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https://issues.apache.org/jira/browse/SPARK-57003?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-57003:
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Labels: pull-request-available (was: )
> Enforce streaming stateful operator output and state-schema nullability
> -----------------------------------------------------------------------
>
> Key: SPARK-57003
> URL: https://issues.apache.org/jira/browse/SPARK-57003
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 4.3.0
> Reporter: Jungtaek Lim
> Priority: Major
> Labels: pull-request-available
>
> This has been a long standing issue of streaming engine vs Query Optimizer.
> By the nature of streaming query, the query is meant to be long-running, in
> many cases spans to multiple Spark versions. Also, the logical plan is not
> always the same across batches (e.g. there are multiple stream sources and
> one of the source does not have a new data at batch N). This puts the
> streaming query to be affected by analyzer and optimizer.
> The state schema of stateful operator is mostly determined by the input
> schema of the stateful operator, and nullability isn't an exception. If the
> input schema has a nullable column, state schema would have a nullable
> column. Vice versa with non-nullable column.
> For Query Optimizer, one of the optimizations is to flip the nullability,
> say, nullable to non-nullable if appropriate. (This can be done directly or
> indirectly - one of indirect example is determining the nullability of the
> column when union is eliminated with one side) If this optimization can
> happen conditionally, the nullability of the column can change over time,
> which breaks stateful operator.
> The holistic fix of this is to loose the nullability of state schema to be
> always nullable, and propagate this to the output schema of the stateful
> operator, which should be also propagated to the downstream operators.
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