在新的AWS EMR群集中无法获取SparkContext

时间：2018-11-04 12:31:04

标签： amazon-web-services apache-spark pyspark amazon-emr

我只是设置了一个AWS EMR集群（带有Spark 2.3.2的EMC版本5.18）。我进入主机并运行spark-shell或pyspark并得到以下错误：

$ spark-shell

log4j:ERROR setFile(null,true) call failed.
java.io.FileNotFoundException: /stderr (Permission denied)
        at java.io.FileOutputStream.open0(Native Method)
        at java.io.FileOutputStream.open(FileOutputStream.java:270)
        at java.io.FileOutputStream.<init>(FileOutputStream.java:213)
        at java.io.FileOutputStream.<init>(FileOutputStream.java:133)
        at org.apache.log4j.FileAppender.setFile(FileAppender.java:294)
        at org.apache.log4j.FileAppender.activateOptions(FileAppender.java:165)
        at org.apache.log4j.DailyRollingFileAppender.activateOptions(DailyRollingFileAppender.java:223)
        at org.apache.log4j.config.PropertySetter.activate(PropertySetter.java:307)
        at org.apache.log4j.config.PropertySetter.setProperties(PropertySetter.java:172)
        at org.apache.log4j.config.PropertySetter.setProperties(PropertySetter.java:104)
        at org.apache.log4j.PropertyConfigurator.parseAppender(PropertyConfigurator.java:842)
        at org.apache.log4j.PropertyConfigurator.parseCategory(PropertyConfigurator.java:768)
        at org.apache.log4j.PropertyConfigurator.parseCatsAndRenderers(PropertyConfigurator.java:672)
        at org.apache.log4j.PropertyConfigurator.doConfigure(PropertyConfigurator.java:516)
        at org.apache.log4j.PropertyConfigurator.doConfigure(PropertyConfigurator.java:580)
        at org.apache.log4j.helpers.OptionConverter.selectAndConfigure(OptionConverter.java:526)
        at org.apache.log4j.LogManager.<clinit>(LogManager.java:127)
        at org.apache.spark.internal.Logging$class.initializeLogging(Logging.scala:120)
        at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:108)
        at org.apache.spark.deploy.SparkSubmit$.initializeLogIfNecessary(SparkSubmit.scala:71)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:128)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
log4j:ERROR Either File or DatePattern options are not set for appender [DRFA-stderr].
log4j:ERROR setFile(null,true) call failed.
java.io.FileNotFoundException: /stdout (Permission denied)
        at java.io.FileOutputStream.open0(Native Method)
        at java.io.FileOutputStream.open(FileOutputStream.java:270)
        at java.io.FileOutputStream.<init>(FileOutputStream.java:213)
        at java.io.FileOutputStream.<init>(FileOutputStream.java:133)
        at org.apache.log4j.FileAppender.setFile(FileAppender.java:294)
        at org.apache.log4j.FileAppender.activateOptions(FileAppender.java:165)
        at org.apache.log4j.DailyRollingFileAppender.activateOptions(DailyRollingFileAppender.java:223)
        at org.apache.log4j.config.PropertySetter.activate(PropertySetter.java:307)
        at org.apache.log4j.config.PropertySetter.setProperties(PropertySetter.java:172)
        at org.apache.log4j.config.PropertySetter.setProperties(PropertySetter.java:104)
        at org.apache.log4j.PropertyConfigurator.parseAppender(PropertyConfigurator.java:842)
        at org.apache.log4j.PropertyConfigurator.parseCategory(PropertyConfigurator.java:768)
        at org.apache.log4j.PropertyConfigurator.parseCatsAndRenderers(PropertyConfigurator.java:672)
        at org.apache.log4j.PropertyConfigurator.doConfigure(PropertyConfigurator.java:516)
        at org.apache.log4j.PropertyConfigurator.doConfigure(PropertyConfigurator.java:580)
        at org.apache.log4j.helpers.OptionConverter.selectAndConfigure(OptionConverter.java:526)
        at org.apache.log4j.LogManager.<clinit>(LogManager.java:127)
        at org.apache.spark.internal.Logging$class.initializeLogging(Logging.scala:120)
        at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:108)
        at org.apache.spark.deploy.SparkSubmit$.initializeLogIfNecessary(SparkSubmit.scala:71)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:128)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
log4j:ERROR Either File or DatePattern options are not set for appender [DRFA-stdout].
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
18/11/04 12:24:32 ERROR SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Required executor memory (4608+460 MB) is above the max threshold (3072 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
        at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:318)
        at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:166)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:57)
        at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:164)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:500)
        at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2493)
        at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:934)
        at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:925)
        at scala.Option.getOrElse(Option.scala:121)
        at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:925)
        at org.apache.spark.repl.Main$.createSparkSession(Main.scala:103)
        at $line3.$read$$iw$$iw.<init>(<console>:15)
        at $line3.$read$$iw.<init>(<console>:43)
        at $line3.$read.<init>(<console>:45)
        at $line3.$read$.<init>(<console>:49)
        at $line3.$read$.<clinit>(<console>)
        at $line3.$eval$.$print$lzycompute(<console>:7)
        at $line3.$eval$.$print(<console>:6)
        at $line3.$eval.$print(<console>)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:786)
        at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1047)
        at scala.tools.nsc.interpreter.IMain$WrappedRequest$$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$$anonfun$apply$mcV$sp$1$$anonfun$apply$mcV$sp$2.apply(SparkILoop.scala:79)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$mcV$sp$2.apply(SparkILoop.scala:79)
        at scala.collection.immutable.List.foreach(List.scala:381)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(SparkILoop.scala:79)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1$$anonfun$apply$mcV$sp$1.apply(SparkILoop.scala:79)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1$$anonfun$apply$mcV$sp$1.apply(SparkILoop.scala:79)
        at scala.tools.nsc.interpreter.ILoop.savingReplayStack(ILoop.scala:91)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply$mcV$sp(SparkILoop.scala:78)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:78)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:78)
        at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:214)
        at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:77)
        at org.apache.spark.repl.SparkILoop.loadFiles(SparkILoop.scala:110)
        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$$anonfun$process$1.apply(ILoop.scala:909)
        at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)
        at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)
        at org.apache.spark.repl.Main$.doMain(Main.scala:76)
        at org.apache.spark.repl.Main$.main(Main.scala:56)
        at org.apache.spark.repl.Main.main(Main.scala)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
        at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:894)
        at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:198)
        at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:228)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:137)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
18/11/04 12:24:33 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
18/11/04 12:24:33 WARN MetricsSystem: Stopping a MetricsSystem that is not running
java.lang.IllegalArgumentException: Required executor memory (4608+460 MB) is above the max threshold (3072 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
  at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:318)
  at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:166)
  at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:57)
  at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:164)
  at org.apache.spark.SparkContext.<init>(SparkContext.scala:500)
  at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2493)
  at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:934)
  at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:925)
  at scala.Option.getOrElse(Option.scala:121)
  at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:925)
  at org.apache.spark.repl.Main$.createSparkSession(Main.scala:103)
  ... 55 elided
<console>:14: error: not found: value spark
       import spark.implicits._
              ^
<console>:14: error: not found: value spark
       import spark.sql
              ^
Welcome to
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     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.3.2
      /_/

Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_181)
Type in expressions to have them evaluated.
Type :help for more information.

我是Spark和EMR的新手，不知道该怎么办。我错过了一些配置步骤，还是必须提供其他步骤才能使其正常工作？

谢谢您的帮助！

3 个答案:

答案 0 :(得分：4)

我们也遇到了这个问题，希望一些AWS或Spark工程师可以阅读。我将范围缩小到/etc/spark/conf/log4j.properties文件，以及如何使用${spark.yarn.app.container.log.dir}系统属性配置记录器。该值的值为null，因此日志目录现在的值为/stdout和/stderr，而不是所需的/mnt/var/log/hadoop-yarn/containers/<app_id>/<container_id>/(stdout|stderr)，这是它在EMR <5.18.0中的工作方式。

解决方法＃1（不理想）：如果将属性设置为hadoop用户可以访问的静态路径，例如/var/log/hadoop-yarn/stderr，则一切正常。这可能会破坏历史记录服务器之类的东西，以及其他未知数量的东西，但spark-shell和pyspark可以启动而不会出错。

更新解决方法2（还原）：不知道为什么我没有更早执行此操作，但将其与5.13群集进行比较，则整个DRFA-stderr和DRFA-stdout附加程序都不适用-存在。如果您将那些部分注释掉，删除它们，或者只是从模板中复制log4j.properties文件，此问题也将消失（再次，对其余服务的影响未知）。我不确定该部分的来源，主存储库配置没有这些附加程序，因此它似乎是AWS发行版专有的。

答案 1 :(得分：3)

如果查看/etc/spark/conf/log4j.properties文件，您会发现new setup允许每小时滚动Spark Streaming日志（可能是建议的here）。

发生问题是因为未在Spark驱动程序进程中设置${spark.yarn.app.container.log.dir}系统属性。该属性最终设置为Yarn的容器日志目录，但是稍后会发生（请查看here和here）。

为了解决Spark驱动程序中的此错误，请将以下内容添加到您的spark-submit或spark-shell命令中： --driver-java-options='-Dspark.yarn.app.container.log.dir=/mnt/var/log/hadoop'

请注意，/mnt/var/log/hadoop/stderr和/mnt/var/log/hadoop/stdout文件将由在同一节点上启动的所有（火花流）进程重用。

答案 2 :(得分：0)

为了解决此问题，您可以在emr设置上以json格式添加配置。我们使用这样的代码

{
    "Classification": "yarn-site",
    "Configurations": [
    ],
    "Properties": {
      "spark.yarn.app.container.log.dir": "/var/log/hadoop-yarn"
    }
  }
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