Apache Spark running spark-shell on YARN error - hadoop

Question

I downloaded: spark-2.1.0-bin-hadoop2.7.tgz from http://spark.apache.org/downloads.html. I have Hadoop HDFS and YARN started with $ start-dfs.sh and $ start-yarn.sh. But running $ spark-shell --master yarn --deploy-mode client gives me the error below:
$ spark-shell --master yarn --deploy-mode client
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/04/08 23:04:54 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/04/08 23:04:54 WARN util.Utils: Your hostname, Pandora resolves to a loopback address: 127.0.1.1; using 192.168.1.11 instead (on interface wlp3s0)
17/04/08 23:04:54 WARN util.Utils: Set SPARK_LOCAL_IP if you need to bind to another address
17/04/08 23:04:56 WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
17/04/08 23:05:15 ERROR cluster.YarnClientSchedulerBackend: Yarn application has already exited with state FINISHED!
17/04/08 23:05:15 ERROR spark.SparkContext: Error initializing SparkContext.
java.lang.IllegalStateException: Spark context stopped while waiting for backend
at org.apache.spark.scheduler.TaskSchedulerImpl.waitBackendReady(TaskSchedulerImpl.scala:614)
at org.apache.spark.scheduler.TaskSchedulerImpl.postStartHook(TaskSchedulerImpl.scala:169)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:567)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2313)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:868)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:860)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:860)
at org.apache.spark.repl.Main$.createSparkSession(Main.scala:95)
at $line3.$read$$iw$$iw.<init>(<console>:15)
at $line3.$read$$iw.<init>(<console>:42)
at $line3.$read.<init>(<console>:44)
at $line3.$read$.<init>(<console>:48)
at $line3.$read$.<clinit>(<console>)
at $line3.$eval$.$print$lzycompute(<console>:7)
at $line3.$eval$.$print(<console>:6)
at $line3.$eval.$print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:786)
at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1047)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:638)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:637)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:637)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:569)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:565)
at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:807)
at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:681)
at scala.tools.nsc.interpreter.ILoop.processLine(ILoop.scala:395)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply$mcV$sp(SparkILoop.scala:38)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:214)
at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:37)
at org.apache.spark.repl.SparkILoop.loadFiles(SparkILoop.scala:105)
at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:920)
at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)
at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)
at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)
at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)
at org.apache.spark.repl.Main$.doMain(Main.scala:68)
at org.apache.spark.repl.Main$.main(Main.scala:51)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
17/04/08 23:05:15 ERROR client.TransportClient: Failed to send RPC 7918328175210939600 to /192.168.1.11:56186: java.nio.channels.ClosedChannelException
java.nio.channels.ClosedChannelException
at io.netty.channel.AbstractChannel$AbstractUnsafe.write(...)(Unknown Source)
17/04/08 23:05:15 ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map()) to AM was unsuccessful
java.io.IOException: Failed to send RPC 7918328175210939600 to /192.168.1.11:56186: java.nio.channels.ClosedChannelException
at org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:249)
at org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:233)
at io.netty.util.concurrent.DefaultPromise.notifyListener0(DefaultPromise.java:514)
at io.netty.util.concurrent.DefaultPromise.notifyListenersNow(DefaultPromise.java:488)
at io.netty.util.concurrent.DefaultPromise.access$000(DefaultPromise.java:34)
at io.netty.util.concurrent.DefaultPromise$1.run(DefaultPromise.java:438)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:408)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:455)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.nio.channels.ClosedChannelException
at io.netty.channel.AbstractChannel$AbstractUnsafe.write(...)(Unknown Source)
17/04/08 23:05:15 ERROR util.Utils: Uncaught exception in thread Yarn application state monitor
org.apache.spark.SparkException: Exception thrown in awaitResult
at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:77)
at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:75)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.PartialFunction$OrElse.apply(PartialFunction.scala:167)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:83)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.requestTotalExecutors(CoarseGrainedSchedulerBackend.scala:512)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend.stop(YarnSchedulerBackend.scala:93)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.stop(YarnClientSchedulerBackend.scala:151)
at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:467)
at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1588)
at org.apache.spark.SparkContext$$anonfun$stop$8.apply$mcV$sp(SparkContext.scala:1826)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1283)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1825)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$MonitorThread.run(YarnClientSchedulerBackend.scala:108)
Caused by: java.io.IOException: Failed to send RPC 7918328175210939600 to /192.168.1.11:56186: java.nio.channels.ClosedChannelException
at org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:249)
at org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:233)
at io.netty.util.concurrent.DefaultPromise.notifyListener0(DefaultPromise.java:514)
at io.netty.util.concurrent.DefaultPromise.notifyListenersNow(DefaultPromise.java:488)
at io.netty.util.concurrent.DefaultPromise.access$000(DefaultPromise.java:34)
at io.netty.util.concurrent.DefaultPromise$1.run(DefaultPromise.java:438)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:408)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:455)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.nio.channels.ClosedChannelException
at io.netty.channel.AbstractChannel$AbstractUnsafe.write(...)(Unknown Source)
java.lang.IllegalStateException: Spark context stopped while waiting for backend
at org.apache.spark.scheduler.TaskSchedulerImpl.waitBackendReady(TaskSchedulerImpl.scala:614)
at org.apache.spark.scheduler.TaskSchedulerImpl.postStartHook(TaskSchedulerImpl.scala:169)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:567)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2313)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:868)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:860)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:860)
at org.apache.spark.repl.Main$.createSparkSession(Main.scala:95)
... 47 elided
<console>:14: error: not found: value spark
import spark.implicits._
^
<console>:14: error: not found: value spark
import spark.sql
^
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.1.0
/_/
Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_121)
Type in expressions to have them evaluated.
Type :help for more information.
YARN detects Spark is running with it, but the error is causing Spark to exit with undefined status.

Answer 1

I found the solution from another Stackoverflow question. It was not about configuring Apache Spark, it was about configuring Hadoop YARN:
Running yarn with spark not working with Java 8
Make sure your yarn-site.xml, from your Hadoop configuration folder, has these properties:
<property>
<name>yarn.nodemanager.pmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>

Answer 2

I met the same problem with you. When I check the NodeManager log，I find this warn:
2017-10-26 19:43:21,787 WARN org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl: Container [pid=3820,containerID=container_1509016963775_0001_02_000001] is running beyond virtual memory limits. Current usage: 339.0 MB of 1 GB physical memory used; 2.2 GB of 2.1 GB virtual memory used. Killing container.
So I set a bigger virtual memory(yarn.nodemanager.vmem-pmem-ratio in yarn-site.xml, which default value is 2.1). Then it really worked.