java.lang.IllegalArgumentException: Required executor memory (1024), overhead (384 MB), and PySpark

ERROR spark.SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1024 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:346)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:180)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:60)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:186)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:511)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2549)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:944)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:935)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:935)
at org.apache.spark.repl.Main$.createSparkSession(Main.scala:106)
at $line3.$read$$iw$$iw.<init>(<console>:15)
at $line3.$read$$iw.<init>(<console>:43)
at $line3.$read.<init>(<console>:45)
at $line3.$read$.<init>(<console>:49)
at $line3.$read$.<clinit>(<console>)
at $line3.$eval$.$print$lzycompute(<console>:7)
at $line3.$eval$.$print(<console>:6)
at $line3.$eval.$print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:793)
at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1054)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:645)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:644)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:644)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:576)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:572)
at scala.tools.nsc.interpreter.IMain$$anonfun$quietRun$1.apply(IMain.scala:231)
at scala.tools.nsc.interpreter.IMain$$anonfun$quietRun$1.apply(IMain.scala:231)
at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:221)
at scala.tools.nsc.interpreter.IMain.quietRun(IMain.scala:231)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1$$anonfun$apply$mcV$sp$1.apply(SparkILoop.scala:109)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1$$anonfun$apply$mcV$sp$1.apply(SparkILoop.scala:109)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply$mcV$sp(SparkILoop.scala:109)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:109)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:109)
at scala.tools.nsc.interpreter.ILoop.savingReplayStack(ILoop.scala:91)
at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:108)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$org$apache$spark$repl$SparkILoop$$anonfun$$loopPostInit$1$1.apply$mcV$sp(SparkILoop.scala:211)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$org$apache$spark$repl$SparkILoop$$anonfun$$loopPostInit$1$1.apply(SparkILoop.scala:199)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$org$apache$spark$repl$SparkILoop$$anonfun$$loopPostInit$1$1.apply(SparkILoop.scala:199)
at scala.tools.nsc.interpreter.ILoop$$anonfun$mumly$1.apply(ILoop.scala:189)
at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:221)
at scala.tools.nsc.interpreter.ILoop.mumly(ILoop.scala:186)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1.org$apache$spark$repl$SparkILoop$$anonfun$$loopPostInit$1(SparkILoop.scala:199)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$startup$1$1.apply(SparkILoop.scala:267)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$startup$1$1.apply(SparkILoop.scala:247)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1.withSuppressedSettings$1(SparkILoop.scala:235)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1.startup$1(SparkILoop.scala:247)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:282)
at org.apache.spark.repl.SparkILoop.runClosure(SparkILoop.scala:159)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:182)
at org.apache.spark.repl.Main$.doMain(Main.scala:78)
at org.apache.spark.repl.Main$.main(Main.scala:58)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:851)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:167)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:195)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:926)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:935)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
20/05/21 20:50:12 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
20/05/21 20:50:12 WARN metrics.MetricsSystem: Stopping a MetricsSystem that is not running

运行spark-shell是报错

解决方法： 修改配置文件

yarn.app.mapreduce.am.resource.mb =4g

yarn.nodemanager.resource.memory-mb=8g

yarn.scheduler.maximum-allocation-mb=4g

标签：lang,1024,anonfun,scala,SparkILoop,executor,apache,org,spark
来源： https://www.cnblogs.com/erlou96/p/12933548.html