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https://issues.apache.org/jira/browse/SPARK-31164?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Zhenhua Wang updated SPARK-31164:
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Description: For a bucketed table, when deciding output partitioning, if
the output doesn't contain all bucket columns, the result is
`UnknownPartitioning`. But when generating rdd, current Spark uses
`createBucketedReadRDD` because it doesn't check if the output contains all
bucket columns. So the rdd and its output partitioning are inconsistent. (was:
For a bucketed table, when deciding output partitioning, if the output doesn't
contain all bucket columns, the result is `UnknownPartitioning`. But when
generating rdd, current Spark uses `createBucketedReadRDD` because it doesn't
check if the output contains all bucket columns. So the rdd and it's output
partitioning are inconsistent.)
> Inconsistent rdd and output partitioning for bucket table when output doesn't
> contain all bucket columns
> --------------------------------------------------------------------------------------------------------
>
> Key: SPARK-31164
> URL: https://issues.apache.org/jira/browse/SPARK-31164
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.5, 3.0.0
> Reporter: Zhenhua Wang
> Priority: Major
>
> For a bucketed table, when deciding output partitioning, if the output
> doesn't contain all bucket columns, the result is `UnknownPartitioning`. But
> when generating rdd, current Spark uses `createBucketedReadRDD` because it
> doesn't check if the output contains all bucket columns. So the rdd and its
> output partitioning are inconsistent.
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