Apache Spark 在 YARN 错误上运行 spark-shell

【问题标题】：Apache Spark running spark-shell on YARN errorApache Spark 在 YARN 错误上运行 spark-shell
【发布时间】：2017-09-01 22:27:51
【问题描述】：

我从http://spark.apache.org/downloads.html 下载了：spark-2.1.0-bin-hadoop2.7.tgz。我的 Hadoop HDFS 和 YARN 以 $ start-dfs.sh 和 $ start-yarn.sh 开头。但是运行$ spark-shell --master yarn --deploy-mode client 会给我以下错误：

    $ 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 检测到 Spark 正在与它一起运行，但错误导致 Spark 以未定义状态退出。

【问题讨论】：

  • Spark 不需要 HDFS 或 YARN，除非您专门对其进行了配置
  • @cricket_007 我很确定我没有专门配置任何东西来让我们使用 YARN。我遵循的教程设置了配置文件，然后需要指定标志以在 YARN 上运行 spark。这是我遵循的教程，我尝试了不同的配置，但仍然无法正常工作：why-not-learn-something.blogspot.com/2015/06/…
  • Spark 1.3 太旧了...为什么需要 YARN 或 HDFS（或者根本不需要 hadoop）
  • @cricket_007 我已经下载了新的 spark tar 文件。提取它，如果不首先启动 $start-dfs.sh，我将无法运行 $spark-shell 而不会出错。这意味着，至少较新的 spark 需要 HDFS。我没有看到任何其他原因。
  • HDFS 仅用于从 HDFS 读取。只有在 YARN 上运行 Spark 时才需要 YARN。如果没有这两者，您可以使用内置的独立调度程序在本地运行 Spark，该调度程序从本地文件系统读取。你应该试试spark-2.1.0-bin-without-hadoop.tgz

标签： hadoop apache-spark hdfs hadoop-yarn


【解决方案1】：

我从另一个 Stackoverflow 问题中找到了解决方案。这不是关于配置 Apache Spark，而是关于配置 Hadoop YARN：

Running yarn with spark not working with Java 8

确保 Hadoop 配置文件夹中的 yarn-site.xml 具有以下属性：

<property>
    <name>yarn.nodemanager.pmem-check-enabled</name>
    <value>false</value>
</property>

<property>
    <name>yarn.nodemanager.vmem-check-enabled</name>
    <value>false</value>
</property>

【讨论】：

  • 它也对我有用。我已经努力获得解决方案近 4 个小时。你让我开心。
  • 我正在使用 HDP 2.6.5 沙箱 YARN 已经具有这些属性错误。但是 spark-shell 在尝试启动 yarn 模式时会报错。
【解决方案2】：

我遇到了同样的问题。当我检查 NodeManager 日志时，我发现这个警告：

2017-10-26 19:43:21,787 警告 org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl：容器 [pid=3820,containerID=container_1509016963775_0001_02_000001] 运行超出虚拟内存限制。当前使用情况：已使用 339.0 MB 的 1 GB 物理内存；使用了 2.2 GB 的 2.1 GB 虚拟内存。杀死容器。

所以我设置了一个更大的虚拟内存（yarn-site.xml 中的yarn.nodemanager.vmem-pmem-ratio，默认值为2.1）。然后它真的奏效了。

【讨论】：

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