输入文件大小：75gb
制图员人数：2273
减速器数量：1（如web ui所示）
分裂数：2273
输入文件数：867
集群：apache hadoop 2.4.0
5个节点群集，每个1tb。
1个主节点和4个数据节点。
已经4个小时了。现在仍然只有12%的Map完成。我只是想知道在给定集群配置的情况下，这样做有意义吗？或者配置有什么问题吗？
yarn-site.xml文件

<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.resourcemanager.resource- tracker.address</name>
             <value>master:8025</value>
             </property>
             <property>
             <name>yarn.resourcemanager.scheduler.address</name>
             <value>master:8030</value>
             </property>
             <property>
              <name>yarn.resourcemanager.scheduler.address</name>
             <value>master:8030</value>
             </property>
             <property>
             <name>yarn.resourcemanager.address</name>
             <value>master:8040</value>
             </property>
             <property>
             <name>yarn.resourcemanager.hostname</name>
             <value>master</value>
             <description>The hostname of the RM.</description>
             </property>
             <property>
             <name>yarn.scheduler.minimum-allocation-mb</name>
             <value>1024</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>8192</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>1</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>32</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>8192</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.vmem-pmem-ratio</name>
             <value>4</value>
             </property> 
             <property>
   <name>yarn.nodemanager.vmem-check-enabled</name>
   <value>false</value>
   <description>Whether virtual memory limits will be enforced for containers</description>
</property>

Map我使用多个输出的reduce作业。所以reducer会发出多个文件。每台机器有15gb内存。正在运行的容器是8个。rm web ui中的总可用内存为32gb。
感谢您的指导。提前谢谢。