Can't get a SparkContext in new AWS EMR Cluster

Question

i just set up an AWS EMR Cluster (EMR Version 5.18 with Spark 2.3.2). I ssh into the master maschine and run spark-shell or pyspark and get the following error:

$ 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.

I am new to Spark and to EMR and dont know what to do. Is there some configuration step i missed or anything else i have to provide to make it work?

Thank you for your help!

FWIW - the error re stdout and stderr (permission denied) started to happen from EMR 5.18.0 and 5.19.0. If you try 5.17.0 you may not receive this error message. I am guessing something changed between Spark 2.3.1 and 2.3.2 which arrived in EMR 5.18.0. I have submitted support case to AWS. The real error you have is this: java.lang.IllegalArgumentException: Required executor memory (4608+460 MB) is above the max threshold (3072 MB) of this cluster. We need to see the configuration of the cluster you are creating to assist further. — Acid Rider, Nov 14 '18 at 23:48

score 6 · Answer 1 · answered Dec 24 '18 at 12:17

If you look into /etc/spark/conf/log4j.properties file, you'll find that there's new setup allowing to roll Spark Streaming logs hourly (probably as it's suggested here).

The problem occurs because ${spark.yarn.app.container.log.dir} system property is not set in Spark driver process. The property is set eventually to Yarn's container log directory, but this happens later (look here and here).

In order to fix this error in Spark driver, add the following to your spark-submit or spark-shell command: --driver-java-options='-Dspark.yarn.app.container.log.dir=/mnt/var/log/hadoop'

Please note that /mnt/var/log/hadoop/stderr and /mnt/var/log/hadoop/stdout files will be reused by all the (Spark Streaming) processes started on the same node.

Diffuser · Answer 2 · 2018-11-20T20:40:20.360

We have also run into this issue and hope some AWS or Spark engineers are reading this. I've narrowed this down to the /etc/spark/conf/log4j.properties file and how the loggers are configured using the ${spark.yarn.app.container.log.dir} system property. That value is evaluating to null and so the logging directory now evaluates to /stdout and /stderr instead of the desired /mnt/var/log/hadoop-yarn/containers/<app_id>/<container_id>/(stdout|stderr) which is how it worked in EMR < 5.18.0.

Workaround #1 (not ideal): If you set that property to a static path which the hadoop user has access to like /var/log/hadoop-yarn/stderr things work fine. This probably breaks things like the history server and an unknown number of other things, but spark-shell and pyspark can start without errors.

UPDATE Workaround #2 (revert): Not sure why I didn't do this earlier but comparing this to a 5.13 cluster, the entirety of the DRFA-stderr and DRFA-stdout appenders were non-existent. If you comment those sections out, delete them, or simply copy the log4j.properties file from the template this problem also goes away (again, unknown impact to the rest of the services). I'm not sure where that section originated from, the master repo configs do not have those appenders so it appears to be proprietary to AWS distros.

would you mind taking a look at [this](https://forums.aws.amazon.com/thread.jspa?threadID=294049&tstart=0)? — 7kemZmani, Nov 24 '18 at 21:03
Responded to that AWS thread. For reference here in case people cannot see the AWS forum, 7kemZmani is asking why the use of a custom log4j config is not being respected when used as a part of spark-submit using a javaopt `-Dlog4j.configuration`. I am not familiar with the use of a log4j.properties file as a part of the spark-submit options but the default file seems to have precedence over those or is being used in some underlying spark process that uses the base config. I believe the second work around I provided here would still apply. — Diffuser, Nov 26 '18 at 15:44

score 0 · Answer 3 · answered Feb 12 '19 at 11:49

0

in order to fix this issue you can add configuration in json format on emr provisioning. We use code like this

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

answered Feb 12 '19 at 11:49

Dima Lukyanchikov

1

1

I tried what you are suggesting, I am currently using emr 5.19.0 and it doesn't work. I tried the @spektom approach and it works: --driver-java-options='-Dspark.yarn.app.container.log.dir=/mnt/var/log/hadoop' – sylvinho81 Feb 15 '19 at 22:44

Can't get a SparkContext in new AWS EMR Cluster

3 Answers3

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