Github user xuanyuanking commented on a diff in the pull request:
https://github.com/apache/spark/pull/22326#discussion_r215271726
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1208,9 +1208,38 @@ 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)
+ val newJoinType =
+ if (commonJoinCondition.nonEmpty && newJoinCond.isEmpty) {
+ if (SQLConf.get.crossJoinEnabled) {
+ // 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.
+ logWarning(s"The whole
commonJoinCondition:$commonJoinCondition of the join " +
+ "plan is unevaluable, it will be ignored and the join
plan will be " +
+ s"turned to cross join. This plan shows below:\n $j")
+ Cross
+ } else {
+ // if the crossJoinEnabled is false, an AnalysisException
will throw by
+ // [[CheckCartesianProducts]], we throw firstly here for
better readable
+ // information.
+ throw new AnalysisException("Detected the whole
commonJoinCondition:" +
+ s"$commonJoinCondition of the join plan is unevaluable,
we need to cast the " +
+ "join to cross join by setting the configuration
variable " +
+ s"${SQLConf.CROSS_JOINS_ENABLED.key}=true")
+ }
+ } else {
+ joinType
+ }
+
+ val join = Join(newLeft, newRight, newJoinType, newJoinCond)
+ if (others.nonEmpty && joinType.isInstanceOf[InnerLike]) {
+ Filter(others.reduceLeft(And), join)
+ } else {
+ join
--- End diff --
```
I am a bit surprised that works, it would be great to understand why.
Thanks.
```
Sorry for the bad test, that's too special and the result just right by
accident. The original implement will make all semi join return `[]` in PySpark.
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