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https://issues.apache.org/jira/browse/SPARK-10066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14700293#comment-14700293
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Robert Beauchemin commented on SPARK-10066:
-------------------------------------------

Yes. I've even changed it (as a test) so both /tmp and /tmp/hive are world rwx 
-able. Here's listing from HDFS:
drwxrwxrwx   - hdfs   hdfs            0 2015-06-18 00:24 /tmp                   
                                                     
drwxrwxrwx   - ambari-qa hdfs            0 2015-08-16 21:38 /tmp/hive 

> 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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