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https://issues.apache.org/jira/browse/SPARK-23498?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16379753#comment-16379753
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Kevin Zhang commented on SPARK-23498:
-------------------------------------
yes, thanks. But when we use spark sql to run existing hive scripts we expected
spark sql could have the same results as hive, and that's why I open this jira.
Now that [~q79969786] has marked this as duplicated with [SPARK-21646
|https://issues.apache.org/jira/browse/SPARK-21646], I will patch in my own
branch first.
> Accuracy problem in comparison with string and integer
> ------------------------------------------------------
>
> Key: SPARK-23498
> URL: https://issues.apache.org/jira/browse/SPARK-23498
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.0, 2.2.1, 2.3.0
> Reporter: Kevin Zhang
> Priority: Major
>
> While comparing a string column with integer value, spark sql will
> automatically cast the string operant to int, the following sql will return
> true in hive but false in spark
>
> {code:java}
> select '1000.1'>1000
> {code}
>
> from the physical plan we can see the string operant was cast to int which
> caused the accuracy loss
> {code:java}
> *Project [false AS (CAST(1000.1 AS INT) > 1000)#4]
> +- Scan OneRowRelation[]
> {code}
> To solve it, using a wider common type like double to cast both sides of
> operant of a binary operator may be safe.
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