szehon-ho commented on code in PR #57644:
URL: https://github.com/apache/spark/pull/57644#discussion_r3798212958
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/DatasetManager.scala:
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@@ -317,9 +318,23 @@ object DatasetManager extends Logging {
val (catalog, identifier) =
PipelinesCatalogUtils.resolveTableCatalog(context.spark,
table.identifier)
- val outputSchema = table.specifiedSchema.getOrElse(
- inferredSchemas(table.identifier).asNullable
- )
+ val outputSchema = table.specifiedSchema match {
Review Comment:
`VirtualTableInput.load` returns the declared schema as-is and never
consults the materialized table:
```scala
val deducedSchema = specifiedSchema match {
// If the user specified a schema, use it directly.
case Some(ss) => ss
```
So once `outputSchema` differs from `ss`, a same-graph downstream dataset is
planned against the declared columns but reads the real table at execution
(`reanalyzeFlow` leaves the upstream table out of the subgraph, so it resolves
as an external catalog read).
See the first point in my review comment for the step-by-step and the fix
I'd suggest.
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