I found a warn in nodemanager log. is the virtual memory exceed? how
should I config yarn to solve this problem?
2016-10-21 10:41:12,588 INFO
org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl:
Memory usage of ProcessTree 20299 for container-id
container_1477017445921_0001_02_000001: 335.1 MB of 1 GB physical
memory used; 2.2 GB of 2.1 GB virtual memory used
2016-10-21 10:41:12,589 WARN
org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl:
Process tree for container: container_1477017445921_0001_02_000001 has
processes older than 1 iteration running over the configured limit.
Limit=2254857728, current usage = 2338873344
On Fri, Oct 21, 2016 at 8:49 AM, Saisai Shao <sai.sai.s...@gmail.com> wrote:
> It is not Spark has difficulty to communicate with YARN, it simply means AM
> is exited with FINISHED state.
>
> I'm guessing it might be related to memory constraints for container, please
> check the yarn RM and NM logs to find out more details.
>
> Thanks
> Saisai
>
> On Fri, Oct 21, 2016 at 8:14 AM, Xi Shen <davidshe...@gmail.com> wrote:
>>
>> 16/10/20 18:12:14 ERROR cluster.YarnClientSchedulerBackend: Yarn
>> application has already exited with state FINISHED!
>>
>> From this, I think it is spark has difficult communicating with YARN. You
>> should check your Spark log.
>>
>>
>> On Fri, Oct 21, 2016 at 8:06 AM Li Li <fancye...@gmail.com> wrote:
>>>
>>> which log file should I
>>>
>>> On Thu, Oct 20, 2016 at 10:02 PM, Saisai Shao <sai.sai.s...@gmail.com>
>>> wrote:
>>> > Looks like ApplicationMaster is killed by SIGTERM.
>>> >
>>> > 16/10/20 18:12:04 ERROR yarn.ApplicationMaster: RECEIVED SIGNAL TERM
>>> > 16/10/20 18:12:04 INFO yarn.ApplicationMaster: Final app status:
>>> >
>>> > This container may be killed by yarn NodeManager or other processes,
>>> > you'd
>>> > better check yarn log to dig out more details.
>>> >
>>> > Thanks
>>> > Saisai
>>> >
>>> > On Thu, Oct 20, 2016 at 6:51 PM, Li Li <fancye...@gmail.com> wrote:
>>> >>
>>> >> I am setting up a small yarn/spark cluster. hadoop/yarn version is
>>> >> 2.7.3 and I can run wordcount map-reduce correctly in yarn.
>>> >> And I am using spark-2.0.1-bin-hadoop2.7 using command:
>>> >> ~/spark-2.0.1-bin-hadoop2.7$ ./bin/spark-submit --class
>>> >> org.apache.spark.examples.SparkPi --master yarn-client
>>> >> examples/jars/spark-examples_2.11-2.0.1.jar 10000
>>> >> it fails and the first error is:
>>> >> 16/10/20 18:12:03 INFO storage.BlockManagerMaster: Registered
>>> >> BlockManager BlockManagerId(driver, 10.161.219.189, 39161)
>>> >> 16/10/20 18:12:03 INFO handler.ContextHandler: Started
>>> >> o.s.j.s.ServletContextHandler@76ad6715{/metrics/json,null,AVAILABLE}
>>> >> 16/10/20 18:12:12 INFO
>>> >> cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster
>>> >> registered as NettyRpcEndpointRef(null)
>>> >> 16/10/20 18:12:12 INFO cluster.YarnClientSchedulerBackend: Add WebUI
>>> >> Filter. org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter,
>>> >> Map(PROXY_HOSTS -> ai-hz1-spark1, PROXY_URI_BASES ->
>>> >> http://ai-hz1-spark1:8088/proxy/application_1476957324184_0002),
>>> >> /proxy/application_1476957324184_0002
>>> >> 16/10/20 18:12:12 INFO ui.JettyUtils: Adding filter:
>>> >> org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
>>> >> 16/10/20 18:12:12 INFO cluster.YarnClientSchedulerBackend:
>>> >> SchedulerBackend is ready for scheduling beginning after waiting
>>> >> maxRegisteredResourcesWaitingTime: 30000(ms)
>>> >> 16/10/20 18:12:12 WARN spark.SparkContext: Use an existing
>>> >> SparkContext, some configuration may not take effect.
>>> >> 16/10/20 18:12:12 INFO handler.ContextHandler: Started
>>> >> o.s.j.s.ServletContextHandler@489091bd{/SQL,null,AVAILABLE}
>>> >> 16/10/20 18:12:12 INFO handler.ContextHandler: Started
>>> >> o.s.j.s.ServletContextHandler@1de9b505{/SQL/json,null,AVAILABLE}
>>> >> 16/10/20 18:12:12 INFO handler.ContextHandler: Started
>>> >> o.s.j.s.ServletContextHandler@378f002a{/SQL/execution,null,AVAILABLE}
>>> >> 16/10/20 18:12:12 INFO handler.ContextHandler: Started
>>> >>
>>> >> o.s.j.s.ServletContextHandler@2cc75074{/SQL/execution/json,null,AVAILABLE}
>>> >> 16/10/20 18:12:12 INFO handler.ContextHandler: Started
>>> >> o.s.j.s.ServletContextHandler@2d64160c{/static/sql,null,AVAILABLE}
>>> >> 16/10/20 18:12:12 INFO internal.SharedState: Warehouse path is
>>> >> '/home/hadoop/spark-2.0.1-bin-hadoop2.7/spark-warehouse'.
>>> >> 16/10/20 18:12:13 INFO spark.SparkContext: Starting job: reduce at
>>> >> SparkPi.scala:38
>>> >> 16/10/20 18:12:13 INFO scheduler.DAGScheduler: Got job 0 (reduce at
>>> >> SparkPi.scala:38) with 10000 output partitions
>>> >> 16/10/20 18:12:13 INFO scheduler.DAGScheduler: Final stage:
>>> >> ResultStage 0 (reduce at SparkPi.scala:38)
>>> >> 16/10/20 18:12:13 INFO scheduler.DAGScheduler: Parents of final stage:
>>> >> List()
>>> >> 16/10/20 18:12:13 INFO scheduler.DAGScheduler: Missing parents: List()
>>> >> 16/10/20 18:12:13 INFO scheduler.DAGScheduler: Submitting ResultStage
>>> >> 0 (MapPartitionsRDD[1] at map at SparkPi.scala:34), which has no
>>> >> missing parents
>>> >> 16/10/20 18:12:13 INFO memory.MemoryStore: Block broadcast_0 stored as
>>> >> values in memory (estimated size 1832.0 B, free 366.3 MB)
>>> >> 16/10/20 18:12:13 INFO memory.MemoryStore: Block broadcast_0_piece0
>>> >> stored as bytes in memory (estimated size 1169.0 B, free 366.3 MB)
>>> >> 16/10/20 18:12:13 INFO storage.BlockManagerInfo: Added
>>> >> broadcast_0_piece0 in memory on 10.161.219.189:39161 (size: 1169.0 B,
>>> >> free: 366.3 MB)
>>> >> 16/10/20 18:12:13 INFO spark.SparkContext: Created broadcast 0 from
>>> >> broadcast at DAGScheduler.scala:1012
>>> >> 16/10/20 18:12:13 INFO scheduler.DAGScheduler: Submitting 10000
>>> >> missing tasks from ResultStage 0 (MapPartitionsRDD[1] at map at
>>> >> SparkPi.scala:34)
>>> >> 16/10/20 18:12:13 INFO cluster.YarnScheduler: Adding task set 0.0 with
>>> >> 10000 tasks
>>> >> 16/10/20 18:12:14 ERROR cluster.YarnClientSchedulerBackend: Yarn
>>> >> application has already exited with state FINISHED!
>>> >> 16/10/20 18:12:14 INFO server.ServerConnector: Stopped
>>> >> ServerConnector@389adf1d{HTTP/1.1}{0.0.0.0:4040}
>>> >> 16/10/20 18:12:14 INFO handler.ContextHandler: Stopped
>>> >>
>>> >> o.s.j.s.ServletContextHandler@841e575{/stages/stage/kill,null,UNAVAILABLE}
>>> >> 16/10/20 18:12:14 INFO handler.ContextHandler: Stopped
>>> >> o.s.j.s.ServletContextHandler@66629f63{/api,null,UNAVAILABLE}
>>> >> 16/10/20 18:12:14 INFO handler.ContextHandler: Stopped
>>> >> o.s.j.s.ServletContextHandler@2b62442c{/,null,UNAVAILABLE}
>>> >>
>>> >>
>>> >> I also use yarn log to get logs from yarn(total log is very lengthy in
>>> >> attachement):
>>> >> 16/10/20 18:12:03 INFO yarn.ExecutorRunnable:
>>> >>
>>> >>
>>> >> ===============================================================================
>>> >> YARN executor launch context:
>>> >> env:
>>> >> CLASSPATH ->
>>> >>
>>> >>
>>> >> {{PWD}}<CPS>{{PWD}}/__spark_conf__<CPS>{{PWD}}/__spark_libs__/*<CPS>$HADOOP_CONF_DIR<CPS>$HADOOP_COMMON_HOME/share/hadoop/common/*<CPS>$HADOOP_COMMON_HOME/share/hadoop/common/lib/*<CPS>$HADOOP_HDFS_HOME/share/hadoop/hdfs/*<CPS>$HADOOP_HDFS_HOME/share/hadoop/hdfs/lib/*<CPS>$HADOOP_YARN_HOME/share/hadoop/yarn/*<CPS>$HADOOP_YARN_HOME/share/hadoop/yarn/lib/*<CPS>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*<CPS>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*
>>> >> SPARK_LOG_URL_STDERR ->
>>> >>
>>> >>
>>> >> http://ai-hz1-spark3:8042/node/containerlogs/container_1476957324184_0002_01_000003/hadoop/stderr?start=-4096
>>> >> SPARK_YARN_STAGING_DIR ->
>>> >>
>>> >>
>>> >> hdfs://ai-hz1-spark1/user/hadoop/.sparkStaging/application_1476957324184_0002
>>> >> SPARK_USER -> hadoop
>>> >> SPARK_YARN_MODE -> true
>>> >> SPARK_LOG_URL_STDOUT ->
>>> >>
>>> >>
>>> >> http://ai-hz1-spark3:8042/node/containerlogs/container_1476957324184_0002_01_000003/hadoop/stdout?start=-4096
>>> >>
>>> >> command:
>>> >> {{JAVA_HOME}}/bin/java -server -Xmx1024m
>>> >> -Djava.io.tmpdir={{PWD}}/tmp '-Dspark.driver.port=60657'
>>> >> -Dspark.yarn.app.container.log.dir=<LOG_DIR>
>>> >> -XX:OnOutOfMemoryError='kill %p'
>>> >> org.apache.spark.executor.CoarseGrainedExecutorBackend --driver-url
>>> >> spark://CoarseGrainedScheduler@10.161.219.189:60657 --executor-id 2
>>> >> --hostname ai-hz1-spark3 --cores 1 --app-id
>>> >> application_1476957324184_0002 --user-class-path file:$PWD/__app__.jar
>>> >> 1> <LOG_DIR>/stdout 2> <LOG_DIR>/stderr
>>> >>
>>> >>
>>> >> ===============================================================================
>>> >>
>>> >> 16/10/20 18:12:03 INFO impl.ContainerManagementProtocolProxy: Opening
>>> >> proxy : ai-hz1-spark5:55857
>>> >> 16/10/20 18:12:03 INFO impl.ContainerManagementProtocolProxy: Opening
>>> >> proxy : ai-hz1-spark3:51061
>>> >> 16/10/20 18:12:04 ERROR yarn.ApplicationMaster: RECEIVED SIGNAL TERM
>>> >> 16/10/20 18:12:04 INFO yarn.ApplicationMaster: Final app status:
>>> >> UNDEFINED, exitCode: 16, (reason: Shutdown hook called before final
>>> >> status was reported.)
>>> >> 16/10/20 18:12:04 INFO util.ShutdownHookManager: Shutdown hook called
>>> >>
>>> >>
>>> >> ---------------------------------------------------------------------
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>>> >
>>> >
>>>
>>> ---------------------------------------------------------------------
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>>>
>> --
>>
>>
>> Thanks,
>> David S.
>
>
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