Job: The url to track the job:
http://ec2-yyy.eu-central-
1.compute.amazonaws.com:8088/proxy/application_1511799491035_0006/
17/11/27 16:35:22 INFO mapreduce.Job: Running job:
job_1511799491035_0006
and never gets past this state.
In the job tracker, it says
ACCEPTED: waiting for AM container to be allocated, launched and
register with RM.
I then looked into the log files where I found
2017-11-27 13:50:29,202 INFO org.apache.hadoop.conf.Configuration: found resource capacity-scheduler.xml at file:/usr/local/hadoop/etc/hadoop/capacity-scheduler.xml
2017-11-27 13:50:29,252 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacitySchedulerConfiguration: max alloc mb per queue for root is undefined
2017-11-27 13:50:29,252 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacitySchedulerConfiguration: max alloc vcore per queue for root is undefined
2017-11-27 13:50:29,256 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.ParentQueue: root, capacity=1.0, asboluteCapacity=1.0, maxCapacity=1.0, asboluteMaxCapacity=1.0, state=RUNNING, acls=ADMINISTER_QUEUE:*SUBMIT_APP:*, labels=*, reservationsContinueLooking=true
2017-11-27 13:50:29,256 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.ParentQueue: Initialized parent-queue root name=root, fullname=root
2017-11-27 13:50:29,265 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacitySchedulerConfiguration: max alloc mb per queue for root.default is undefined
2017-11-27 13:50:29,265 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacitySchedulerConfiguration: max alloc vcore per queue for root.default is undefined
which suggest that there is a problem with the capacity scheduler. The file capacity-scheduler.xml looks as follows:
<configuration>
<property>
<name>yarn.scheduler.capacity.maximum-applications</name>
<value>10000</value>
<description>
Maximum number of applications that can be pending and running.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.maximum-am-resource-percent</name>
<value>0.1</value>
<description>
Maximum percent of resources in the cluster which can be used to run
application masters i.e. controls number of concurrent running
applications.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.resource-calculator</name>
<value>org.apache.hadoop.yarn.util.resource.DefaultResourceCalculator</value>
<description>
The ResourceCalculator implementation to be used to compare
Resources in the scheduler.
The default i.e. DefaultResourceCalculator only uses Memory while
DominantResourceCalculator uses dominant-resource to compare
multi-dimensional resources such as Memory, CPU etc.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.root.queues</name>
<value>default</value>
<description>
The queues at the this level (root is the root queue).
</description>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.capacity</name>
<value>100</value>
<description>Default queue target capacity.</description>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.user-limit-factor</name>
<value>1</value>
<description>
Default queue user limit a percentage from 0.0 to 1.0.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.maximum-capacity</name>
<value>100</value>
<description>
The maximum capacity of the default queue.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.state</name>
<value>RUNNING</value>
<description>
The state of the default queue. State can be one of RUNNING or STOPPED.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.acl_submit_applications</name>
<value>*</value>
<description>
The ACL of who can submit jobs to the default queue.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.root.default.acl_administer_queue</name>
<value>*</value>
<description>
The ACL of who can administer jobs on the default queue.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.node-locality-delay</name>
<value>40</value>
<description>
Number of missed scheduling opportunities after which the CapacityScheduler
attempts to schedule rack-local containers.
Typically this should be set to number of nodes in the cluster, By default is setting
approximately number of nodes in one rack which is 40.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.queue-mappings</name>
<value></value>
<description>
A list of mappings that will be used to assign jobs to queues
The syntax for this list is [u|g]:[name]:[queue_name][,next mapping]*
Typically this list will be used to map users to queues,
for example, u:%user:%user maps all users to queues with the same name
as the user.
</description>
</property>
<property>
<name>yarn.scheduler.capacity.queue-mappings-override.enable</name>
<value>false</value>
<description>
If a queue mapping is present, will it override the value specified
by the user? This can be used by administrators to place jobs in queues
that are different than the one specified by the user.
The default is false.
</description>
</property>
</configuration>
I would be grateful for any hints on how to approach this?.
Thanks
c14

Answer 1

Every thing fine with Cluster configuration but When it comes to Job execution, RAM provided by t2.micro instance is not enough to run the MapReduce jobs, so better use bigger instances for cluster creation and job execution