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Posted to issues@spark.apache.org by "Robert Beauchemin (JIRA)" <ji...@apache.org> on 2015/08/17 23:52:45 UTC
[jira] [Commented] (SPARK-10066) Can't create HiveContext with
spark-shell or spark-sql on snapshot
[ https://issues.apache.org/jira/browse/SPARK-10066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14700293#comment-14700293 ]
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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