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From "Ryan Bald (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-21811) Inconsistency when finding the widest common type of a combination of DateType, StringType, and NumericType
Date Tue, 22 Aug 2017 23:39:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-21811?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16137612#comment-16137612
] 

Ryan Bald commented on SPARK-21811:
-----------------------------------

Just kidding, looking at every argument's type before deciding what the widest common type
should be is overkill, will try to avoid.

> Inconsistency when finding the widest common type of a combination of DateType, StringType,
and NumericType
> -----------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-21811
>                 URL: https://issues.apache.org/jira/browse/SPARK-21811
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.3.0
>            Reporter: Ryan Bald
>            Priority: Minor
>
> Finding the widest common type for the arguments of a variadic function (such as IN or
COALESCE) when the types of the arguments are a combination of DateType/TimestampType, StringType,
and NumericType fails with an AnalysisException for some orders of the arguments and succeeds
with a common type of StringType for other orders of the arguments.
> The below examples used to reproduce the error assume a schema of:
> {{[c1: date, c2: string, c3: int]}}
> The following succeeds:
> {{SELECT coalesce(c1, c2, c3) FROM table}}
> While the following produces an exception:
> {{SELECT coalesce(c1, c3, c2) FROM table}}
> The order of arguments affects the behavior because it looks to be the widest common
type is found by repeatedly looking at two arguments at a time, the widest common type found
thus far and the next argument. On initial thought of a fix, I think the way the widest common
type is found would have to be changed and instead look at all arguments first before deciding
what the widest common type should be.
> As my boss is out of office for the rest of the day I will give a pull request a shot,
but as I am not super familiar with Scala or Spark's coding style guidelines, a pull request
is not promised. Going forward with my attempted pull request, I will assume having DateType/TimestampType,
StringType, and NumericType arguments in an IN expression and COALESCE function (and any other
function/expression where this combination of argument types can occur) is valid. I find it
also quite reasonable to have this combination of argument types to be invalid, so if that's
what is decided, then oh well.
> If I were a betting man, I'd say the fix would be made in the following file: [TypeCoercion.scala|https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala]



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