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https://issues.apache.org/jira/browse/SPARK-10066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15137201#comment-15137201
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Sangeet Chourey commented on SPARK-10066:
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RESOLVED : Downloaded the correct Winutils version and issue was resolved.
Ideally, it should be locally compiled but if downloading compiled version make
sure that it is 32/64 bit as applicable.
I tried on Windows 7 64 bit, Spark 1.6 and downloaded winutils.exe from
https://www.barik.net/archive/2015/01/19/172716/ and it worked..!!
Complete Steps are at :
http://letstalkspark.blogspot.com/2016/02/getting-started-with-spark-on-window-64.html
> Can't create HiveContext with spark-shell or spark-sql on snapshot
> ------------------------------------------------------------------
>
> Key: SPARK-10066
> URL: https://issues.apache.org/jira/browse/SPARK-10066
> Project: Spark
> Issue Type: Bug
> Components: Spark Shell, SQL
> Affects Versions: 1.5.0
> Environment: Centos 6.6
> Reporter: Robert Beauchemin
> Priority: Minor
>
> Built the 1.5.0-preview-20150812 with the following:
> ./make-distribution.sh -Pyarn -Phadoop-2.6 -Dhadoop.version=2.6.0 -Phive
> -Phive-thriftserver -Psparkr -DskipTests
> Starting spark-shell or spark-sql returns the following error:
> java.lang.RuntimeException: java.lang.RuntimeException: The root scratch dir:
> /tmp/hive on HDFS should be writable. Current permissions are: rwx------
> at
> org.apache.hadoop.hive.ql.session.SessionState.createRootHDFSDir(SessionState.java:612)
> .... [elided]
> at
> org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:508)
>
> It's trying to create a new HiveContext. Running pySpark or sparkR works and
> creates a HiveContext successfully. SqlContext can be created successfully
> with any shell.
> I've tried changing permissions on that HDFS directory (even as far as making
> it world-writable) without success. Tried changing SPARK_USER and also
> running spark-shell as different users without success.
> This works on same machine on 1.4.1 and on earlier pre-release versions of
> Spark 1.5.0 (same make-distribution parms) sucessfully. Just trying the
> snapshot...
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