>>> *15/12/16 10:22:01 WARN cluster.YarnScheduler: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources*
That means you don't have resources for your application, please check your hadoop web ui. On Wed, Dec 16, 2015 at 10:32 AM, zml张明磊 <mingleizh...@ctrip.com> wrote: > Yesterday night, I run the jar on my pseudo-distributed mode without WARN > and ERROR. However, Today, Getting the WARN and directly leading to the > ERROR below. My computer memory is 8GB and I think it’s not the issue as > the LOG WARN describe. What ‘s wrong ? The code haven’t change yet. And the > environment haven’t change too. So Strange. Can anybody help me ? Why ……. > > > > Thanks. > > Minglei. > > > > Here is the submit job script > > > > /bin/spark-submit --master local[*] --driver-memory 8g --executor-memory > 8g --class com.ctrip.ml.client.Client > /root/di-ml-tool/target/di-ml-tool-1.0-SNAPSHOT.jar > > > > Error below > > *15/12/16 10:22:01 WARN cluster.YarnScheduler: Initial job has not > accepted any resources; check your cluster UI to ensure that workers are > registered and have sufficient resources* > > 15/12/16 10:22:04 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: > ApplicationMaster has disassociated: 10.32.3.21:48311 > > 15/12/16 10:22:04 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: > ApplicationMaster has disassociated: 10.32.3.21:48311 > > 15/12/16 10:22:04 WARN remote.ReliableDeliverySupervisor: Association with > remote system [akka.tcp://sparkYarnAM@10.32.3.21:48311] has failed, > address is now gated for [5000] ms. Reason is: [Disassociated]. > > *15/12/16 10:22:04 ERROR cluster.YarnClientSchedulerBackend: Yarn > application has already exited with state FINISHED!* > > > > Exception in thread "main" 15/12/16 10:22:04 INFO > cluster.YarnClientSchedulerBackend: Shutting down all executors > > Exception in thread "Yarn application state monitor" > org.apache.spark.SparkException: Error asking standalone scheduler to shut > down executors > > at > org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:261) > > at > org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stop(CoarseGrainedSchedulerBackend.scala:266) > > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.stop(YarnClientSchedulerBackend.scala:158) > > at > org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:416) > > at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1411) > > at org.apache.spark.SparkContext.stop(SparkContext.scala:1644) > > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$$anon$1.run(YarnClientSchedulerBackend.scala:139) > > Caused by: java.lang.InterruptedException > > at > java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1325) > > at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208) > > at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218) > > at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) > > at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107) > > at > scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53) > > at scala.concurrent.Await$.result(package.scala:107) > > at > org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:102) > > at > org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:78) > > at > org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:257) > > > -- Best Regards Jeff Zhang
