[jira] [Assigned] (SPARK-52576) In Declarative Pipelines, drop/recreate on full refresh and MV update

["Wenchen Fan (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-52576?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wenchen Fan reassigned SPARK-52576:
-----------------------------------

    Assignee: Sandy Ryza

> In Declarative Pipelines, drop/recreate on full refresh and MV update
> ---------------------------------------------------------------------
>
>                 Key: SPARK-52576
>                 URL: https://issues.apache.org/jira/browse/SPARK-52576
>             Project: Spark
>          Issue Type: Improvement
>          Components: Declarative Pipelines
>    Affects Versions: 4.1.0
>            Reporter: Sandy Ryza
>            Assignee: Sandy Ryza
>            Priority: Major
>              Labels: pull-request-available
>
> Some pipeline runs result in wiping out and replacing all the data for a table:
>  * Every run of a materialized view
>  * Runs of streaming tables that have the "full refresh" flag
> In the current implementation, this "wipe out and replace" is implemented by:
>  * Truncating the table
>  * Altering the table to drop/update/add columns that don't match the columns in the DataFrame for the current run
> The reason that we want originally wanted to truncate + alter instead of drop / recreate is that dropping has some undesirable effects. E.g. it interrupts readers of the table and wipes away things like ACLs.
> However, we discovered that not all catalogs support dropping columns (e.g. Hive does not), and there’s no way to tell whether a catalog supports dropping columns or not. So change the implementation to drop/recreate the table instead of truncate/alter.



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