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amazon-web-services - 无法在新的 AWS EMR 集群中获取 SparkContext

转载 作者：行者123 更新时间：2023-12-03 20:50:01 25 4
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我刚刚设置了一个 AWS EMR 集群(带有 Spark 2.3.2 的 EMR 版本 5.18)。我通过 ssh 进入主机器并运行 spark-shell 或 pyspark 并收到以下错误:

$ 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
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   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，不知道该怎么做。是否有一些我错过的配置步骤或我必须提供的任何其他内容才能使其工作？

感谢您的帮助!

最佳答案

如果您查看 /etc/spark/conf/log4j.properties文件，你会发现有new setup允许每小时滚动一次 Spark Streaming 日志(可能是建议的 here )。

出现问题的原因是${spark.yarn.app.container.log.dir}系统属性未在 Spark 驱动程序进程中设置。该属性最终设置为 Yarn 的容器日志目录，但这会在稍后发生(查看 here 和 here )。

为了修复 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:
--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文件将被同一节点上启动的所有(Spark Streaming)进程重用。

关于amazon-web-services - 无法在新的 AWS EMR 集群中获取 SparkContext，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/53140852/

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