peter-toth commented on code in PR #55927:
URL: https://github.com/apache/spark/pull/55927#discussion_r3258010248
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sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledJoin.scala:
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@@ -28,6 +28,21 @@ import
org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Dist
trait ShuffledJoin extends JoinCodegenSupport {
def isSkewJoin: Boolean
+ private def containsNullSafeJoinMarker(keys: Seq[Expression]): Boolean = {
+ keys.exists(_.exists(_.isInstanceOf[IsNull]))
+ }
+
+ private lazy val canSpreadNullJoinKeys: Boolean = {
Review Comment:
~Is this robust enough? What if someone crafts a null handling join
condition by hand?~
~Actually, this looks good.~
Actually, why this is needed at all and when can't we spread nulls?
`<=>` is translated to 2 key pairs `Coalesce(a.k, default), Coalesce(b.k,
default))` and `(IsNull(a.k), IsNull(b.k))`, so null never shows up in shuffle
keys. The join type check seems fair enough.
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