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Date: Thu, 24 Nov 2016 14:02:58 +0000 (UTC)
From: "Takeshi Yamamuro (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.13023169.1479990431000.360984.1479996178404@Atlassian.JIRA>
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References: <JIRA.13023169.1479990431000@Atlassian.JIRA> <JIRA.13023169.1479990431819@arcas>
Subject: [jira] [Commented] (SPARK-18577) Ambiguous reference with duplicate
 column names in aggregate
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archived-at: Thu, 24 Nov 2016 14:03:00 -0000


    [ https://issues.apache.org/jira/browse/SPARK-18577?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15693356#comment-15693356 ] 

Takeshi Yamamuro commented on SPARK-18577:
------------------------------------------

I reproduced this in master though, do we need to resolve this reference?
For example, postgresql cannot resolve the same query;
{code}
postgres=# CREATE TABLE t(id INT, name VARCHAR, rank FLOAT8);
CREATE TABLE

postgres=# \d t
         Table "public.t"
 Column |       Type        | Modifiers 
--------+-------------------+-----------
 id     | integer           | 
 name   | character varying | 
 rank   | double precision  | 


postgres=# INSERT INTO t values(1, 'xxx', 1.0);
INSERT 0 1

postgres=# SELECT * FROM t;
 id | name | rank 
----+------+------
  1 | xxx  |    1
(1 row)

postgres=# SELECT id, COUNT(*) FROM t t1 JOIN t t2 ON t1.name = t2.name GROUP BY t1.id;
ERROR:  column reference "id" is ambiguous at character 8
{code}

> Ambiguous reference with duplicate column names in aggregate
> ------------------------------------------------------------
>
>                 Key: SPARK-18577
>                 URL: https://issues.apache.org/jira/browse/SPARK-18577
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.2
>            Reporter: Yerui Sun
>
> Assuming we have a table 't' with 3 columns 'id', 'name' and 'rank', and here's the sql to re-produce issue:
> {code}
> select id, count(*) from t t1 join t t2 on t1.name = t2.name group by t1.id
> {code}
> The error message is:
> {code}
> Reference 'id' is ambiguous, could be: id#3, id#9.; line 1 pos 7
> {code}
> The sql can be parsed in Hive, since the select 'id' reference can be resolved to 't1.id', which presented in group expressions.


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