Kimahriman opened a new pull request, #40085:
URL: https://github.com/apache/spark/pull/40085
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### What changes were proposed in this pull request?
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Add a new higher order function `filter_value` which takes a column to
validate and a function that takes the result of that column and returns a
boolean indicating whether to keep the value or return null. This is
semantically the same as a `when` expression passing the column into a
validation function, except it guarantees to only evaluate the initial column
once. The idea was taken from the Scala `Option.filter`, open to other names if
anyone has a better idea.
### Why are the changes needed?
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Conditionally evaluated expressions are currently not candidates for
subexpression elimination. This can lead to a lot of duplicate evaluations of
expressions when doing common data cleaning tasks, such as only keeping a value
if it matches some validation checks. It gets worse when multiple different
checks are chained together, and you can end up with a single expensive
expression being evaluated numerous times.
https://github.com/apache/spark/pull/32987 attempts to solve this by
allowing conditionally evaluated expressions to be candidates for subexpression
elimination, however I have not been able to get that merged in the past 1.5
years. I still think that is valuable and useful, especially as an opt-in
behavior, but this is an alternative option to help improve performance of
these kinds of data validation tasks.
A custom implementation of `NullIf` could help as well, however it would
only support exact equals checks, where this can support any logic you need to
do validation.
### Does this PR introduce _any_ user-facing change?
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Adds a new function.
### How was this patch tested?
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New UTs.
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