spark Container killed on request. Exit code is 143 解决办法

scala启动多个sparkcontext时，发现启动不起来。查看Applications任务时，发现Memory Total和Memory Used一样了。

一，调整spark，driver和executor的内存

1，log日志错误

For more detailed output, check application tracking page:http://bigserver1:8088/cluster/app/application_1555651019351_0001Then, click on links to logs of each attempt.
Diagnostics: Container [pid=280568,containerID=container_e09_1555651019351_0001_02_000001] is running beyond virtual memory limits. Current usage: 383.9 MB of 1 GB physical memory used; 1.5 GB of 1.1 GB virtual memory used. Killing container.

Killed by external signal
。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。省略。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。

Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
Failing this attempt

2，java异常错误

2019-04-24 14:42:31 ERROR TransportRequestHandler:293 - Error sending result RpcResponse{requestId=8586978943123146370, body=NioManagedBuffer{buf=java.nio.HeapByteBuffer[pos=0 lim=13 cap=13]}} to /10.0.40.222:34982; closing connection
io.netty.handler.codec.EncoderException: java.lang.OutOfMemoryError: Java heap space

上面二个错误，都是因为spark分配的内存不足造成的，解决办法有二种

1，spark-submit的时候加上--driver-memory 2g --executor-memory 2g

2，修改spark-defaults.conf，添加以下内容

spark.driver.memory              2g
spark.executor.memory            2g

第二方法，改完后要重启spark集群。

改过后，spark-submit不报错，跑完后，出现以下内容说明配置成功

spark driver executor 内存调整

二，调整yarn资源

SparkContext.java.lang.IllegalArgumentException: Required executor memory (1024+384 MB) is above the max threshold (1024 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.

解决办法：修改yarn-site.xml

<property>
 <name>yarn.nodemanager.resource.cpu-vcores</name>
 <value>16</value>
</property>
<property>
 <name>yarn.scheduler.minimum-allocation-vcores</name>
 <value>1</value>
</property>
<property>
 <name>yarn.scheduler.maximum-allocation-vcores</name>
 <value>16</value>
</property>

<property>
 <name>yarn.nodemanager.resource.memory-mb</name>
 <value>16384</value>
</property>
<property>
 <name>yarn.scheduler.minimum-allocation-mb</name>
 <value>1024</value>
</property>
<property>
 <name>yarn.scheduler.maximum-allocation-mb</name>
 <value>16384</value>
</property>

改过的文件同步到hadoop集群中的所有机器上面，然后重启hadoop集群，如果Memory Total，增加了，说明成功了。

yarn 内存调整