HADOOP YARN - 应用程序已添加到调度程序，但尚未激活。由于集群资源为空而跳过 AM 分配

我正在评估一个项目的 YARN。我正在尝试让简单的分布式 shell 示例正常工作。我已将应用程序提交到“已提交”阶段，但它从未启动。这是从这一行报告的信息：

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;

其他开发者的解决方案似乎还要增加yarn.scheduler.capacity.maximum-am-resource-percent在yarn-site.xml 文件中从其默认值.1。我尝试过的价值观.2 and .5但这似乎没有帮助。

看起来您没有以正确的方式配置分配给 Yarn 的 RAM。如果您尝试根据自己的安装从教程中推断/改编，这可能是……中的一个引脚。我会强烈推荐您使用诸如this one https://github.com/stealthly/hdp-accumulo/tree/master/vagrant/hdp_manual_install_rpm_helper_files-2.0.6.76:

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每个节点拥有的核心数
• -m每个节点拥有的内存量（千兆）
• -d每个节点拥有的磁盘数量
• -bool如果安装了 HBase，则为“True”；如果不是则为“假”

这应该给你类似的东西：

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

编辑你的yarn-site.xml and mapred-site.xml因此。

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

此外，你应该在你的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>

这在你的mapred-site.xml:

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

然后，使用以下命令将您的conf文件上传到每个节点scp（如果您将 ssh 密钥上传到每一个）

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

然后，重新启动纱线

stop-yarn.sh
start-yarn.sh

并检查您是否可以看到您的节点：

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

这可能会解决问题（祝你好运）（附加信息 https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.6.4/bk_command-line-installation/content/determine-hdp-memory-config.html)