spark on yarn:Container is running beyond physical memory limits

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在虚拟机中安装好hadoop和spark后。执行start-all.sh（hadoop命令）来开启hdfs和yarn服务。服务开启成功后，为了测试spark on yarn的可执行性，输入命令：

./bin/spark-submit --class org.apache.spark.examples.SparkPi --master yarn --deploy-mode client ./examples/jars/spark-examples_2.11-2.1.1.jar 100

执行后我们发现该程序报错：

Application application_1447669815913_0002 failed 2 times due to AM Container for appattempt_1447669815913_0002_000002 exited with exitCode: -104 For more detailed output, check application tracking page:http://myserver.com:8088/proxy/application_1447669815913_0002/Then, click on links to logs of each attempt. Diagnostics: Container [pid=47384,containerID=container_1447669815913_0002_02_000001] is running beyond physical memory limits. Current usage: 17.9 GB of 17.5 GB physical memory used; 18.7 GB of 36.8 GB virtual memory used. Killing container.

造成这个错误的原因是虚拟机资源不足，不能创建满足要求的container。由于我们的执行是spark on yarn，所以为了提高虚拟机的内存，我们可以在hadoop的配置目录下

配置yarn-site.xml. 我们在原有配置下添加如下配置：

<property>   <name>yarn.nodemanager.vmem-check-enabled</name>    <value>false</value>    <description>Whether virtual memory limits will be enforced for containers</description>  </property><property>   <name>yarn.nodemanager.vmem-pmem-ratio</name>    <value>4</value>    <description>Ratio between virtual memory to physical memory when setting memory limits for containers</description></property>

第一个配置是虚拟机内存不足时，是否强制启动container, 设置为否，可以尽早发现内存错误

第二个配置是增加虚拟机内存到物理内存的映射比例。默认为2.1.

重启服务，重新提交任务，执行成功。(一定要重启服务)

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