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https://issues.apache.org/jira/browse/SPARK-42473?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17692857#comment-17692857
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Apache Spark commented on SPARK-42473:
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User 'RunyaoChen' has created a pull request for this issue:
https://github.com/apache/spark/pull/40140
> An explicit cast will be needed when INSERT OVERWRITE SELECT UNION ALL
> ----------------------------------------------------------------------
>
> Key: SPARK-42473
> URL: https://issues.apache.org/jira/browse/SPARK-42473
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 3.3.1
> Environment: spark 3.3.1
> Reporter: kevinshin
> Priority: Major
>
> *when 'union all' and one select statement use* *Literal as column value ,
> the other* *select statement has computed expression at the same column ,
> then the whole statement will compile failed. A explicit cast will be needed.*
> for example:
> {color:#4c9aff}explain{color}
> {color:#4c9aff}*INSERT* OVERWRITE *TABLE* test.spark33_decimal_orc{color}
> {color:#4c9aff}*select* *null* *as* amt1, {*}cast{*}('256.99' *as*
> {*}decimal{*}(20,8)) *as* amt2{color}
> {color:#4c9aff}*union* *all*{color}
> {color:#4c9aff}*select* {*}cast{*}('200.99' *as*
> {*}decimal{*}(20,8)){*}/{*}100 *as* amt1,{*}cast{*}('256.99' *as*
> {*}decimal{*}(20,8)) *as* amt2;{color}
> *will got error :*
> org.apache.spark.{*}sql{*}.catalyst.expressions.Literal cannot be *cast* *to*
> org.apache.spark.{*}sql{*}.catalyst.expressions.AnsiCast
> The SQL will need to change to :
> {color:#4c9aff}explain{color}
> {color:#4c9aff}*INSERT* OVERWRITE *TABLE* test.spark33_decimal_orc{color}
> {color:#4c9aff}*select* *null* *as* amt1,{*}cast{*}('256.99' *as*
> {*}decimal{*}(20,8)) *as* amt2{color}
> {color:#4c9aff}*union* *all*{color}
> {color:#4c9aff}*select* {color:#de350b}{*}cast{*}({color}{*}cast{*}('200.99'
> *as* {*}decimal{*}(20,8)){*}/{*}100 *as*
> {*}decimal{*}(20,8){color:#de350b}){color} *as* amt1,{*}cast{*}('256.99' *as*
> {*}decimal{*}(20,8)) *as* amt2;{color}
>
> *but this is not need in spark3.2.1 , is this a bug for spark3.3.1 ?*
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