yarn application accepted but not running cloudera despite resource allocation

Question

I am using a Cloudera quickstart VM 5.13.0.0 to run Spark applications in yarn-client mode. I have allocated 10GB and 3 cores to my Cloudera VM. When I submit the application, the application is ACCEPTED but never moves on to RUNNING. When I try to look for logs using yarn logs -applicationId I do not see anything. Its absolutely blank.

I have looked up this issue on:

• here
• here
• here
• here
• here
• here
• here

I have practically meddled with all the configs that these links see a problem with. I still do not have an answer to my problem which on the face of it looks like the ones in the links above. Here are the config parameters of my cloudera cluster:

mapreduce.map.memory.mb 128M
mapreduce.reduce.memory.mb 128M
mapreduce.job.heap.memory-mb.ratio 0.8
yarn.nodemanager.resource.memory-mb 1900M
yarn.nodemanager.resource.percentage-physical-cpu-limit 100
yarn.nodemanager.resource.cpu-vcores 1
yarn.scheduler.minimum-allocation-mb 1M
yarn.scheduler.increment-allocation-mb 100M
yarn.scheduler.maximum-allocation-mb 1600M
yarn.scheduler.minimum-allocation-vcores 1
yarn.scheduler.increment-allocation-vcores 1
yarn.scheduler.maximum-allocation-vcores 2
yarn.scheduler.fair.continuous-scheduling-enabled unchecked
mapreduce.am.max-attempts 1
yarn.resourcemanager.am.max-retries, yarn.resourcemanager.am.max-attempts 1
yarn.app.mapreduce.am.resource.mb 1G
yarn.app.mapreduce.am.resource.cpu-vcores 1
ApplicationMaster Java Maximum Heap Size 512M
yarn.resourcemanager.scheduler.class org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler
yarn.scheduler.fair.user-as-default-queue unchecked
yarn.scheduler.fair.preemption unchecked
yarn.scheduler.fair.preemption.cluster-utilization-threshold 0.8
yarn.scheduler.fair.sizebasedweight unchecked
Fair Scheduler Allocations (deployed) {"defaultFairSharePreemptionThreshold":null,"defaultFairSharePreemptionTimeout":null,"defaultMinSharePreemptionTimeout":null,"defaultQueueSchedulingPolicy":"drf","queueMaxAMShareDefault":-1.0,"queueMaxAppsDefault":null,"queuePlacementRules":[{"create":true,"name":"specified","queue":null,"rules":null},{"create":null,"name":"nestedUserQueue","queue":null,"rules":[{"create":true,"name":"default","queue":"users","rules":null}]},{"create":null,"name":"default","queue":null,"rules":null}],"queues":[{"aclAdministerApps":null,"aclSubmitApps":null,"allowPreemptionFrom":null,"fairSharePreemptionThreshold":null,"fairSharePreemptionTimeout":null,"minSharePreemptionTimeout":null,"name":"root","queues":[{"aclAdministerApps":null,"aclSubmitApps":null,"allowPreemptionFrom":null,"fairSharePreemptionThreshold":null,"fairSharePreemptionTimeout":null,"minSharePreemptionTimeout":null,"name":"default","queues":[],"schedulablePropertiesList":[{"impalaDefaultQueryMemLimit":null,"impalaDefaultQueryOptions":null,"impalaMaxMemory":null,"impalaMaxQueuedQueries":null,"impalaMaxRunningQueries":null,"impalaQueueTimeout":null,"maxAMShare":-1.0,"maxChildResources":null,"maxResources":null,"maxRunningApps":null,"minResources":null,"scheduleName":"default","weight":1.0}],"schedulingPolicy":"drf","type":null},{"aclAdministerApps":null,"aclSubmitApps":null,"allowPreemptionFrom":null,"fairSharePreemptionThreshold":null,"fairSharePreemptionTimeout":null,"minSharePreemptionTimeout":null,"name":"users","queues":[],"schedulablePropertiesList":[{"impalaDefaultQueryMemLimit":null,"impalaDefaultQueryOptions":null,"impalaMaxMemory":null,"impalaMaxQueuedQueries":null,"impalaMaxRunningQueries":null,"impalaQueueTimeout":null,"maxAMShare":-1.0,"maxChildResources":null,"maxResources":null,"maxRunningApps":null,"minResources":null,"scheduleName":"default","weight":1.0}],"schedulingPolicy":"drf","type":"parent"}],"schedulablePropertiesList":[{"impalaDefaultQueryMemLimit":null,"impalaDefaultQueryOptions":null,"impalaMaxMemory":null,"impalaMaxQueuedQueries":null,"impalaMaxRunningQueries":null,"impalaQueueTimeout":null,"maxAMShare":null,"maxChildResources":null,"maxResources":null,"maxRunningApps":null,"minResources":null,"scheduleName":"default","weight":1.0}],"schedulingPolicy":"drf","type":null}],"userMaxAppsDefault":1,"users":[]}

Here is what the queue description looks like when the application is still in ACCEPTED state:

Likewise, here is the record from the Yarn RM UI (Note that the resources are allocated (memory/cpu) and Running Containers shows 1 container running):

Here is the Application Summary:

Here are the application logs (empty):

And, lastly, here is what the driver sees:

enter code here19/12/26 00:16:42 INFO Client: 
 client token: N/A
 diagnostics: Application application_1577297544619_0002 failed 1 times due to AM Container for appattempt_1577297544619_0002_000001 exited with  exitCode: 10
 For more detailed output, check application tracking page:http://quickstart.cloudera:8088/proxy/application_1577297544619_0002/Then, click on links to logs of each attempt.
 Diagnostics: Exception from container-launch.
 Container id: container_1577297544619_0002_01_000001
 Exit code: 10
 Stack trace: ExitCodeException exitCode=10: 
at org.apache.hadoop.util.Shell.runCommand(Shell.java:604)
at org.apache.hadoop.util.Shell.run(Shell.java:507)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:789)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:213)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)


Container exited with a non-zero exit code 10
Failing this attempt. Failing the application.
 ApplicationMaster host: N/A
 ApplicationMaster RPC port: -1
 queue: root.default
 start time: 1577299469533
 final status: FAILED
 tracking URL: http://quickstart.cloudera:8088/cluster/app/application_1577297544619_0002
 user: shepanch
19/12/26 00:16:42 ERROR SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master.
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:85)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:62)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:165)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:512)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2511)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
at cloudera.jobs.ClouderaSampleJob$.delayedEndpoint$cloudera$jobs$ClouderaSampleJob$1(ClouderaSampleJob.scala:17)
at cloudera.jobs.ClouderaSampleJob$delayedInit$body.apply(ClouderaSampleJob.scala:6)
at scala.Function0$class.apply$mcV$sp(Function0.scala:34)
at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
at scala.App$$anonfun$main$1.apply(App.scala:76)
at scala.App$$anonfun$main$1.apply(App.scala:76)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:35)
at scala.App$class.main(App.scala:76)
at cloudera.jobs.ClouderaSampleJob$.main(ClouderaSampleJob.scala:6)
at cloudera.jobs.ClouderaSampleJob.main(ClouderaSampleJob.scala)

Is there anything that can be done to solve this issue?

Answer 1

After all the research and apart from the reasons mentioned in the links I have mentioned in the question, I found that this can happen due to various reasons:

1. when you have different versions of spark in the client (driver) and the cluster. Once you ensure that both bundle the same version of spark, it runs fine.
2. you might need to mention the property spark.driver.host. Make sure the IP passed in here can be pinged from the guest VM.