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。在用通常的start-dfs.sh和start-yarn.sh启动Hadoop之后，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，所以我真的不知道在哪里寻找解决方案。我尝试了这里建议的内容(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(，但我仍然收到同样的错误。你有什么想法吗？
我已经设法通过使用或多或少与岑黎明对这个类似问题的回答所描述的相同方法来解决这个问题。
唯一的区别是我在我的HDFS中添加了包含在$SPARK_HOME/libexec/jars中的所有JAR，压缩在一个zip文件中。
在$SPARK_HOME/libexec/conf/spark-defaults.conf中，我添加了以下行：
spark.yarn.archive=hdfs:///user/MY_USERNAME/spark-archive.zip
这可能是由于 java 1.8 没有为所有 YARN 节点正确安装/配置......如果您使用的是Cloudera，则必须确保为特定HOST的"配置"选项卡中的所有主机正确配置属性"Java主目录"。(例如/usr/lib/jvm/jdk1.8.0_144(
对我来说，YARN 节点管理器节点上缺少一些 HDFS 库，因此，在YARN NodeManager VM上安装hadoop-hdfs和hadoop-client软件包后，问题得到了解决
yum -y install hadoop-client.x86_64 hadoop-hdfs.x86_64
你肯定可以用 --master-yarn 在纱线上运行火花壳。但是，要运行 spark-shell，您必须使用"部署模式客户端"，因为在 spark shell 的情况下，驱动程序将在客户端上运行。试试这个，如果你遇到一些错误，请发布
./bin/spark-shell --master yarn --deploy-mode client