lyne7-sc commented on code in PR #2391:
URL: https://github.com/apache/auron/pull/2391#discussion_r3610813993
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thirdparty/auron-iceberg/src/main/scala/org/apache/spark/sql/auron/iceberg/IcebergScanSupport.scala:
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@@ -391,6 +425,57 @@ object IcebergScanSupport extends Logging {
private def deletesEmpty(deletes: java.util.List[_]): Boolean =
deletes == null || deletes.isEmpty
+ private def plannedInputPartitions(exec: BatchScanExec, useRuntimeFilters:
Boolean)
+ : Option[(Seq[InputPartition], Option[Seq[Seq[InputPartition]]])] = {
+ exec.outputPartitioning match {
+ case partitioning: KeyGroupedPartitioning =>
+ // Runtime filtering can change the final groups after static
planning. Keep this
+ // combination on Spark until native execution can preserve those
dynamic groups.
+ if (exec.runtimeFilters.nonEmpty) {
Review Comment:
Thanks for the detailed explanation! That makes sense to me.
I updated the check to ignore
`DynamicPruningExpression(Literal.TrueLiteral)` while continuing to fall back
to Spark for effective runtime filters.
I also added coverage for both sides:
- A no-op `TrueLiteral` runtime filter keeps the scan native and preserves
`KeyGroupedPartitioning`.
- An effective runtime filter keeps the fact scan as `BatchScanExec`.
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