peter-toth commented on code in PR #37525:
URL: https://github.com/apache/spark/pull/37525#discussion_r1090675484
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sql/core/src/main/scala/org/apache/spark/sql/execution/AliasAwareOutputExpression.scala:
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@@ -16,52 +16,42 @@
*/
package org.apache.spark.sql.execution
-import org.apache.spark.sql.catalyst.expressions.{Alias, Expression,
NamedExpression, SortOrder}
-import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning,
Partitioning, PartitioningCollection, UnknownPartitioning}
+import scala.collection.mutable
-/**
- * A trait that provides functionality to handle aliases in the
`outputExpressions`.
- */
-trait AliasAwareOutputExpression extends UnaryExecNode {
- protected def outputExpressions: Seq[NamedExpression]
-
- private lazy val aliasMap = outputExpressions.collect {
- case a @ Alias(child, _) => child.canonicalized -> a.toAttribute
- }.toMap
-
- protected def hasAlias: Boolean = aliasMap.nonEmpty
-
- protected def normalizeExpression(exp: Expression): Expression = {
- exp.transformDown {
- case e: Expression => aliasMap.getOrElse(e.canonicalized, e)
- }
- }
-}
+import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.catalyst.plans.{AliasAwareOutputExpression,
AliasAwareQueryOutputOrdering}
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning,
PartitioningCollection, UnknownPartitioning}
/**
* A trait that handles aliases in the `outputExpressions` to produce
`outputPartitioning` that
* satisfies distribution requirements.
*/
-trait AliasAwareOutputPartitioning extends AliasAwareOutputExpression {
+trait PartitioningPreservingUnaryExecNode extends UnaryExecNode
+ with AliasAwareOutputExpression {
final override def outputPartitioning: Partitioning = {
- val normalizedOutputPartitioning = if (hasAlias) {
- child.outputPartitioning match {
+ if (hasAlias) {
+ flattenPartitioning(child.outputPartitioning).flatMap {
case e: Expression =>
- normalizeExpression(e).asInstanceOf[Partitioning]
- case other => other
+ // We need unique partitionings but if the input partitioning is
+ // `HashPartitioning(Seq(id + id))` and we have `id -> a` and `id ->
b` aliases then after
+ // the projection we have 4 partitionings:
+ // `HashPartitioning(Seq(a + a))`, `HashPartitioning(Seq(a + b))`,
+ // `HashPartitioning(Seq(b + a))`, `HashPartitioning(Seq(b + b))`,
but
+ // `HashPartitioning(Seq(a + b))` is the same as
`HashPartitioning(Seq(b + a))`.
+ val partitioningSet = mutable.Set.empty[Expression]
+ projectExpression(e)
+ .filter(e => partitioningSet.add(e.canonicalized))
+ .take(aliasCandidateLimit)
+ .asInstanceOf[Stream[Partitioning]]
Review Comment:
This cast is required to avoid a compile error as `projectExpression`
returns `Stream[Expression]` but the `flatMap` requires `Seq[Partitioning]`.
We could use `.asInstanceOf[Seq[Partitioning]]` here but I'm not sure it
makes any difference.
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