我经常在虚拟机上工作，在my-site.xml中有以下设置：

<property>
    <description>Max available memory on each data node.</description>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>4096</value>
  </property>

  <property>
    <description>Max available cores data node.</description>
    <name>yarn.nodemanager.resource.cpu-vcores</name>
    <value>8</value>
  </property>

  <property>
    <description>Minimum allocation unit.</description>
    <name>yarn.scheduler.minimum-allocation-mb</name>
    <value>256</value>
  </property>

  <property>
    <description>Max allocation unit.</description>
    <name>yarn.scheduler.maximum-allocation-mb</name>
    <value>4096</value>
  </property>

  <property>
    <description>Minimum increment setting - set to same as min-allocation</description>
    <name>yarn.scheduler.increment-allocation-mb</name>
    <value>256</value>
  </property>

在mapred-site.xml中-

<!-- small cluster memory settings -->
  <property>
    <name>mapreduce.map.memory.mb</name>
    <value>256</value>
  </property>

  <property>
    <name>mapreduce.reduce.memory.mb</name>
    <value>256</value>
  </property>

  <property>
    <description>Application master allocation</description>
    <name>yarn.app.mapreduce.am.resource.mb</name>
    <value>256</value>
  </property>

  <property>
    <name>mapreduce.map.java.opts</name>
    <value>-Xmx204m</value>
  </property>

  <property>
    <name>mapreduce.reduce.java.opts</name>
    <value>-Xmx204m</value>
  </property>

  <property>
    <description>Application Master JVM opts</description>
    <name>yarn.app.mapreduce.am.command-opts</name>
    <value>-Xmx204m</value>
  </property>

  <property>
    <name>mapreduce.task.io.sort.mb</name>
    <value>50</value>
  </property>

我发现这些设置在我的虚拟机上运行的任何具有虚拟机大小的数据卷（也就是说，不是太大！）的东西上都能很好地工作。