Question

我正在尝试运行一个非常简单的工作来测试我的hadoop设置，所以我尝试使用Word Count示例，它被卡在0％，所以我尝试了一些其他简单的工作，每一个都卡住了

52191_0003/
14/07/14 23:55:51 INFO mapreduce.Job: Running job: job_1405376352191_0003
14/07/14 23:55:57 INFO mapreduce.Job: Job job_1405376352191_0003 running in uber mode : false
14/07/14 23:55:57 INFO mapreduce.Job:  map 0% reduce 0%

我正在使用hadoop版本 - Hadoop 2.3.0-cdh5.0.2

我对谷歌进行了快速研究并发现增加了

yarn.scheduler.minimum-allocation-mb
yarn.nodemanager.resource.memory-mb

我有单节点群集，在我的Macbook中运行双核和8 GB Ram。

我的yarn-site.xml文件 -

<configuration>

<!-- Site specific YARN configuration properties -->
  <property>
  <property>
    <name>yarn.resourcemanager.hostname</name>
    <value>resourcemanager.company.com</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/*,
        $HADOOP_YARN_HOME/*,$HADOOP_YARN_HOME/lib/*
    </value>
  </property>

  <property>
    <name>yarn.nodemanager.local-dirs</name>
    <value>file:///data/1/yarn/local,file:///data/2/yarn/local,file:///data/3/yarn/local</value>
  </property>
  <property>
    <name>yarn.nodemanager.log-dirs</name>
    <value>file:///data/1/yarn/logs,file:///data/2/yarn/logs,file:///data/3/yarn/logs</value>
  </property>
  <property>
  </property>
    <name>yarn.log.aggregation.enable</name>
    <value>true</value> 
  <property>
    <description>Where to aggregate logs</description>
    <name>yarn.nodemanager.remote-app-log-dir</name>
    <value>hdfs://var/log/hadoop-yarn/apps</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
    <description>shuffle service that needs to be set for Map Reduce to run </description>
  </property>
   <property>
    <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
    <value>org.apache.hadoop.mapred.ShuffleHandler</value>
  </property>
  </property>

  <property>
        <name>yarn.app.mapreduce.am.resource.mb</name>
        <value>8092</value>
    </property>
    <property>
        <name>yarn.app.mapreduce.am.command-opts</name>
        <value>-Xmx768m</value>
    </property>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
        <description>Execution framework.</description>
    </property>
    <property>
        <name>mapreduce.map.cpu.vcores</name>
        <value>4</value>
        <description>The number of virtual cores required for each map task.</description>
    </property>
    <property>
        <name>mapreduce.map.memory.mb</name>
        <value>8092</value>
        <description>Larger resource limit for maps.</description>
    </property>
    <property>
        <name>mapreduce.map.java.opts</name>
        <value>-Xmx768m</value>
        <description>Heap-size for child jvms of maps.</description>
    </property>
    <property>
        <name>mapreduce.jobtracker.address</name>
        <value>jobtracker.alexjf.net:8021</value>
    </property>

 <property>
    <name>yarn.scheduler.minimum-allocation-mb</name>
    <value>2048</value>
    <description>Minimum limit of memory to allocate to each container request at the Resource Manager.</description>
  </property>
  <property>
    <name>yarn.scheduler.maximum-allocation-mb</name>
    <value>8092</value>
    <description>Maximum limit of memory to allocate to each container request at the Resource Manager.</description>
  </property>
  <property>
    <name>yarn.scheduler.minimum-allocation-vcores</name>
    <value>2</value>
    <description>The minimum allocation for every container request at the RM, in terms of virtual CPU cores. Requests lower than this won't take effect, and the specified value will get allocated the minimum.</description>
  </property>
  <property>
    <name>yarn.scheduler.maximum-allocation-vcores</name>
    <value>10</value>
    <description>The maximum allocation for every container request at the RM, in terms of virtual CPU cores. Requests higher than this won't take effect, and will get capped to this value.</description>
  </property>
  <property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>2048</value>
    <description>Physical memory, in MB, to be made available to running containers</description>
  </property>
  <property>
    <name>yarn.nodemanager.resource.cpu-vcores</name>
    <value>4</value>
    <description>Number of CPU cores that can be allocated for containers.</description>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
    <description>shuffle service that needs to be set for Map Reduce to run </description>
  </property>
   <property>
    <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
    <value>org.apache.hadoop.mapred.ShuffleHandler</value>
  </property>

</configuration>

我的mapred-site.xml

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

只有1个属性。尝试了几种排列和组合，但无法摆脱错误。

记录工作

 23:55:55,694 WARN [main] org.apache.hadoop.conf.Configuration: job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval;  Ignoring.
2014-07-14 23:55:55,697 WARN [main] org.apache.hadoop.conf.Configuration: job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts;  Ignoring.
2014-07-14 23:55:55,699 INFO [main] org.apache.hadoop.yarn.client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8030
2014-07-14 23:55:55,769 INFO [main] org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: maxContainerCapability: 8092
2014-07-14 23:55:55,769 INFO [main] org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: queue: root.abhishekchoudhary
2014-07-14 23:55:55,775 INFO [main] org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl: Upper limit on the thread pool size is 500
2014-07-14 23:55:55,777 INFO [main] org.apache.hadoop.yarn.client.api.impl.ContainerManagementProtocolProxy: yarn.client.max-nodemanagers-proxies : 500
2014-07-14 23:55:55,787 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.JobImpl: job_1405376352191_0003Job Transitioned from INITED to SETUP
2014-07-14 23:55:55,789 INFO [CommitterEvent Processor #0] org.apache.hadoop.mapreduce.v2.app.commit.CommitterEventHandler: Processing the event EventType: JOB_SETUP
2014-07-14 23:55:55,800 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.JobImpl: job_1405376352191_0003Job Transitioned from SETUP to RUNNING
2014-07-14 23:55:55,823 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskImpl: task_1405376352191_0003_m_000000 Task Transitioned from NEW to SCHEDULED
2014-07-14 23:55:55,824 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskImpl: task_1405376352191_0003_m_000001 Task Transitioned from NEW to SCHEDULED
2014-07-14 23:55:55,824 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskImpl: task_1405376352191_0003_m_000002 Task Transitioned from NEW to SCHEDULED
2014-07-14 23:55:55,825 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskImpl: task_1405376352191_0003_m_000003 Task Transitioned from NEW to SCHEDULED
2014-07-14 23:55:55,826 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskAttemptImpl: attempt_1405376352191_0003_m_000000_0 TaskAttempt Transitioned from NEW to UNASSIGNED
2014-07-14 23:55:55,827 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskAttemptImpl: attempt_1405376352191_0003_m_000001_0 TaskAttempt Transitioned from NEW to UNASSIGNED
2014-07-14 23:55:55,827 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskAttemptImpl: attempt_1405376352191_0003_m_000002_0 TaskAttempt Transitioned from NEW to UNASSIGNED
2014-07-14 23:55:55,827 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskAttemptImpl: attempt_1405376352191_0003_m_000003_0 TaskAttempt Transitioned from NEW to UNASSIGNED
2014-07-14 23:55:55,828 INFO [Thread-49] org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: mapResourceReqt:8092
2014-07-14 23:55:55,858 INFO [eventHandlingThread] org.apache.hadoop.mapreduce.jobhistory.JobHistoryEventHandler: Event Writer setup for JobId: job_1405376352191_0003, File: hdfs://localhost/tmp/hadoop-yarn/staging/abhishekchoudhary/.staging/job_1405376352191_0003/job_1405376352191_0003_1.jhist
2014-07-14 23:55:56,773 INFO [RMCommunicator Allocator] org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Before Scheduling: PendingReds:0 ScheduledMaps:4 ScheduledReds:0 AssignedMaps:0 AssignedReds:0 CompletedMaps:0 CompletedReds:0 ContAlloc:0 ContRel:0 HostLocal:0 RackLocal:0
2014-07-14 23:55:56,799 INFO [RMCommunicator Allocator] org.apache.hadoop.mapreduce.v2.app.rm.RMContainerRequestor: getResources() for application_1405376352191_0003: ask=1 release= 0 newContainers=0 finishedContainers=0 resourcelimit=<memory:0, vCores:0> knownNMs=1

Answer 1

基于消息Connecting to ResourceManager at /0.0.0.0:8030，你确定你的ResourceManager应该是0.0.0.0:8030（默认值）吗？如果不是，您应该将以下内容添加到yarn-site.xml：

<property>
  <name>yarn.resourcemanager.hostname</name>
  <value>MASTER ADDRESS</value>
</property>
<property>
  <name>yarn.resourcemanager.resource-tracker.address</name>
  <value>${yarn.resourcemanager.hostname}:8025</value>
</property>
<property>
  <name>yarn.resourcemanager.scheduler.address</name>
  <value>${yarn.resourcemanager.hostname}:8030</value>
</property>
<property>
  <name>yarn.resourcemanager.address</name>
  <value>${yarn.resourcemanager.hostname}:8040</value>
</property>
<property>
  <name>yarn.resourcemanager.webapp.address</name>
  <value>${yarn.resourcemanager.hostname}:8088</value>
</property>
<property>
  <name>yarn.resourcemanager.admin.address</name>
  <value>${yarn.resourcemanager.hostname}:8033</value>
</property>

将MASTER ADDRESS替换为主节点的地址。您可以单独更改资源管理器的webapp，admin等的地址。

Answer 2

您的设置似乎不正确。

设置yarn.nodemanager.resource.memory-mb 设置为2GB。这是“物理内存量，以MB为单位，可以为容器分配。”但是您的mapreduce.map.memory.mb是8GB。 8GB是你真正要求的。

此外，您已将yarn.app.mapreduce.am.resource.mb设置为8GB。因此，您正在尝试分配一个AM来控制8GB的作业以及8GB的几个映射器。

<强>解决方案

要解决此问题，您可以将AM尺寸缩小至1GB，然后将mapper尺寸缩小至.5GB，这对于玩游戏来说更合理用于字数。

其他资源

您可以参考Clouera提供的this instruction来更详细地了解这些属性。

Answer 3

我不知道您在创建此问题时是否只是复制/粘贴错误，但查看yarn-site.xml它会以两个<property>标记开头。我不确定Hadoop的xml解析器是否会实际应用那些嵌套的<property>标记。

Answer 4

我正在使用Apache Hadoop版本2.7.2，因此可能就像＆＃34; apple-to-oranges＆＃34;比较，但是前几天我遇到了同样的无声卡住状态。在大多数情况下，这种＆＃34;沉默＆＃34;延长的时间段表示调度程序无法为应用程序分配足够的资源。

在具有类似配置的特定情况下，增加 yarn-site.xml 中属性 yarn.nodemanager.resource.memory-mb 的值就可以了。

您还可以检查资源分配的其他属性here

Hadoop YARN工作陷入地图0％并降低0％

4 个答案: