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https://issues.apache.org/jira/browse/SPARK-57022?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-57022:
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Labels: pull-request-available (was: )
> Support nested column pruning for transform over arrays of structs
> ------------------------------------------------------------------
>
> Key: SPARK-57022
> URL: https://issues.apache.org/jira/browse/SPARK-57022
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 4.2.0
> Reporter: Chao Sun
> Priority: Major
> Labels: pull-request-available
>
> Spark supports nested column pruning for ordinary nested struct field
> accesses, but it does not currently prune nested fields accessed through the
> lambda variable of transform over an array<struct> column.
> For example:
> {code:sql}
> SELECT transform(rule_results, rule ->
> named_struct(
> 'rule_public_id', rule.rule_public_id,
> 'rule_version', rule.rule_version))
> FROM events
> {code}
> If rule_results contains additional fields, Spark currently retains the full
> element struct in the scan schema, even though the query only reads
> rule_public_id and rule_version. This can increase Parquet and ORC I/O for
> datasets containing wide array element structs.
> The proposed change extends nested schema pruning to recognize statically
> identifiable nested field accesses through the element variable of
> ArrayTransform. It builds a projected element schema from the referenced
> fields and carries that narrower schema back to the array input.
> Because pruning fields changes the physical ordinal layout of the element
> struct, the projected expression must also rewrite the bound lambda variable
> type and nested GetStructField ordinals against the pruned schema. For
> example, if struct<a, b, c> is pruned to struct<a, c>, field c moves from
> ordinal 2 to 1.
> The optimization remains conservative: if the lambda consumes the complete
> array element, such as x -> struct(x.a, x), Spark retains the full element
> schema instead of pruning it.
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