Application report for application_ (state: ACCEPTED) never ends for Spark Submit (with Spark 1.2.0 on YARN)

Question

I am running kinesis plus spark application https://spark.apache.org/docs/1.2.0/streaming-kinesis-integration.html

I am running as below

command on ec2 instance :

 ./spark/bin/spark-submit --class org.apache.spark.examples.streaming.myclassname --master yarn-cluster --num-executors 2 --driver-memory 1g --executor-memory 1g --executor-cores 1  /home/hadoop/test.jar 

I have installed spark on EMR.

EMR details
Master instance group - 1   Running MASTER  m1.medium   
1

Core instance group - 2 Running CORE    m1.medium

I am getting below INFO and it never ends.

15/06/14 11:33:23 INFO yarn.Client: Requesting a new application from cluster with 2 NodeManagers
15/06/14 11:33:23 INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster (2048 MB per container)
15/06/14 11:33:23 INFO yarn.Client: Will allocate AM container, with 1408 MB memory including 384 MB overhead
15/06/14 11:33:23 INFO yarn.Client: Setting up container launch context for our AM
15/06/14 11:33:23 INFO yarn.Client: Preparing resources for our AM container
15/06/14 11:33:24 INFO yarn.Client: Uploading resource file:/home/hadoop/.versions/spark-1.3.1.e/lib/spark-assembly-1.3.1-hadoop2.4.0.jar -> hdfs://172.31.13.68:9000/user/hadoop/.sparkStaging/application_1434263747091_0023/spark-assembly-1.3.1-hadoop2.4.0.jar
15/06/14 11:33:29 INFO yarn.Client: Uploading resource file:/home/hadoop/test.jar -> hdfs://172.31.13.68:9000/user/hadoop/.sparkStaging/application_1434263747091_0023/test.jar
15/06/14 11:33:31 INFO yarn.Client: Setting up the launch environment for our AM container
15/06/14 11:33:31 INFO spark.SecurityManager: Changing view acls to: hadoop
15/06/14 11:33:31 INFO spark.SecurityManager: Changing modify acls to: hadoop
15/06/14 11:33:31 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop); users with modify permissions: Set(hadoop)
15/06/14 11:33:31 INFO yarn.Client: Submitting application 23 to ResourceManager
15/06/14 11:33:31 INFO impl.YarnClientImpl: Submitted application application_1434263747091_0023
15/06/14 11:33:32 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:32 INFO yarn.Client:
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: N/A
         ApplicationMaster RPC port: -1
         queue: default
         start time: 1434281611893
         final status: UNDEFINED
         tracking URL: http://172.31.13.68:9046/proxy/application_1434263747091_0023/
         user: hadoop
15/06/14 11:33:33 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:34 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:35 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:36 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:37 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:38 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:39 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:40 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)
15/06/14 11:33:41 INFO yarn.Client: Application report for application_1434263747091_0023 (state: ACCEPTED)

Could somebody please let me know as why it's not working ?

Answer 1

I had this exact problem when multiple users were trying to run on our cluster at once. The fix was to change setting of the scheduler.

In the file /etc/hadoop/conf/capacity-scheduler.xml we changed the property yarn.scheduler.capacity.maximum-am-resource-percent from 0.1 to 0.5.

Changing this setting increases the fraction of the resources that is made available to be allocated to application masters, increasing the number of masters possible to run at once and hence increasing the number of possible concurrent applications.