I'm experimenting the same issue with spark 1.3.1 I verified that hadoop works (ie: running hadoop's pi example)
It seems like hadoop conf is in the classpath (/opt/test/service/hadoop/etc/hadoop ) SPARK_PRINT_LAUNCH_COMMAND=1 ./bin/spark-shell --master yarn-client Spark Command: /usr/lib/jvm/jre/bin/java -cp /opt/test/service/spark/conf:/opt/test/service/spark/assembly/target/scala-2.11/spark-assembly-1.3.1-hadoop2.6.0.jar:/opt/test/service/spark/lib_managed/jars/datanucleus-core-3.2.10.jar:/opt/test/service/spark/lib_managed/jars/datanucleus-rdbms-3.2.9.jar:/opt/test/service/spark/lib_managed/jars/datanucleus-api-jdo-3.2.6.jar:/opt/test/service/hadoop/etc/hadoop -XX:MaxPermSize=128m -Dscala.usejavacp=true -Xms512m -Xmx512m org.apache.spark.deploy.SparkSubmit --class org.apache.spark.repl.Main --master yarn-client spark-shell .... 15/04/20 19:39:11 INFO yarn.Client: client token: N/A diagnostics: N/A ApplicationMaster host: N/A ApplicationMaster RPC port: -1 queue: default start time: 1429558750744 final status: UNDEFINED tracking URL: http://namenode-01.test.xxx.com:8088/proxy/application_1429543348669_0014/ user: nobody .... I do have hadoop running in HA mode. and when I go to Hadoop logs I also see 15/04/20 19:39:15 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8030. Already tried 0 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS) ... could it be the same? On Wed, Apr 15, 2015 at 4:58 AM, Vineet Mishra <clearmido...@gmail.com> wrote: > Hi Akhil, > > Its running fine when running through Namenode(RM) but fails while running > through Gateway, if I add hadoop-core jars to the hadoop > directory(/opt/cloudera/parcels/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hadoop/) it > works fine. > > Its really strange that I am running the job through Spark-Submit and > running via NameNode works fine and it fails when running through gateway > even when both are having same classpath. > > Anyone tries running Spark from Gateway? > > Looking for the quick revert! > > Thanks, > > > On Wed, Apr 15, 2015 at 12:07 PM, Akhil Das <ak...@sigmoidanalytics.com> > wrote: > >> Make sure your yarn service is running on 8032. >> >> Thanks >> Best Regards >> >> On Tue, Apr 14, 2015 at 12:35 PM, Vineet Mishra <clearmido...@gmail.com> >> wrote: >> >>> Hi Team, >>> >>> I am running Spark Word Count example( >>> https://github.com/sryza/simplesparkapp), if I go with master as local >>> it works fine. >>> >>> But when I change the master to yarn its end with retries connecting to >>> resource manager(stack trace mentioned below), >>> >>> 15/04/14 11:31:57 INFO RMProxy: Connecting to ResourceManager at / >>> 0.0.0.0:8032 >>> 15/04/14 11:31:58 INFO Client: Retrying connect to server: >>> 0.0.0.0/0.0.0.0:8032. Already tried 0 time(s); retry policy is >>> RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 >>> MILLISECONDS) >>> 15/04/14 11:31:59 INFO Client: Retrying connect to server: >>> 0.0.0.0/0.0.0.0:8032. Already tried 1 time(s); retry policy is >>> RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 >>> MILLISECONDS) >>> >>> If I run the same command from Namenode instance it ends with >>> ArrayOutofBoundException(Stack trace mentioned below), >>> >>> 15/04/14 11:38:44 INFO YarnClientSchedulerBackend: SchedulerBackend is >>> ready for scheduling beginning after reached minRegisteredResourcesRatio: >>> 0.8 >>> Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 1 >>> at >>> com.cloudera.sparkwordcount.SparkWordCount$.main(SparkWordCount.scala:28) >>> at com.cloudera.sparkwordcount.SparkWordCount.main(SparkWordCount.scala) >>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>> at >>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>> at >>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>> at java.lang.reflect.Method.invoke(Method.java:606) >>> at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358) >>> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75) >>> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) >>> >>> Looking forward to get it resolve to work on respective nodes. >>> >>> Thanks, >>> >> >> >
