Github user xuanyuanking commented on a diff in the pull request:
https://github.com/apache/spark/pull/22326#discussion_r220236374
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/joins.scala
---
@@ -152,3 +153,60 @@ object EliminateOuterJoin extends Rule[LogicalPlan]
with PredicateHelper {
if (j.joinType == newJoinType) f else Filter(condition,
j.copy(joinType = newJoinType))
}
}
+
+/**
+ * Correctly handle PythonUDF which need access both side of join side by
changing the new join
+ * type to Cross.
+ */
+object HandlePythonUDFInJoinCondition extends Rule[LogicalPlan] with
PredicateHelper {
+ override def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+ case j @ Join(_, _, joinType, condition)
+ if condition.map(splitConjunctivePredicates).getOrElse(Nil).exists(
+ _.collectFirst { case udf: PythonUDF => udf }.isDefined) =>
+ if (!joinType.isInstanceOf[InnerLike] && joinType != LeftSemi) {
+ // The current strategy only support InnerLike and LeftSemi join
because for other type,
+ // it breaks SQL semantic if we run the join condition as a filter
after join. If we pass
+ // the plan here, it'll still get a an invalid PythonUDF
RuntimeException with message
+ // `requires attributes from more than one child`, we throw
firstly here for better
+ // readable information.
+ throw new AnalysisException("Using PythonUDF in join condition of
join type" +
+ s" $joinType is not supported.")
+ }
+ if (SQLConf.get.crossJoinEnabled) {
+ // if condition expression contains python udf, it will be moved
out from
+ // the new join conditions, and the join type will be changed to
CrossJoin.
+ logWarning(s"The join condition:$condition of the join plan
contains " +
+ "PythonUDF, it will be moved out and the join plan will be " +
+ s"turned to cross join. This plan shows below:\n $j")
+ val (udf, rest) =
+ condition.map(splitConjunctivePredicates).get.partition(
+ _.collectFirst { case udf: PythonUDF => udf }.isDefined)
+ val newCondition = if (rest.isEmpty) {
+ Option.empty
+ } else {
+ Some(rest.reduceLeft(And))
+ }
+ val newJoin = j.copy(joinType = Cross, condition = newCondition)
+ joinType match {
+ case _: InnerLike =>
+ Filter(udf.reduceLeft(And), newJoin)
+ case LeftSemi =>
+ Project(
+ j.left.output.map(_.toAttribute),
Filter(udf.reduceLeft(And), newJoin))
+ case _ =>
+ throw new AnalysisException("Using PythonUDF in join condition
of join type" +
+ s" $joinType is not supported.")
+ }
+ } else {
+ // if the crossJoinEnabled is false, a RuntimeException will be
thrown later while
+ // the PythonUDF need to access both side of join, we throw
firstly here for better
+ // readable information.
+ throw new AnalysisException(s"Detected the join
condition:$condition of this join " +
--- End diff --
Got it, I'll move the cross join detection logic only into
`CheckCartesianProducts` for safety.
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