yes, when I use yarn-cluster mode, it's correct. What's wrong with yarn-client? the spark shell is also not work because it's client mode. Any solution for this?
On Thu, Oct 20, 2016 at 11:32 PM, Amit Tank <amittankopensou...@gmail.com> wrote: > I recently started learning spark so I may be completely wrong here but I > was facing similar problem with sparkpi on yarn. After changing yarn to > cluster mode it worked perfectly fine. > > Thank you, > Amit > > > On Thursday, October 20, 2016, 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 >>> >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >> >> > --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org