[jira] [Updated] (SPARK-60067) UpdateFields expression size grows with struct width

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-60067?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

ASF GitHub Bot updated SPARK-60067:
-----------------------------------
    Labels: pull-request-available  (was: )

> UpdateFields expression size grows with struct width
> ----------------------------------------------------
>
>                 Key: SPARK-60067
>                 URL: https://issues.apache.org/jira/browse/SPARK-60067
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 4.4.0
>            Reporter: Ben Hollis
>            Priority: Major
>              Labels: pull-request-available
>
> Spark represents `Column.withField` and `Column.dropFields` as the Catalyst expression `UpdateFields`. Before execution, Spark replaces it with a new struct containing one Catalyst expression for every output field, including fields that were not changed. A one-field update to an N-field struct therefore creates O(N) expression nodes before any row is evaluated. Wide and nested structs produce large plans that increase optimizer, expression-binding, code-generation, and executor memory costs.
> *Example*
> {code:java}
> val updated = col("wide_struct").withField("field_0", lit(1))
> df.select(updated) {code}
>  
> If `wide_struct` has 1,000 fields, this one-field update becomes a `CreateNamedStruct` with roughly 1,000 field expressions. The query changes one value, but Spark builds and processes a width-sized Catalyst tree before execution. Multiple updates or uses repeat that representation and can produce plans with tens of thousands of nodes.



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