带 Yarn 的 Spark Shell - 错误：Yarn 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

Question

作为这个问题的后续，当我尝试在我的单节点机器上使用 Spark 2.1.1 over Yarn (Hadoop 2.8.0) 时，我遇到了一个新错误。如果我启动 Spark Shell

spark-shell

它开始没有问题。在使用通常的start-dfs.shand启动 Hadoop 之后start-yarn.sh，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，所以我真的不知道去哪里寻找解决方案。我尝试了这里建议的内容（这看起来是一个类似的问题，因为我使用的是 Java 8），但没有任何效果。我也尝试使用 Java 7（将JAVA_HOME变量设置为JDK 7 安装文件夹），但我仍然遇到相同的错误。你有什么想法？

Answer 1

我已经设法通过使用与 Liming Cen 对此类似问题的答案所描述的方法或多或少相同的方法来解决该问题。

唯一的区别是我将中包含的所有JAR添加到我的 HDFS 中$SPARK_HOME/libexec/jars，并压缩在 zip 文件中。

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

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