aokolnychyi commented on a change in pull request #2584:
URL: https://github.com/apache/iceberg/pull/2584#discussion_r630682483
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File path:
spark3-extensions/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DynamicFileFilterExec.scala
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@@ -48,8 +48,32 @@ abstract class DynamicFileFilterExecBase(
override def outputOrdering: Seq[SortOrder] = scanExec.outputOrdering
override def supportsColumnar: Boolean = scanExec.supportsColumnar
- override protected def doExecute(): RDD[InternalRow] = scanExec.execute()
- override protected def doExecuteColumnar(): RDD[ColumnarBatch] =
scanExec.executeColumnar()
+ /*
+ If both target and source have the same partitioning we can have a problem
here if our filter exec actually
+ changes the partition. Currently this can only occur in the SinglePartition
distribution is in use which only
+ happens if both the target and source have a single partition, but if it
does we have the potential of eliminating
+ the only partition in the target. If there are no partitions in the target
then we will throw an exception because
+ the partitioning was assumed to be the same 1 partition in source and
target. We fix this by making sure that
+ we always return at least 1 empty partition, in the future we may need to
handle more complicated partitioner
+ scenarios.
+ */
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ val result = scanExec.execute()
+ if (result.partitions.length == 0) {
Review comment:
We could also remember the original output partitioning in dynamic
filtering and then do that check.
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