anew commented on code in PR #57365:
URL: https://github.com/apache/spark/pull/57365#discussion_r3616934756
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/Scd2BatchProcessor.scala:
##########
@@ -811,6 +811,93 @@ case class Scd2BatchProcessor(
staged.select(outputColumns.toImmutableArraySeq: _*)
}
+ /**
+ * Drop delete-encoded rows (tombstones and decomposition tails) that became
redundant after
+ * reconciliation.
+ *
+ * A tombstone or decomposition tail is redundant when the immediately
preceding row's reconciled
+ * [[endAtColName]] equals its own sequence: the preceding upsert already
encodes the delete
+ * boundary, so the standalone delete-encoded row should no longer be routed
to aux.
+ *
+ * For example, an open upsert `[startAt=10, endAt=null)` followed by a
tombstone at `15`
+ * reconciles into a closed upsert `[10, 15)`, making the tombstone
redundant. Likewise, an
+ * existing closed `[10, 20)` bisected by an event at `15` reconciles the
event into `[15, 20)`,
+ * making the `[null, 20)` decomposition tail redundant.
+ */
+ private[autocdc] def dropLeftoverDeletesPostReconciliation(
+ reconciledDf: DataFrame): DataFrame = {
+ val recordStartAt =
+
Scd2BatchProcessor.recordStartAtOf(F.col(AutoCdcReservedNames.cdcMetadataColName))
+ val startAt = F.col(Scd2BatchProcessor.startAtColName)
+ val endAt = F.col(Scd2BatchProcessor.endAtColName)
+
+ // Both tombstones and decomposition tails encode a delete boundary in
their `endAt`. Either
+ // becomes redundant when the immediately preceding upsert was reconciled
to close exactly on
+ // that boundary, since the resulting closed upsert already carries it.
+ val row = Scd2IntervalColumns(recordStartAt, startAt, endAt)
+ val isTombstone = RowClassifier.isTombstone(row)
+ val isDecompositionTail = RowClassifier.isDecompositionTail(row)
+ val isDeleteEncodedRow = isTombstone || isDecompositionTail
+
+ val withWindowCols = reconciledDf
+ .withColumn(
+ Scd2BatchProcessor.previousEndAtColName,
+ F.lag(endAt, 1).over(orderChronologicallyPerKeyWindow)
+ )
+ .withColumn(
+ Scd2BatchProcessor.isRedundantDeleteEncodingColName,
+ isDeleteEncodedRow && (F.col(Scd2BatchProcessor.previousEndAtColName)
<=> endAt)
+ )
+
+ withWindowCols
+ .filter(!F.col(Scd2BatchProcessor.isRedundantDeleteEncodingColName))
+ .drop(
+ Scd2BatchProcessor.previousEndAtColName,
+ Scd2BatchProcessor.isRedundantDeleteEncodingColName
+ )
+ }
+
+ /**
+ * Convert surviving decomposition tails into tombstones.
+ *
+ * A decomposition tail that survives deletion cleanup is an unmatched
delete boundary; setting
+ * [[recordStartAtFieldName]], and [[startAtColName]] to the tail's end
sequence lets downstream
+ * aux handling preserve it as a tombstone.
+ *
+ * For example, if an existing closed upsert `[startAt=10, endAt=20)` is
bisected by an event at
+ * `15`, decomposition first produces an open head `[10, null)` plus a tail
`[null, 20)`;
+ * reconciliation may close the head as `[10, 15)`, leaving the tail's
boundary at `20` to be
+ * promoted into a tombstone.
+ */
+ private[autocdc] def promoteDecompositionTailsToTombstones(
+ reconciledDf: DataFrame): DataFrame = {
+ val recordStartAt =
+
Scd2BatchProcessor.recordStartAtOf(F.col(AutoCdcReservedNames.cdcMetadataColName))
+ val startAt = F.col(Scd2BatchProcessor.startAtColName)
+ val endAt = F.col(Scd2BatchProcessor.endAtColName)
+ val isDecompositionTail =
+ RowClassifier.isDecompositionTail(Scd2IntervalColumns(recordStartAt,
startAt, endAt))
+
+ val outputColumns = reconciledDf.columns.map {
+ case c if c == AutoCdcReservedNames.cdcMetadataColName =>
+ val metadata = reconciledDf.schema(c).metadata
+ F.when(
+ isDecompositionTail,
+ Scd2BatchProcessor.constructCdcMetadataCol(
+ recordStartAt = endAt,
+ sequencingType = resolvedSequencingType
+ )
+ ).otherwise(F.col(c)).as(c, metadata)
+ case c if c == Scd2BatchProcessor.startAtColName =>
+ val metadata = reconciledDf.schema(c).metadata
+ F.when(isDecompositionTail, endAt).otherwise(startAt).as(c, metadata)
+ case c =>
+ F.col(c)
Review Comment:
fixed and added a test.
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