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Error while initiating spark shell

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Error while initiating spark shell

Explorer

Hi friends,

 

I have cloudera trail version 6.2. In the command prompt when i tried to initiate spark shell using

 

spark-shell, im getting the below error:

 

[root@cloudera tmp]# spark-shell
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/04/09 08:19:33 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:345)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:179)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:60)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:184)
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$$anon$2.doSubmit(SparkSubmit.scala:926)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:935)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
20/04/09 08:19:33 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
20/04/09 08:19:33 WARN metrics.MetricsSystem: Stopping a MetricsSystem that is not running
20/04/09 08:19:33 ERROR repl.Main: Failed to initialize Spark session.
java.lang.IllegalArgumentException: Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1024 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:345)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:179)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:60)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:184)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:511)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2549)
at org.apache.spark.sql.SparkSession$Builder$$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)

 

Not sure, the reason besides above error. Kindly help me out.

 

Regards,

GTA

1 ACCEPTED SOLUTION

Accepted Solutions
Highlighted

Re: Error while initiating spark shell

Contributor

Hey @GTA,

 

Thanks for reaching out to the Cloudera community.


"Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1024 MB) of this cluster!"

 

>> This issue occurs when the total memory required to run a spark executor in a container (Spark executor memory -> spark.executor.memory + Spark executor memory overhead: spark.yarn.executor.memoryOverhead) exceeds the memory available for running containers on the NodeManager (yarn.nodemanager.resource.memory-mb) node.

 

Based on the above exception you have 1 GB configured by default for a spark executor, the overhead is by default 384 MB, the total memory required to run the container is 1024+384 MB = 1408 MB.

 

As the NM was configured with not enough memory to even run a single container (only 1024 MB), this resulted in a valid exception.

 

Increasing the NM settings from 1251 to 2048 MB will definitely allow a single container to run on the NM node. Use the mentioned steps to increase "yarn.nodemanager.resource.memory-mb" parameter to resolve this.

 

Cloudera Manager >> YARN >> Configurations >> Search "yarn.nodemanager.resource.memory-mb" >> Configure 2048 MB or higher >> Save & Restart.

 

Let me know if this helps.

View solution in original post

3 REPLIES 3
Highlighted

Re: Error while initiating spark shell

Contributor

Hey @GTA,

 

Thanks for reaching out to the Cloudera community.


"Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1024 MB) of this cluster!"

 

>> This issue occurs when the total memory required to run a spark executor in a container (Spark executor memory -> spark.executor.memory + Spark executor memory overhead: spark.yarn.executor.memoryOverhead) exceeds the memory available for running containers on the NodeManager (yarn.nodemanager.resource.memory-mb) node.

 

Based on the above exception you have 1 GB configured by default for a spark executor, the overhead is by default 384 MB, the total memory required to run the container is 1024+384 MB = 1408 MB.

 

As the NM was configured with not enough memory to even run a single container (only 1024 MB), this resulted in a valid exception.

 

Increasing the NM settings from 1251 to 2048 MB will definitely allow a single container to run on the NM node. Use the mentioned steps to increase "yarn.nodemanager.resource.memory-mb" parameter to resolve this.

 

Cloudera Manager >> YARN >> Configurations >> Search "yarn.nodemanager.resource.memory-mb" >> Configure 2048 MB or higher >> Save & Restart.

 

Let me know if this helps.

View solution in original post

Re: Error while initiating spark shell

Explorer

Thanks a lot for your reply and for your solution Tony:-)

 

Regards,

GTA

Highlighted

Re: Error while initiating spark shell

New Contributor

I used to work at Cloudera/Hortonworks, and now I am a Hashmap Inc. consultant. This solution worked perfectly, thank you.

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