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https://issues.apache.org/jira/browse/SPARK-24574?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon reassigned SPARK-24574:
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Assignee: Chongguang LIU
> improve array_contains function of the sql component to deal with Column type
> -----------------------------------------------------------------------------
>
> Key: SPARK-24574
> URL: https://issues.apache.org/jira/browse/SPARK-24574
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: Chongguang LIU
> Assignee: Chongguang LIU
> Priority: Major
> Fix For: 2.4.0
>
>
> Hello all,
>
> I ran into a use case in project with spark sql and want to share with you
> some thoughts about the function array_contains.
>
> Say I have a Dataframe containing 2 columns. Column A of type "Array of
> String" and Column B of type "String". I want to determine if the value of
> column B is contained in the value of column A, without using a udf of course.
> The function array_contains came into my mind naturally:
>
>
> def array_contains(column: Column, value: Any): Column = withExpr{
> ArrayContains(column.expr, Literal(value))
> }
>
> However the function takes the column B and does a "Literal" of column B,
> which yields a runtime exception: RuntimeException("Unsupported literal type
> " + v.getClass + " " + v).
>
> Then after discussion with my friends, we fund a solution without using udf:
> new Column(ArrayContains(col("ColumnA").expr, col("ColumnB").expr)
>
> With this solution, I think of empowering a little bit more the function, by
> doing like this:
> def array_contains(column: Column, value: Any): Column = withExpr {
> value match {
> case c: Column => ArrayContains(column.expr, c.expr)
> case _ => ArrayContains(column.expr, Literal(value))
> }
> }
>
> It does a pattern matching to detect if value is of type Column. If yes, it
> will use the .expr of the column, otherwise it will work as it used to.
>
>
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