HADOOP YARN - Application is added to the scheduler and is not yet activated. Skipping AM assignment as cluster resource is empty

Question

I am evaluating YARN for a project. I am trying to get the simple distributed shell example to work. I have gotten the application to the SUBMITTED phase, but it never starts. This is the information reported from this line:

ApplicationReport report = yarnClient.getApplicationReport(appId);

Application is added to the scheduler and is not yet activated. Skipping AM assignment as cluster resource is empty. Details : AM Partition = DEFAULT_PARTITION; AM Resource Request = memory:1024, vCores:1; Queue Resource Limit for AM = memory:0, vCores:0; User AM Resource Limit of the queue = memory:0, vCores:0; Queue AM Resource Usage = memory:128, vCores:1;

The solutions for other developers seems to have to increase yarn.scheduler.capacity.maximum-am-resource-percent in the yarn-site.xml file from its default value of .1. I have tried values of .2 and .5 but it does not seem to help.

Answer 1

Looks like you did not configure the RAM allocated to Yarn in a proper way. This can be a pin in the ..... if you try to infer/adapt from tutorials according to your own installation. I would strongly recommend that you use tools such as this one:

wget http://public-repo-1.hortonworks.com/HDP/tools/2.6.0.3/hdp_manual_install_rpm_helper_files-2.6.0.3.8.tar.gz
tar zxvf hdp_manual_install_rpm_helper_files-2.6.0.3.8.tar.gz
rm hdp_manual_install_rpm_helper_files-2.6.0.3.8.tar.gz
mv hdp_manual_install_rpm_helper_files-2.6.0.3.8/ hdp_conf_files
python hdp_conf_files/scripts/yarn-utils.py -c 4 -m 8 -d 1 false

• -c number of cores you have for each node
• -m amount of memory you have for each node (Giga)
• -d number of disk you have for each node
• -bool "True" if HBase is installed; "False" if not

This should give you something like:

Using cores=4 memory=8GB disks=1 hbase=True
Profile: cores=4 memory=5120MB reserved=3GB usableMem=5GB disks=1
Num Container=3
Container Ram=1536MB
Used Ram=4GB
Unused Ram=3GB
yarn.scheduler.minimum-allocation-mb=1536
yarn.scheduler.maximum-allocation-mb=4608
yarn.nodemanager.resource.memory-mb=4608
mapreduce.map.memory.mb=1536
mapreduce.map.java.opts=-Xmx1228m
mapreduce.reduce.memory.mb=3072
mapreduce.reduce.java.opts=-Xmx2457m
yarn.app.mapreduce.am.resource.mb=3072
yarn.app.mapreduce.am.command-opts=-Xmx2457m
mapreduce.task.io.sort.mb=614

Edit your yarn-site.xml and mapred-site.xml accordingly.

 nano ~/hadoop/etc/hadoop/yarn-site.xml
 nano ~/hadoop/etc/hadoop/mapred-site.xml

Moreover, you should have this in your yarn-site.xml

<property>
      <name>yarn.acl.enable</name>
      <value>0</value>
</property>

<property>
       <name>yarn.resourcemanager.hostname</name>
       <value>name_of_your_master_node</value>
</property>

<property>
       <name>yarn.nodemanager.aux-services</name>
       <value>mapreduce_shuffle</value>
</property>

and this in your mapred-site.xml:

<property>
       <name>mapreduce.framework.name</name>
       <value>yarn</value>
</property>

Then, upload your conf files to each node using scp (If you uploaded you ssh keys to each one)

for node in node1 node2 node3; do scp ~/hadoop/etc/hadoop/* $node:/home/hadoop/hadoop/etc/hadoop/; done

And then, restart yarn

stop-yarn.sh
start-yarn.sh

and check that you can see your nodes:

hadoop@master-node:~$ yarn node -list
18/06/01 12:51:33 INFO client.RMProxy: Connecting to ResourceManager at master-node/192.168.0.37:8032
Total Nodes:3
     Node-Id         Node-State Node-Http-Address   Number-of-Running-Containers
 node3:34683            RUNNING        node3:8042                              0
 node2:36467            RUNNING        node2:8042                              0
 node1:38317            RUNNING        node1:8042                              0

This might fix the issue (good luck) (additional info)