Hadoop 纱线阈值误差

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我正在使用新的HDP2.6。还有安巴里。我在上面安装了纱线、MapReduce、Spark2、Hadoop等。 我试图用——master纱线进入spark shell，但我经常遇到这样的错误：

$bin/spark-shell --master yarn --deploy-mode client


Warning: Ignoring non-spark config property: spark-executor.memory=4g
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/06/12 13:38:38 ERROR SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Required executor memory (8192+819 MB) is above the max threshold (8192 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:334)
        at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:168)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
        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:745)
        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)

​ 但仍然得到完全相同的错误。 这是我的纱线资源：

以下是成功通过Ambari测试的应用程序：

有人能告诉我我做错了什么，因为我快发疯了。一周前我就想解决这个问题了，但我再也做不到了。请找个人（

在命令行中：

bin/spark-shell --conf spark-executor.memory=4g --conf spark.executor.cores=2 --master yarn --deploy-mode client

您拼错了属性

spark executor.memory

。它应该是

spark.executor.memory

你可以在日志中看到Spark甚至告诉你：

Warning: Ignoring non-spark config property: spark-executor.memory=4g

如果4g仍然太高，减少到2g

Warning: Ignoring non-spark config property: spark-executor.memory=4g