Created 08-15-2016 12:28 PM
Hi All,
I am using hdp sandbox 2.3.4.
I have created one oozie job and I am submitting spark job on yarn-cluster (--master yarn-cluster).
workflow.xml looks as below
<workflow-app name="sample" xmlns="uri:oozie:workflow:0.1"> <start to="spark-action" /> <action name="spark-action"> <spark xmlns="uri:oozie:spark-action:0.1"> <job-tracker>${jobTracker}</job-tracker> <name-node>${nameNode}</name-node> <master>${master}</master> <name>${csvProcessingJobName}</name> <class>${csvProcessingJobClass}</class> <jar>${jarName}</jar> <arg>${csvProcessingArg1}</arg> <arg>${csvProcessingArg2}</arg> <arg>${csvProcessingArg3}</arg> <arg>${csvProcessingArg4}</arg> </spark> <ok to="end" /> <error to="end" /> </action> <end name = "end" /> </workflow-app>
job.properties
############ GENERAL HDFS AND ORACLE DB CONNECTION PROPERTIES ############ jobTracker=sandbox.hortonworks.com:8050 nameNode=hdfs://sandbox.hortonworks.com:8020 ############ BUNDLE PROPERTIES ############ bundleAppName=bundle bundleKickOffTime=2016-05-04T07:00Z oozie.bundle.application.path=${nameNode}/user/root/oozie/config/spark/bundle.xml oozie.use.system.libpath=true ############ COORDINATOR PROPERTIES ############ coordinatorAppPath=${nameNode}/user/root/oozie/config/spark/coordinator.xml coordinatorAppName=csv-processing-coordinator coordinatorStartTime=2016-05-04T01:00Z coordinatorEndTime=2016-05-05T01:00Z coordinatorFrequency=1440 coordinatorTimeZone=UTC ############ WORKFLOW PROPERTIES ############ workflowAppPath=${nameNode}/user/root/oozie/config/spark/workflow.xml workflowAppName=rdbms-to-hadoop-workflow master=yarn-cluster jarName=${nameNode}/user/root/oozie/config/spark/processor.jar csvProcessingJobName=processing-job csvProcessingJobClass=com.job.SparkJob csvProcessingArg1=${nameNode}/user/root/abc.csv csvProcessingArg2=tableName csvProcessingArg3=/apps/hive/warehouse/tableName csvProcessingArg4=parquet
But I am getting below error in my mapreduce job. (checking log in job history UI) .. I am not sure what's the cause
Failing Oozie Launcher, Main class [org.apache.oozie.action.hadoop.SparkMain], main() threw exception, Call From sandbox.hortonworks.com/192.168.0.105 to 0.0.0.0:8032 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused java.net.ConnectException: Call From sandbox.hortonworks.com/192.168.0.105 to 0.0.0.0:8032 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused at sun.reflect.GeneratedConstructorAccessor10.newInstance(Unknown Source) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) at java.lang.reflect.Constructor.newInstance(Constructor.java:526) at org.apache.hadoop.net.NetUtils.wrapWithMessage(NetUtils.java:792) at org.apache.hadoop.net.NetUtils.wrapException(NetUtils.java:732) at org.apache.hadoop.ipc.Client.call(Client.java:1431) at org.apache.hadoop.ipc.Client.call(Client.java:1358) at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229) at com.sun.proxy.$Proxy15.getClusterMetrics(Unknown Source) at org.apache.hadoop.yarn.api.impl.pb.client.ApplicationClientProtocolPBClientImpl.getClusterMetrics(ApplicationClientProtocolPBClientImpl.java:206) at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:252) at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:104) at com.sun.proxy.$Proxy16.getClusterMetrics(Unknown Source) at org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.getYarnClusterMetrics(YarnClientImpl.java:501) at org.apache.spark.deploy.yarn.Client$$anonfun$submitApplication$1.apply(Client.scala:129) at org.apache.spark.deploy.yarn.Client$$anonfun$submitApplication$1.apply(Client.scala:129) at org.apache.spark.Logging$class.logInfo(Logging.scala:58) at org.apache.spark.deploy.yarn.Client.logInfo(Client.scala:62) at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:128) at org.apache.spark.deploy.yarn.Client.run(Client.scala:1065) at org.apache.spark.deploy.yarn.Client$.main(Client.scala:1125) at org.apache.spark.deploy.yarn.Client.main(Client.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731) at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181) at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) at org.apache.oozie.action.hadoop.SparkMain.runSpark(SparkMain.java:104) at org.apache.oozie.action.hadoop.SparkMain.run(SparkMain.java:95) at org.apache.oozie.action.hadoop.LauncherMain.run(LauncherMain.java:47) at org.apache.oozie.action.hadoop.SparkMain.main(SparkMain.java:38) 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.oozie.action.hadoop.LauncherMapper.map(LauncherMapper.java:241) at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:54) at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:453) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:343) at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:415) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657) at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:162) Caused by: java.net.ConnectException: Connection refused at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:744) at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206) at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:531) at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:495) at org.apache.hadoop.ipc.Client$Connection.setupConnection(Client.java:612) at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:710) at org.apache.hadoop.ipc.Client$Connection.access$2800(Client.java:373) at org.apache.hadoop.ipc.Client.getConnection(Client.java:1493) at org.apache.hadoop.ipc.Client.call(Client.java:1397) ... 45 more Oozie Launcher failed, finishing Hadoop job gracefully Oozie Launcher, uploading action data to HDFS sequence file: hdfs://sandbox.hortonworks.com:8020/user/root/oozie-oozi/0000002-160815115226550-oozie-oozi-W/csv-processing-spark-action--spark/action-data.seq Oozie Launcher ends
Any idea ?
Thanks
Created 08-15-2016 05:08 PM
Are you able to run the Spark job from the shell/command line? If so, then you may want to use Shell Action instead. Oozie Spark Action in HDP 2.3.4 is still in tech preview and not supported yet. The below tech note was released with the recommendation to use Shell Actions or Java Actions instead.
--------------------
Begin Tech Note
--------------------
Because spark action in oozie is not supported in HDP 2.3.x and HDP 2.4.0, there is no workaround especially in kerberos environment. We can use either java action or shell action to launch spark job in oozie workflow. In this article, we will discuss how to use oozie shell action to run a spark job in kerberos environment.
Prerequisite:
1. Spark client is installed on every host where nodemanager is running. This is because we have no control over which node the
2. Optionally, if the spark job need to interact with hbase cluster, hbase client need to be installed on every host as well.
Steps:
1. Create a shell script with the spark-submit command. For example, in the script.sh:
/usr/hdp/current/spark-client/bin/spark-submit --keytab keytab --principal ambari-qa-falconJ@FALCONJSECURE.COM --class org.apache.spark.examples.SparkPi --master yarn-client --driver-memory 500m --num-executors 1 --executor-memory 500m --executor-cores 1 spark-examples.jar 3
2. Prepare kerberos keytab which will be used by the spark job. For example, we use ambari smoke test user, the keytab is already generated by Ambari in/etc/security/keytabs/smokeuser.headless.keytab.
3. Create the oozie workflow with a shell action which will execute the script created above, for example, in the workflow.xml:
<workflow-app name="WorkFlowForShellAction" xmlns="uri:oozie:workflow:0.4"> <start to="shellAction"/> <action name="shellAction"> <shell xmlns="uri:oozie:shell-action:0.2"> <job-tracker>${jobTracker}</job-tracker> <name-node>${nameNode}</name-node> <exec>script.sh</exec> <file>/user/oozie/shell/script.sh#script.sh</file> <file>/user/oozie/shell/smokeuser.headless.keytab#keytab</file> <file>/user/oozie/shell/spark-examples.jar#spark-examples.jar</file> <capture-output/> </shell> <ok to="end"/> <error to="killAction"/> </action> <kill name="killAction"> <message>"Killed job due to error"</message> </kill> <end name="end"/> </workflow-app>
4. Create the oozie job properties file. For example, in job.properties:
nameNode=falconJ2.sec.support.com:8050
queueName=default
oozie.wf.application.path=${nameNode}/user/oozie/shell
oozie.use.system.libpath=true
5. Upload the following files created above to the oozie workflow application path in HDFS (In this example: /user/oozie/shell):
- workflow.xml
- smokeuser.headless.keytab
- script.sh
- spark uber jar (In this example: /usr/hdp/current/spark-client/lib/spark-examples*.jar)
- Any other configuration file mentioned in workflow (optional)
6. Execute the oozie command to run this workflow. For example:
oozie job -oozie http://<oozie-server>:11000/oozie -config job.properties -run
--------------------
End Tech Note
--------------------
See similar/related response here:
Created 08-15-2016 04:43 PM
There is a similar issue here https://community.hortonworks.com/questions/23132/i-am-getting-error-in-oozie-workflow-what-i-have-d.... Please try the recommended suggestion in that post and update the post if it worked or not.
Created 08-15-2016 05:08 PM
Are you able to run the Spark job from the shell/command line? If so, then you may want to use Shell Action instead. Oozie Spark Action in HDP 2.3.4 is still in tech preview and not supported yet. The below tech note was released with the recommendation to use Shell Actions or Java Actions instead.
--------------------
Begin Tech Note
--------------------
Because spark action in oozie is not supported in HDP 2.3.x and HDP 2.4.0, there is no workaround especially in kerberos environment. We can use either java action or shell action to launch spark job in oozie workflow. In this article, we will discuss how to use oozie shell action to run a spark job in kerberos environment.
Prerequisite:
1. Spark client is installed on every host where nodemanager is running. This is because we have no control over which node the
2. Optionally, if the spark job need to interact with hbase cluster, hbase client need to be installed on every host as well.
Steps:
1. Create a shell script with the spark-submit command. For example, in the script.sh:
/usr/hdp/current/spark-client/bin/spark-submit --keytab keytab --principal ambari-qa-falconJ@FALCONJSECURE.COM --class org.apache.spark.examples.SparkPi --master yarn-client --driver-memory 500m --num-executors 1 --executor-memory 500m --executor-cores 1 spark-examples.jar 3
2. Prepare kerberos keytab which will be used by the spark job. For example, we use ambari smoke test user, the keytab is already generated by Ambari in/etc/security/keytabs/smokeuser.headless.keytab.
3. Create the oozie workflow with a shell action which will execute the script created above, for example, in the workflow.xml:
<workflow-app name="WorkFlowForShellAction" xmlns="uri:oozie:workflow:0.4"> <start to="shellAction"/> <action name="shellAction"> <shell xmlns="uri:oozie:shell-action:0.2"> <job-tracker>${jobTracker}</job-tracker> <name-node>${nameNode}</name-node> <exec>script.sh</exec> <file>/user/oozie/shell/script.sh#script.sh</file> <file>/user/oozie/shell/smokeuser.headless.keytab#keytab</file> <file>/user/oozie/shell/spark-examples.jar#spark-examples.jar</file> <capture-output/> </shell> <ok to="end"/> <error to="killAction"/> </action> <kill name="killAction"> <message>"Killed job due to error"</message> </kill> <end name="end"/> </workflow-app>
4. Create the oozie job properties file. For example, in job.properties:
nameNode=falconJ2.sec.support.com:8050
queueName=default
oozie.wf.application.path=${nameNode}/user/oozie/shell
oozie.use.system.libpath=true
5. Upload the following files created above to the oozie workflow application path in HDFS (In this example: /user/oozie/shell):
- workflow.xml
- smokeuser.headless.keytab
- script.sh
- spark uber jar (In this example: /usr/hdp/current/spark-client/lib/spark-examples*.jar)
- Any other configuration file mentioned in workflow (optional)
6. Execute the oozie command to run this workflow. For example:
oozie job -oozie http://<oozie-server>:11000/oozie -config job.properties -run
--------------------
End Tech Note
--------------------
See similar/related response here: