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https://issues.apache.org/jira/browse/SPARK-51728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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German Schiavon Matteo updated SPARK-51728:
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Description:
This change introduces support for the SELECT * EXCEPT(col1, col2, ...) syntax,
which allows users to project all columns from a dataset except for a specified
subset. This pattern is especially useful in wide tables where explicitly
listing all desired columns would be verbose and error-prone.
The syntax is already widely adopted in other SQL engines like BigQuery
([docs|https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#select_except]),
and adding support in Spark SQL helps improve compatibility, reduce
boilerplate, and improve developer ergonomics when working with large schemas.
was:
This change introduces support for the SELECT * EXCEPT(col1, col2, ...) syntax,
which allows users to project all columns from a dataset except for a specified
subset. This pattern is especially useful in wide tables where explicitly
listing all desired columns would be verbose and error-prone.
The syntax is already widely adopted in other SQL engines like BigQuery (docs),
and adding support in Spark SQL helps improve compatibility, reduce
boilerplate, and improve developer ergonomics when working with large schemas.
> Add support for SELECT * EXCEPT
> -------------------------------
>
> Key: SPARK-51728
> URL: https://issues.apache.org/jira/browse/SPARK-51728
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 4.0.0
> Reporter: German Schiavon Matteo
> Priority: Minor
>
> This change introduces support for the SELECT * EXCEPT(col1, col2, ...)
> syntax, which allows users to project all columns from a dataset except for a
> specified subset. This pattern is especially useful in wide tables where
> explicitly listing all desired columns would be verbose and error-prone.
> The syntax is already widely adopted in other SQL engines like BigQuery
> ([docs|https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#select_except]),
> and adding support in Spark SQL helps improve compatibility, reduce
> boilerplate, and improve developer ergonomics when working with large schemas.
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