Spark Shell with Yarn - 错误：纱线应用程序已经结束！它可能已被杀死或无法启动应用程序主机

时间：2017-06-10 10:05:41

标签： hadoop apache-spark yarn

作为this问题的后续问题，当我尝试在单节点计算机上使用Spark 2.1.1 over Yarn（Hadoop 2.8.0）时，我收到了新错误。如果我用

启动Spark Shell 
spark-shell

它开始没有问题。在使用通常的start-dfs.sh和start-yarn.sh启动Hadoop后，如果我使用

spark-shell --master yarn

我收到以下错误：

17/06/10 12:00:07 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/06/10 12:00:12 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:156)
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
    at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2320)
    at org.apache.spark.sql.SparkSession$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:96)
    at $line3.$read$$iw$$iw.<init>(<console>:15)
    at $line3.$read$$iw.<init>(<console>:42)
    at $line3.$read.<init>(<console>:44)
    at $line3.$read$.<init>(<console>:48)
    at $line3.$read$.<clinit>(<console>)
    at $line3.$eval$.$print$lzycompute(<console>:7)
    at $line3.$eval$.$print(<console>:6)
    at $line3.$eval.$print(<console>)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:786)
    at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1047)
    at scala.tools.nsc.interpreter.IMain$WrappedRequest$$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$$anonfun$process$1.apply(ILoop.scala:909)
    at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)
    at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)
    at org.apache.spark.repl.Main$.doMain(Main.scala:69)
    at org.apache.spark.repl.Main$.main(Main.scala:52)
    at org.apache.spark.repl.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:743)
    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$.main(SparkSubmit.scala:126)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
17/06/10 12:00:12 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
17/06/10 12:00:12 WARN MetricsSystem: Stopping a MetricsSystem that is not running

我是Spark / Yarn的新手，所以我不知道在哪里寻找解决方案。我尝试了所建议的here（看起来像一个类似的问题，因为我使用的是Java 8），但它没有任何效果。我也尝试使用Java 7（将JAVA_HOME变量设置为JDK 7安装文件夹），但我仍然遇到了同样的错误。你有什么想法吗？

4 个答案:

答案 0 :(得分：3)

我设法通过使用Liming Cen对this similar question的答案所描述的相同方法来解决问题。

唯一的区别是我添加到我的HDFS 所有 $SPARK_HOME/libexec/jars中包含的JAR，压缩成zip文件。

在$SPARK_HOME/libexec/conf/spark-defaults.conf我添加了以下行：

spark.yarn.archive=hdfs:///user/MY_USERNAME/spark-archive.zip

答案 1 :(得分：0)

这可能是因为没有为所有YARN节点正确安装/配置java 1.8 ...如果你使用的是Cloudera，你必须确保属性＆＃34; Java Home Directory＆＃34 ;已正确配置内部所有主机＆＃34;配置＆＃34;特定主机的选项卡。 （例如/usr/lib/jvm/jdk1.8.0_144）

答案 2 :(得分：0)

对我来说，一些HDFS库不见了， 因此，在YARN NodeManager VM上安装hadoop-hdfs和hadoop-client软件包后，问题得到解决

yum -y install hadoop-client.x86_64 hadoop-hdfs.x86_64

答案 3 :(得分：-1)

你可以使用--master-yarn在纱线上运行spark-shell。但是，要运行spark-shell，您必须使用部署模式客户端＆＃39;因为驱动程序将在客户端运行以防火花壳。试试这个，如果你收到一些错误就发布

./bin/spark-shell --master yarn --deploy-mode client
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