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  2. Why does spark-shell fail with “SymbolTable.exitingPhase…java.lang.NullPointerException”?

Why does spark-shell fail with “SymbolTable.exitingPhase…java.lang.NullPointerException”?

由 北战南征 提交于 2019-12-06 04:27:45

TL;DR Spark supports Java 8 (and does not support Java 9).

Spark up to 2.3.0-SNAPSHOT (built today from the master) supports Java 8.

With Java 9 in the PATH you'll get the exception you've been facing.

$ java -version
java version "9.0.1"
Java(TM) SE Runtime Environment (build 9.0.1+11)
Java HotSpot(TM) 64-Bit Server VM (build 9.0.1+11, mixed mode)

Failed to initialize compiler: object java.lang.Object in compiler mirror not found.
** Note that as of 2.8 scala does not assume use of the java classpath.
** For the old behavior pass -usejavacp to scala, or if using a Settings
** object programmatically, settings.usejavacp.value = true.

Failed to initialize compiler: object java.lang.Object in compiler mirror not found.
** Note that as of 2.8 scala does not assume use of the java classpath.
** For the old behavior pass -usejavacp to scala, or if using a Settings
** object programmatically, settings.usejavacp.value = true.
Exception in thread "main" java.lang.NullPointerException
    at scala.reflect.internal.SymbolTable.exitingPhase(SymbolTable.scala:256)
    at scala.tools.nsc.interpreter.IMain$Request.x$20$lzycompute(IMain.scala:896)
    at scala.tools.nsc.interpreter.IMain$Request.x$20(IMain.scala:895)
    at scala.tools.nsc.interpreter.IMain$Request.headerPreamble$lzycompute(IMain.scala:895)
    at scala.tools.nsc.interpreter.IMain$Request.headerPreamble(IMain.scala:895)
    at scala.tools.nsc.interpreter.IMain$Request$Wrapper.preamble(IMain.scala:918)
    at scala.tools.nsc.interpreter.IMain$CodeAssembler$$anonfun$apply$23.apply(IMain.scala:1337)
    at scala.tools.nsc.interpreter.IMain$CodeAssembler$$anonfun$apply$23.apply(IMain.scala:1336)
    at scala.tools.nsc.util.package$.stringFromWriter(package.scala:64)
    at scala.tools.nsc.interpreter.IMain$CodeAssembler$class.apply(IMain.scala:1336)
    at scala.tools.nsc.interpreter.IMain$Request$Wrapper.apply(IMain.scala:908)
    at scala.tools.nsc.interpreter.IMain$Request.compile$lzycompute(IMain.scala:1002)
    at scala.tools.nsc.interpreter.IMain$Request.compile(IMain.scala:997)
    at scala.tools.nsc.interpreter.IMain.compile(IMain.scala:579)
    at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:567)
    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 java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.base/java.lang.reflect.Method.invoke(Method.java:564)
    at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:878)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:197)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:227)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:136)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Right after Java 8 is in PATH, Spark is fine.

$ java -version
java version "1.8.0_152"
Java(TM) SE Runtime Environment (build 1.8.0_152-b16)
Java HotSpot(TM) 64-Bit Server VM (build 25.152-b16, mixed mode)

$ ./bin/spark-shell
17/12/30 20:15:07 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/12/30 20:15:12 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
Spark context Web UI available at http://192.168.1.2:4041
Spark context available as 'sc' (master = local[*], app id = local-1514661312813).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.3.0-SNAPSHOT
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_152)
Type in expressions to have them evaluated.
Type :help for more information.

scala> spark.version
res0: String = 2.3.0-SNAPSHOT

As the OP is on Arch Linux and installed Apache Spark package from AUR, the Sources section shows 7 files that are customized for the Linux distribution, incl. spark-env.sh.

There's this very interesting line in spark-env.sh that sets JAVA_HOME:

export JAVA_HOME=/usr/lib/jvm/default-runtime

That could select Java 9 regardless of PATH environment variable that spark-shell uses.

PROTIP You could use SPARK_PRINT_LAUNCH_COMMAND environment variable to know the command and Java that spark-shell starts with, e.g. SPARK_PRINT_LAUNCH_COMMAND=1 spark-shell. You could also check out sh -x spark-shell that's more Linux-way of debugging shell scripts (like spark-shell).

A solution would be to configure /usr/lib/jvm/default-runtime to use Java 8 (not Java 9) by default, but that's...well...your home exercise. Happy Spark'ing!

Just to clarify the problem , as you can see in the second edit, spark was using java 9 . spark-env.sh sets JAVA_HOME as :

export JAVA_HOME=/usr/lib/jvm/default-runtime

To set default jdk in arch linux, check the folder /usr/lib/jvm to see different jvm distributions .

To mark a distribution as default do : 

archlinux-java set java-8-jdk

screenshoot

[yago@CRISTINA-PC ~]$ ls /usr/lib/jvm/
default/         default-runtime/ java-8-jdk/      java-8-openjdk/  
java-9-jdk/      
[yago@CRISTINA-PC ~]$ archlinux-java set java-8-jdk
This script must be run as root

[yago@CRISTINA-PC ~]$ sudo archlinux-java set java-8-jdk
[sudo] password for yago: 
[yago@CRISTINA-PC ~]$ sudo archlinux-java set java-8-jdk
[yago@CRISTINA-PC ~]$ spark-shell
/usr/bin/hadoop
WARNING: HADOOP_SLAVES has been replaced by HADOOP_WORKERS. Using 
value of HADOOP_SLAVES.
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use 
setLogLevel(newLevel).
2017-12-31 10:47:13,050 WARN util.NativeCodeLoader: Unable to load 
native-hadoop library for your platform... using builtin-java classes 
where applicable
Spark context Web UI available at http://127.0.0.1:4040
Spark context available as 'sc' (master = local[*], app id = local-
1514717237307).
Spark session available as 'spark'.
Welcome to
     ____              __
    / __/__  ___ _____/ /__
   _   \ \/ _ \/ _ `/ __/  '_/
  /___/ .__/\_,_/_/ /_/\_\   version 2.2.0
     /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 
1.8.0_152)
Type in expressions to have them evaluated.
Type :help for more information.

scala>
标签
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