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https://issues.apache.org/jira/browse/SPARK-56599?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-56599:
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Labels: SPIP pull-request-available (was: SPIP)
> SPIP: Write schema narrowing for column-level UPDATE in DSv2
> ------------------------------------------------------------
>
> Key: SPARK-56599
> URL: https://issues.apache.org/jira/browse/SPARK-56599
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 4.2.0
> Reporter: Anurag Mantripragada
> Priority: Major
> Labels: SPIP, pull-request-available
>
> Row-level operations like UPDATE are essential for modern data workflows,
> such as fixing ingestion errors or refreshing model scores in machine
> learning pipelines. While Spark supports UPDATE today, the current
> implementation is limited to full row rewrites. Even if a user updates only a
> small subset of columns, Spark must read every column in the table and send
> the entire row back to the data source.
> For wide tables with hundreds of columns, this full-row approach is highly
> inefficient. It wastes significant disk and network I/O by reading data that
> isn't needed and forces connectors to rewrite columns that haven't changed.
> This is particularly problematic for AI/ML use cases where only a few
> features are updated at a time in wide tables.
> This proposal improves the DataSourceV2 API to support write schema
> narrowing. It allows connectors to declare exactly which columns they need to
> receive during an update. Spark will then optimize the operation to read only
> the required columns and send a partial row back to the source. This makes
> updates significantly faster and reduces write amplification for connectors
> like Iceberg.
> [SPIP
> Document|https://docs.google.com/document/d/1-Wiw9U54ESpbLakb9Cn_mO4AviM4nrk4TF7rNhI3JZg/edit?usp=sharing]
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