MapReduce作业挂起，等待AM容器被分配 [英] MapReduce job hangs, waiting for AM container to be allocated

问题描述

我试图将简单的字数统计为MapReduce作业。在本地运行时（所有在名称节点上完成的工作），一切工作正常。但是，当我尝试使用YARN在集群上运行它时（将 mapreduce.framework.name = yarn 添加到mapred

我在这里遇到了类似的问题：
）

p>您应该检查群集中节点管理器的状态。如果NM节点磁盘空间不足，那么RM会将它们标记为不健康，并且这些NM不能分配新容器。1）检查不健康节点：

code> http：//< active_RM>：8088 / cluster / nodes / unhealthy

如果健康报告选项卡说：本地dirs是坏的，那么它意味着你需要从这些节点清理一些磁盘空间。
$ b 2）检查DFS dfs。 data.dir hdfs-site.xml 中的属性。它指向存储hdfs数据的本地文件系统上的位置。

3）登录到这些机器并使用 df -h & hadoop fs - du -h 命令来测量占用的空间。

4）验证hadoop垃圾并删除它if它阻止了你。
hadoop fs -du -h /user/user_name/.Trash 和 hadoop fs -rm -r /user/user_name/.Trash / *

I tried to run simple word count as MapReduce job. Everything works fine when run locally (all work done on Name Node). But, when I try to run it on a cluster using YARN (adding mapreduce.framework.name=yarn to mapred-site.conf) job hangs.

I came across a similar problem here: MapReduce jobs get stuck in Accepted state

Output from job:

*** START ***
15/12/25 17:52:50 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/12/25 17:52:51 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
15/12/25 17:52:51 INFO input.FileInputFormat: Total input paths to process : 5
15/12/25 17:52:52 INFO mapreduce.JobSubmitter: number of splits:5
15/12/25 17:52:52 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1451083949804_0001
15/12/25 17:52:53 INFO impl.YarnClientImpl: Submitted application application_1451083949804_0001
15/12/25 17:52:53 INFO mapreduce.Job: The url to track the job: http://hadoop-droplet:8088/proxy/application_1451083949804_0001/
15/12/25 17:52:53 INFO mapreduce.Job: Running job: job_1451083949804_0001

mapred-site.xml:

<configuration>

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

<property>
   <name>mapreduce.job.tracker</name>
   <value>localhost:54311</value>
</property> 

<!--
<property>
   <name>mapreduce.job.tracker.reserved.physicalmemory.mb</name>
   <value></value>
</property>

<property>
   <name>mapreduce.map.memory.mb</name>
   <value>1024</value>
</property>

<property>
   <name>mapreduce.reduce.memory.mb</name>
   <value>2048</value>
</property>    

<property>
   <name>yarn.app.mapreduce.am.resource.mb</name>
   <value>3000</value>
   <source>mapred-site.xml</source>
</property> -->

</configuration>

yarn-site.xml

<configuration>
 <property>
   <name>yarn.nodemanager.aux-services</name>
   <value>mapreduce_shuffle</value>
 </property>
 <property>
   <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
   <value>org.apache.hadoop.mapred.ShuffleHandler</value>
 </property>

<!--
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>3000</value>
<source>yarn-site.xml</source>
</property>

<property>
  <name>yarn.scheduler.minimum-allocation-mb</name>
  <value>500</value>
</property>

<property>
<name>yarn.scheduler.capacity.maximum-am-resource-percent</name>
<value>3000</value>
</property>
-->

</configuration>

//I the left commented options - they were not solving the problem

YarnApplicationState: ACCEPTED: waiting for AM container to be allocated, launched and register with RM.

What can be the problem?

EDIT:

I tried this configuration (commented) on machines: NameNode(8GB RAM) + 2x DataNode (4GB RAM). I get the same effect: Job hangs on ACCEPTED state.

EDIT2: changed configuration (thanks @Manjunath Ballur) to:

yarn-site.xml:

<configuration>
  <property>
    <name>yarn.resourcemanager.hostname</name>
    <value>hadoop-droplet</value>
  </property>

  <property>
    <name>yarn.resourcemanager.resource-tracker.address</name>
    <value>hadoop-droplet:8031</value>
  </property>
  <property>
    <name>yarn.resourcemanager.address</name>
    <value>hadoop-droplet:8032</value>
  </property>
  <property>
    <name>yarn.resourcemanager.scheduler.address</name>
    <value>hadoop-droplet:8030</value>
  </property>
  <property>
    <name>yarn.resourcemanager.admin.address</name>
    <value>hadoop-droplet:8033</value>
  </property>
  <property>
    <name>yarn.resourcemanager.webapp.address</name>
    <value>hadoop-droplet:8088</value>
  </property>
  <property>
    <description>Classpath for typical applications.</description>
    <name>yarn.application.classpath</name>
    <value>
        $HADOOP_CONF_DIR,
        $HADOOP_COMMON_HOME/*,$HADOOP_COMMON_HOME/lib/*,
        $HADOOP_HDFS_HOME/*,$HADOOP_HDFS_HOME/lib/*,
        $HADOOP_MAPRED_HOME/*,$HADOOP_MAPRED_HOME/lib/*,
        $YARN_HOME/*,$YARN_HOME/lib/*
    </value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce.shuffle</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
    <value>org.apache.hadoop.mapred.ShuffleHandler</value>
  </property>
  <property>
    <name>yarn.nodemanager.local-dirs</name>
    <value>/data/1/yarn/local,/data/2/yarn/local,/data/3/yarn/local</value>
  </property>
  <property>
    <name>yarn.nodemanager.log-dirs</name>
    <value>/data/1/yarn/logs,/data/2/yarn/logs,/data/3/yarn/logs</value>
  </property>
  <property>
    <description>Where to aggregate logs</description>
    <name>yarn.nodemanager.remote-app-log-dir</name>
    <value>/var/log/hadoop-yarn/apps</value>
  </property>
  <property> 
    <name>yarn.scheduler.minimum-allocation-mb</name> 
    <value>50</value>
  </property>
  <property> 
    <name>yarn.scheduler.maximum-allocation-mb</name> 
    <value>390</value>
  </property>
  <property> 
    <name>yarn.nodemanager.resource.memory-mb</name> 
    <value>390</value>
  </property>
</configuration>

mapred-site.xml:

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

<property>  
    <name>yarn.app.mapreduce.am.resource.mb</name>  
    <value>50</value>
</property>
<property> 
    <name>yarn.app.mapreduce.am.command-opts</name> 
    <value>-Xmx40m</value>
</property>
<property>
    <name>mapreduce.map.memory.mb</name>
    <value>50</value>
</property>
<property>
    <name>mapreduce.reduce.memory.mb</name>
    <value>50</value>
</property>
<property>
    <name>mapreduce.map.java.opts</name>
    <value>-Xmx40m</value>
</property>
<property>
    <name>mapreduce.reduce.java.opts</name>
    <value>-Xmx40m</value>
</property>
</configuration>

Still not working. Additional info: I can see no nodes on cluster preview (similar problem here: Slave nodes not in Yarn ResourceManager )

解决方案

You should check the status of Node managers in your cluster. If the NM nodes are short on disk space then RM will mark them "unhealthy" and those NMs can't allocate new containers.

1) Check the Unhealthy nodes: http://<active_RM>:8088/cluster/nodes/unhealthy

If the "health report" tab says "local-dirs are bad" then it means you need to cleanup some disk space from these nodes.

2) Check the DFS dfs.data.dir property in hdfs-site.xml. It points the location on local file system where hdfs data is stored.

3) Login to those machines and use df -h & hadoop fs - du -h commands to measure the space occupied.

4) Verify hadoop trash and delete it if it's blocking you. hadoop fs -du -h /user/user_name/.Trash and hadoop fs -rm -r /user/user_name/.Trash/*

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