cloud-fan commented on code in PR #36697:
URL: https://github.com/apache/spark/pull/36697#discussion_r884617691
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sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanPartitioning.scala:
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@@ -32,15 +32,15 @@ import
org.apache.spark.util.collection.Utils.sequenceToOption
*/
object V2ScanPartitioning extends Rule[LogicalPlan] with SQLConfHelper {
override def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
- case d @ DataSourceV2ScanRelation(relation, scan:
SupportsReportPartitioning, _, _) =>
+ case d @ DataSourceV2ScanRelation(relation, scan:
SupportsReportPartitioning, _, None) =>
val funCatalogOpt = relation.catalog.flatMap {
case c: FunctionCatalog => Some(c)
case _ => None
}
val catalystPartitioning = scan.outputPartitioning() match {
case kgp: KeyGroupedPartitioning => sequenceToOption(kgp.keys().map(
- V2ExpressionUtils.toCatalyst(_, relation, funCatalogOpt)))
+ V2ExpressionUtils.toCatalystOpt(_, relation, funCatalogOpt)))
Review Comment:
I'm wondering if we should also fail here. If a data source uses an invalid
partitioning, we should fail the query and let users know, so that they can
debug and fix the data source. Otherwise, users may live with a performance bug
for a while as it's hard to figure out where the problem is. cc @sunchao
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