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https://issues.apache.org/jira/browse/SPARK-39650?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17561276#comment-17561276
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Apache Spark commented on SPARK-39650:
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User 'HeartSaVioR' has created a pull request for this issue:
https://github.com/apache/spark/pull/37041
> Streaming Deduplication should not check the schema of "value"
> --------------------------------------------------------------
>
> Key: SPARK-39650
> URL: https://issues.apache.org/jira/browse/SPARK-39650
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 3.3.0, 3.4.0
> Reporter: Jungtaek Lim
> Priority: Major
>
> When we use dropDuplicate() in the streaming query, specifying the columns
> explicitly would perform deduplication against the columns rather than all
> columns.
> For the structure of state in streaming deduplication, we construct the key
> from "specified" columns and value as empty row (since it's not used at all).
> That said, once the query specifies the columns in dropDuplicate(), all other
> columns should not affect the operation.
> Unfortunately, even we use the empty row as value of the state store, we
> register the "all columns" as the schema for the value on state store, which
> leads incorrect behavior from checking schema for state store. (This is
> figured out as a long-standing issue, it's from the initial implementation of
> StreamingDeduplicateExec.)
> Specifically, columns for DataFrame which is applied to streaming deduplicate
> should be same across the lifetime of the query, whereas the only specified
> columns should be same actually.
> It would be ideal to change the value schema to be empty, but the change
> itself may not be sufficient since schema file has been already written for
> older streaming queries. We may need to allow state schema compatibility
> checker to ignore value schema if required (either config or parameter of
> method if feasible).
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