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https://issues.apache.org/jira/browse/SPARK-16646?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15388916#comment-15388916
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Hyukjin Kwon commented on SPARK-16646:
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I see! please let me leave my thought as well just in case it is helpful.
Actually, I thought it should not lose values (fractions or integral parts) but
just throw an exception if it goes over 38 while trying to find a tight common
type (and this is what my PR does for now).
For JSON data source, it falls back to double type in schema inference in this
case.
If it should allow the change in original values with such operations, how
about falling back to double type?
> LEAST doesn't accept numeric arguments with different data types
> ----------------------------------------------------------------
>
> Key: SPARK-16646
> URL: https://issues.apache.org/jira/browse/SPARK-16646
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Cheng Lian
> Assignee: Hyukjin Kwon
>
> {code:sql}
> SELECT LEAST(1, 1.5);
> {code}
> {noformat}
> Error: org.apache.spark.sql.AnalysisException: cannot resolve 'least(1,
> CAST(2.1 AS DECIMAL(2,1)))' due to data type mismatch: The expressions should
> all have the same type, got LEAST (ArrayBuffer(IntegerType,
> DecimalType(2,1))).; line 1 pos 7 (state=,code=0)
> {noformat}
> This query works for 1.6.
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