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I want to run my spark Job in Hadoop YARN cluster mode, and I am using the following command:
spark-submit --master yarn-cluster
--driver-memory 1g
--executor-memory 1g
--executor-cores 1
--class com.dc.analysis.jobs.AggregationJob
sparkanalitic.jar param1 param2 param3
I am getting error below, kindly suggest whats going wrong, is the command correct or not. I am using CDH 5.3.1.
Diagnostics: Application application_1424284032717_0066 failed 2 times due
to AM Container for appattempt_1424284032717_0066_000002 exited with
exitCode: 15 due to: Exception from container-launch.
Container id: container_1424284032717_0066_02_000001
Exit code: 15
Stack trace: ExitCodeException exitCode=15:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:538)
at org.apache.hadoop.util.Shell.run(Shell.java:455)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:702)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:197)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:299)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:81)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 15
.Failing this attempt.. Failing the application.
ApplicationMaster host: N/A
ApplicationMaster RPC port: -1
queue: root.hdfs
start time: 1424699723648
final status: FAILED
tracking URL: http://myhostname:8088/cluster/app/application_1424284032717_0066
user: hdfs
2015-02-23 19:26:04 DEBUG Client - stopping client from cache: org.apache.hadoop.ipc.Client@4085f1ac
2015-02-23 19:26:04 DEBUG Utils - Shutdown hook called
2015-02-23 19:26:05 DEBUG Utils - Shutdown hook called
Any help would be greatly appreciated.
826
Once connected, Spark acquires executors on nodes in the cluster, which are processes that run computations and store data for your application. Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to the executors. Finally, SparkContext sends tasks to the executors to run.
Based on the resource manager, the spark can run in two modes: Local Mode and cluster mode. The way we specify the resource manager is by the way of a command-line option called --master.
It can mean a lot of things, for us, we get the similar error message because of unsupported Java class version, and we fixed the problem by deleting the referenced Java class in our project.
Use this command to see the detailed error message:
yarn logs -applicationId application_1424284032717_0066
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