vitaliili-db commented on code in PR #55887:
URL: https://github.com/apache/spark/pull/55887#discussion_r3250281694
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownUtils.scala:
##########
@@ -187,6 +193,90 @@ object PushDownUtils extends Logging {
}
}
+ /**
+ * Pushes runtime filters into `scan` and re-plans its input partitions. For
scans whose
+ * `outputPartitioning` is a [[KeyedPartitioning]] (SPJ-active), validates
that the data source
+ * preserved the original partitioning and pads with `None` to preserve key
alignment with the
+ * pre-filter partition set.
+ *
+ * Must be called at execute time: runtime filters carry
[[DynamicPruningExpression]] and
+ * scalar-subquery references whose values are only resolved after their
broadcast/subquery
+ * side completes. Callers should wrap the result in a `lazy val` so the
mutating
+ * [[pushRuntimeFilters]] call runs at most once per scan instance.
+ *
+ * @param scan the V2 scan to push filters into
+ * @param runtimeFilters runtime filters to translate and push
+ * @param partitionPredicateSchema by-name schema for iterative
[[PartitionPredicate]] pushdown
+ * @param output scan output attributes
+ * @param outputPartitioning Spark-side output partitioning (used for
SPJ validation)
+ * @param inputPartitions by-name original (unfiltered)
partitions; consulted only when
+ * no runtime filters fire, so callers can
compute it lazily
+ * @return one entry per original input partition: `Some(part)` for
surviving partitions and
+ * `None` for partition keys whose splits were entirely pruned (SPJ
alignment)
+ */
+ def filterAndPlanPartitions(
Review Comment:
good idea, renamed
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownUtils.scala:
##########
@@ -187,6 +193,90 @@ object PushDownUtils extends Logging {
}
}
+ /**
+ * Pushes runtime filters into `scan` and re-plans its input partitions. For
scans whose
+ * `outputPartitioning` is a [[KeyedPartitioning]] (SPJ-active), validates
that the data source
+ * preserved the original partitioning and pads with `None` to preserve key
alignment with the
+ * pre-filter partition set.
+ *
+ * Must be called at execute time: runtime filters carry
[[DynamicPruningExpression]] and
+ * scalar-subquery references whose values are only resolved after their
broadcast/subquery
+ * side completes. Callers should wrap the result in a `lazy val` so the
mutating
+ * [[pushRuntimeFilters]] call runs at most once per scan instance.
+ *
+ * @param scan the V2 scan to push filters into
+ * @param runtimeFilters runtime filters to translate and push
+ * @param partitionPredicateSchema by-name schema for iterative
[[PartitionPredicate]] pushdown
+ * @param output scan output attributes
+ * @param outputPartitioning Spark-side output partitioning (used for
SPJ validation)
+ * @param inputPartitions by-name original (unfiltered)
partitions; consulted only when
+ * no runtime filters fire, so callers can
compute it lazily
+ * @return one entry per original input partition: `Some(part)` for
surviving partitions and
+ * `None` for partition keys whose splits were entirely pruned (SPJ
alignment)
+ */
+ def filterAndPlanPartitions(
+ scan: Scan,
+ runtimeFilters: Seq[Expression],
+ partitionPredicateSchema: => Option[Seq[PartitionPredicateField]],
+ output: Seq[AttributeReference],
+ outputPartitioning: Partitioning,
+ inputPartitions: => Seq[InputPartition]): Seq[Option[InputPartition]] = {
+ val filtered = pushRuntimeFilters(scan, runtimeFilters,
partitionPredicateSchema, output)
+ if (filtered) {
+ // call toBatch again to get filtered partitions
+ val newPartitions = scan.toBatch.planInputPartitions()
+
+ outputPartitioning match {
+ case k: KeyedPartitioning =>
Review Comment:
done
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