spark在yarn集群上执行client模式代码 Container killed on request. Exit code is 143 Container exited with a no

spark在yarn集群上执行client模式代码
启动历史服务

bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode client \
./examples/jars/spark-examples_2.12-3.0.0.jar \
10

出现问题：
[2021-03-15 20:36:42.553]Container killed on request. Exit code is 143
[2021-03-15 20:36:42.554]Container exited with a non-zero exit code 143.
For more detailed output, check the application tracking page: http://hadoop103:8088/clusterpp/application_1615792406203_0009 Then click on links to logs of each attempt.
. Failing the application.
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(rnClientSchedulerBackend.scala:95)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchulerBackend.scala:62)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:201)
at org.apache.spark.SparkContext.(SparkContext.scala:550)
at org.apache.spark.SparkContext $. g e t O r C r e a t e (S p a r k C o n t e x t . s c a l a : 2555) a t o r g . a p a c h e . s p a r k . s q l . S p a r k S e s s i o n$ Builder. $a n o n f u n$ getOrCreate $1 (S p a r k S e s s i o n . s c a : 930) a t s c a l a . O p t i o n . g e t O r E l s e (O p t i o n . s c a l a : 189) a t o r g . a p a c h e . s p a r k . s q l . S p a r k S e s s i o n$ Builder.getOrCreate(SparkSession.scala:921)
at org.apache.spark.examples.SparkPi $. m a i n (S p a r k P i . s c a l a : 30) a t o r g . a p a c h e . s p a r k . e x a m p l e s . S p a r k P i . m a i n (S p a r k P i . s c a l a) a t s u n . r e f l e c t . N a t i v e M e t h o d A c c e s s o r I m p l . i n v o k e 0 (N a t i v e M e t h o d) a t s u n . r e f l e c t . N a t i v e M e t h o d A c c e s s o r I m p l . i n v o k e (N a t i v e M e t h o d A c c e s s o r I m p l . j a v a : 62) a t s u n . r e f l e c t . D e l e g a t i n g M e t h o d A c c e s s o r I m p l . i n v o k e (D e l e g a t i n g M e t h o d A c c e s s o r I m p l . j a v a 3) a t j a v a . l a n g . r e f l e c t . M e t h o d . i n v o k e (M e t h o d . j a v a : 498) a t o r g . a p a c h e . s p a r k . d e p l o y . J a v a M a i n A p p l i c a t i o n . s t a r t (S p a r k A p p l i c a t i o n . s c a l a : 52) a t o r g . a p a c h e . s p a r k . d e p l o y . S p a r k S u b m i t . o r g$ apache $s p a r k$ deploy $S p a r k S u b m i t$ $runMain(arkSubmit.scala:928)
at org.apache.spark.deploy.SparkSubmit.doRunMain $1 (S p a r k S u b m i t . s c a l a : 180) a t o r g . a p a c h e . s p a r k . d e p l o y . S p a r k S u b m i t . s u b m i t (S p a r k S u b m i t . s c a l a : 203) a t o r g . a p a c h e . s p a r k . d e p l o y . S p a r k S u b m i t . d o S u b m i t (S p a r k S u b m i t . s c a l a : 90) a t o r g . a p a c h e . s p a r k . d e p l o y . S p a r k S u b m i t$ $anon $2 . d o S u b m i t (S p a r k S u b m i t . s c a l a : 1007) a t o r g . a p a c h e . s p a r k . d e p l o y . S p a r k S u b m i t$ .main(SparkSubmit.scala:1016)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

解决办法：
修改hadoop配置文件 /opt/module/hadoop/etc/hadoop/yarn-site.xml 并分发、重启hadoop集群

<!--是否启动一个线程检查每个任务正使用的物理内存量，如果任务超出分配值，则直接将其杀掉，默认是true -->
 <property>
     <name>yarn.nodemanager.pmem-check-enabled</name>
     <value>false</value>
</property>
<!--是否启动一个线程检查每个任务正使用的虚拟内存量，如果任务超出分配值，则直接将其杀掉，默认是true -->
<property>
     <name>yarn.nodemanager.vmem-check-enabled</name>
     <value>false</value>
</property>

原文链接：https://blog.csdn.net/weixin_45401713/article/details/114852000