Github user mgaido91 commented on a diff in the pull request: https://github.com/apache/spark/pull/22326#discussion_r214834311 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala --- @@ -1208,9 +1208,26 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper { reduceLeftOption(And).map(Filter(_, left)).getOrElse(left) val newRight = rightJoinConditions. reduceLeftOption(And).map(Filter(_, right)).getOrElse(right) - val newJoinCond = commonJoinCondition.reduceLeftOption(And) - - Join(newLeft, newRight, joinType, newJoinCond) + val (newJoinConditions, others) = + commonJoinCondition.partition(canEvaluateWithinJoin) + val newJoinCond = newJoinConditions.reduceLeftOption(And) + // if condition expression is unevaluable, it will be removed from + // the new join conditions, if all conditions is unevaluable, we should + // change the join type to CrossJoin. + val newJoinType = + if (commonJoinCondition.nonEmpty && newJoinCond.isEmpty) { + logWarning(s"The whole commonJoinCondition:$commonJoinCondition of the join " + + s"plan:\n $j is unevaluable, it will be ignored and the join plan will be " + --- End diff -- @dilipbiswal I haven't checked the particular plan posted in that comment, for which I think you are right, we can handle as you suggested, but I was checking the case in the UT and in the description of this PR, ie. when the input for the Python UDF contains attributes from both sides. In that case I don't have a better suggestion.
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