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https://issues.apache.org/jira/browse/SPARK-42492?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17690894#comment-17690894
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Apache Spark commented on SPARK-42492:
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User 'Kimahriman' has created a pull request for this issue:
https://github.com/apache/spark/pull/40085
> Add new function filter_value
> -----------------------------
>
> Key: SPARK-42492
> URL: https://issues.apache.org/jira/browse/SPARK-42492
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 3.3.2
> Reporter: Adam Binford
> Priority: Major
>
> Doing data validation in Spark can lead to a lot of extra evaluations of
> expressions. This is because conditionally evaluated expressions aren't
> candidates for subexpression elimination. For example a simple expression
> such as
> {{when(validate(col), col)}}
> to only keep col if it matches some condition, will lead to col being
> evaluated twice. And if call itself is made up of a series of expensive
> expressions itself, like regular expression checks, this can lead to a lot of
> wasted computation time.
> The initial attempt to resolve this was
> https://issues.apache.org/jira/browse/SPARK-35564, adding support for
> subexpression elimination to conditionally evaluated expressions. However I
> have not been able to get that merged, so this is an alternative (though I
> believe that is still useful on top of this).
> We can add a new higher order function "filter_value" that takes the column
> you want to validate as an argument, and then a function that runs a lambda
> expression returning a boolean on whether to keep that column or not. It
> would have the same semantics as the above when expression, except it would
> guarantee to only evaluate the initial column once.
> An alternative would be to implement a real definition for the NullIf
> expression, but that would only support exact equals checks and not any
> generic condition.
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