[SPARK-12190] spark does not start cleanly windows 7 64 bit

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Duplicate
• Affects Version/s: 1.5.2
• Component/s: Windows
• Labels: newbie

Environment

windows 7 64 bit

PATH includes:
C:\Users\Stefan\spark-1.5.2-bin-hadoop2.6\bin
C:\ProgramData\Oracle\Java\javapath
C:\Users\Stefan\scala\bin

SYSTEM variables set are:
JAVA_HOME=C:\Program Files\Java\jre1.8.0_65
HADOOP_HOME=C:\Users\Stefan\hadoop-2.6.0
(where the bin\winutils resides)

• Assignee: Unassigned
• Reporter: stefan
• Created: 08/Dec/15 03:09
• Updated: 08/Dec/15 09:32
• Resolved: 08/Dec/15 09:32

Description

See environment description above for all my PATH info and ENV variables.
Hadoop is not compiled, nor is a distributed storage set up, but the hadoop binary with winutils. exe was downloaded from here:
https://www.barik.net/archive/2015/01/19/172716/
and moved to the home directory

Spark was not built on this machine but rather the precompiled binary was downloaded.

Java is this version:
java version "1.8.0_65"
Java(TM) SE Runtime Environment (build 1.8.0_65-b17)
Java HotSpot(TM) 64-Bit Server VM (build 25.65-b01, mixed mode)

Spark-shell is invoked and the error is shown below:
C:\Users\Stefan\spark-1.5.2-bin-hadoop2.6\bin>spark-shell

log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's repl log4j profile: org/apache/spark/log4j-defaults-repl.properties
To adjust logging level use sc.setLogLevel("INFO")
Welcome to
____ __
/ _/_ ___ ____/ /_
\ \/ _ \/ _ `/ __/ '/
/__/ ./_,// //_\ version 1.5.2
/_/

Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_65)
Type in expressions to have them evaluated.
Type :help for more information.
15/12/07 21:18:40 WARN MetricsSystem: Using default name DAGScheduler for source because spark.app.id is not set.
Spark context available as sc.
15/12/07 21:18:42 WARN General: Plugin (Bundle) "org.datanucleus.store.rdbms" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/bin/../lib/datanucleus-rdbms-3.2.9.jar."
15/12/07 21:18:42 WARN General: Plugin (Bundle) "org.datanucleus" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/bin/../lib/datanucleus-core-3.2.10.jar."
15/12/07 21:18:42 WARN General: Plugin (Bundle) "org.datanucleus.api.jdo" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/bin/../lib/datanucleus-api-jdo-3.2.6.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar."
15/12/07 21:18:42 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
15/12/07 21:18:42 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
15/12/07 21:18:47 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
15/12/07 21:18:47 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
15/12/07 21:18:47 WARN : Your hostname, BloomBear-SSD resolves to a loopback/non-reachable address: fe80:0:0:0:2424:cdcb:ecc1:c9cb%eth6, but we couldn't find any external IP address!
java.lang.RuntimeException: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: ---------
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
at org.apache.spark.sql.hive.client.ClientWrapper.<init>(ClientWrapper.scala:171)
at org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveContext.scala:162)
at org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala:160)
at org.apache.spark.sql.hive.HiveContext.<init>(HiveContext.scala:167)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Unknown Source)
at org.apache.spark.repl.SparkILoop.createSQLContext(SparkILoop.scala:1028)
at $iwC$$iwC.<init>(<console>:9)
at $iwC.<init>(<console>:18)
at <init>(<console>:20)
at .<init>(<console>:24)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1340)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:132)
at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:124)
at org.apache.spark.repl.SparkIMain.beQuietDuring(SparkIMain.scala:324)
at org.apache.spark.repl.SparkILoopInit$class.initializeSpark(SparkILoopInit.scala:124)
at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:64)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1$$anonfun$apply$mcZ$sp$5.apply$mcV$sp(SparkILoop.scala:974)
at org.apache.spark.repl.SparkILoopInit$class.runThunks(SparkILoopInit.scala:159)
at org.apache.spark.repl.SparkILoop.runThunks(SparkILoop.scala:64)
at org.apache.spark.repl.SparkILoopInit$class.postInitialization(SparkILoopInit.scala:108)
at org.apache.spark.repl.SparkILoop.postInitialization(SparkILoop.scala:64)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:991)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:674)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: ---------
at org.apache.hadoop.hive.ql.session.SessionState.createRootHDFSDir(SessionState.java:612)
at org.apache.hadoop.hive.ql.session.SessionState.createSessionDirs(SessionState.java:554)
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:508)
... 56 more

<console>:10: error: not found: value sqlContext
import sqlContext.implicits._
^
<console>:10: error: not found: value sqlContext
import sqlContext.sql
^

Please copy the above output to a text editor so it is more legible.
There are three files that SPARK complains about being registered in duplicate. For example, this file exists:
"file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar"
but it is the same as this file
"file:/C:/Users/Stefan/spark-1.5.2-bin-hadoop2.6/bin/../lib/datanucleus-rdbms-3.2.9.jar."

I gave up w scala and installed python. Remarkably, pyspark starts cleanly. So that means it is a scala install problem?
I changed one of my PATH entries from
C:\Users\Stefan\scala\bin
to
C:\Users\Stefan\scala

made no difference

So I searched for "datanucleus-rdbms-3.2.9.jar" within all the files in the scala directory. No hits. This text also does not exist in any of the files in the hadoop or the spark directories
So I do not know why spark is complaining about that.
I actually do not have this package installed:
package org.datanucleus.store.rdbms;
I think maybe because I did not set up a distributed storage system for hadoop??

I think this error may be related as well:
BoneCP specified but not present in CLASSPATH (or one of dependencies)

And then What's this about?
The root scratch dir: /tmp/hive on HDFS should be writable.

I need some hints to clear this up.

Issue Links

• duplicates: SPARK-10528 spark-shell throws java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable.