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Date: Wed, 3 Jan 2018 07:18:00 +0000 (UTC)
From: "anubhav tarar (JIRA)" <jira@apache.org>
To: issues@carbondata.apache.org
Message-ID: <JIRA.13128169.1514963837000.561985.1514963880842@Atlassian.JIRA>
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Subject: [jira] [Created] (CARBONDATA-1973) User Should not Be able to give
 the duplicate column name in partition even if its case sensitive
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archived-at: Wed, 03 Jan 2018 07:18:07 -0000

anubhav tarar created CARBONDATA-1973:
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             Summary: User Should not Be able to give the duplicate column name in partition even if its case sensitive
                 Key: CARBONDATA-1973
                 URL: https://issues.apache.org/jira/browse/CARBONDATA-1973
             Project: CarbonData
          Issue Type: Bug
          Components: spark-integration
    Affects Versions: 1.3.0
         Environment: spark-2.1
            Reporter: anubhav tarar
            Assignee: anubhav tarar
            Priority: Trivial


1.carbon.sql("CREATE TABLE uniqdata_char2(name char,id int) partitioned by (NAME char)stored by 'carbondata' ")
 name [uniqdata_char2]
18/01/03 12:44:44 WARN HiveExternalCatalog: Couldn't find corresponding Hive SerDe for data source provider org.apache.spark.sql.CarbonSource. Persisting data source table `default`.`uniqdata_char2` into Hive metastore in Spark SQL specific format, which is NOT compatible with Hive.
18/01/03 12:44:44 AUDIT CarbonCreateTableCommand: [anubhav-Vostro-3559][anubhav][Thread-1]Table created with Database name [default] and Table name [uniqdata_char2]
res30: org.apache.spark.sql.DataFrame = []

as we can see table get created successfully

2.try same thing on hive

carbon.sql("CREATE TABLE uniqdata_char2_hive(name char,id int) partitioned by (NAME char) ")
it gives exception

org.apache.spark.sql.AnalysisException: Found duplicate column(s) in table definition of `uniqdata_char2_hive`: name;
  at org.apache.spark.sql.execution.datasources.AnalyzeCreateTable.org$apache$spark$sql$execution$datasources$AnalyzeCreateTable$$failAnalysis(rules.scala:198)

behaviour of carbondata should be similiar to hive


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