[SPARK-34081][SQL] Only pushdown LeftSemi/LeftAnti over Aggregate if join can be planned as broadcast join

### What changes were proposed in this pull request?

Should not pushdown LeftSemi/LeftAnti over Aggregate for some cases.

```scala
spark.range(50000000L).selectExpr("id % 10000 as a", "id % 10000 as b").write.saveAsTable("t1")
spark.range(40000000L).selectExpr("id % 8000 as c", "id % 8000 as d").write.saveAsTable("t2")
spark.sql("SELECT distinct a, b FROM t1 INTERSECT SELECT distinct c, d FROM t2").explain
```

Before this pr:
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[a#16L, b#17L], functions=[])
   +- HashAggregate(keys=[a#16L, b#17L], functions=[])
      +- HashAggregate(keys=[a#16L, b#17L], functions=[])
         +- Exchange hashpartitioning(a#16L, b#17L, 5), ENSURE_REQUIREMENTS, [id=#72]
            +- HashAggregate(keys=[a#16L, b#17L], functions=[])
               +- SortMergeJoin [coalesce(a#16L, 0), isnull(a#16L), coalesce(b#17L, 0), isnull(b#17L)], [coalesce(c#18L, 0), isnull(c#18L), coalesce(d#19L, 0), isnull(d#19L)], LeftSemi
                  :- Sort [coalesce(a#16L, 0) ASC NULLS FIRST, isnull(a#16L) ASC NULLS FIRST, coalesce(b#17L, 0) ASC NULLS FIRST, isnull(b#17L) ASC NULLS FIRST], false, 0
                  :  +- Exchange hashpartitioning(coalesce(a#16L, 0), isnull(a#16L), coalesce(b#17L, 0), isnull(b#17L), 5), ENSURE_REQUIREMENTS, [id=#65]
                  :     +- FileScan parquet default.t1[a#16L,b#17L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/spark/spark-warehouse/org.apache.spark.sql.Data..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:bigint,b:bigint>
                  +- Sort [coalesce(c#18L, 0) ASC NULLS FIRST, isnull(c#18L) ASC NULLS FIRST, coalesce(d#19L, 0) ASC NULLS FIRST, isnull(d#19L) ASC NULLS FIRST], false, 0
                     +- Exchange hashpartitioning(coalesce(c#18L, 0), isnull(c#18L), coalesce(d#19L, 0), isnull(d#19L), 5), ENSURE_REQUIREMENTS, [id=#66]
                        +- HashAggregate(keys=[c#18L, d#19L], functions=[])
                           +- Exchange hashpartitioning(c#18L, d#19L, 5), ENSURE_REQUIREMENTS, [id=#61]
                              +- HashAggregate(keys=[c#18L, d#19L], functions=[])
                                 +- FileScan parquet default.t2[c#18L,d#19L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/spark/spark-warehouse/org.apache.spark.sql.Data..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<c:bigint,d:bigint>
```

After this pr:
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[a#16L, b#17L], functions=[])
   +- Exchange hashpartitioning(a#16L, b#17L, 5), ENSURE_REQUIREMENTS, [id=#74]
      +- HashAggregate(keys=[a#16L, b#17L], functions=[])
         +- SortMergeJoin [coalesce(a#16L, 0), isnull(a#16L), coalesce(b#17L, 0), isnull(b#17L)], [coalesce(c#18L, 0), isnull(c#18L), coalesce(d#19L, 0), isnull(d#19L)], LeftSemi
            :- Sort [coalesce(a#16L, 0) ASC NULLS FIRST, isnull(a#16L) ASC NULLS FIRST, coalesce(b#17L, 0) ASC NULLS FIRST, isnull(b#17L) ASC NULLS FIRST], false, 0
            :  +- Exchange hashpartitioning(coalesce(a#16L, 0), isnull(a#16L), coalesce(b#17L, 0), isnull(b#17L), 5), ENSURE_REQUIREMENTS, [id=#67]
            :     +- HashAggregate(keys=[a#16L, b#17L], functions=[])
            :        +- Exchange hashpartitioning(a#16L, b#17L, 5), ENSURE_REQUIREMENTS, [id=#61]
            :           +- HashAggregate(keys=[a#16L, b#17L], functions=[])
            :              +- FileScan parquet default.t1[a#16L,b#17L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/spark/spark-warehouse/org.apache.spark.sql.Data..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:bigint,b:bigint>
            +- Sort [coalesce(c#18L, 0) ASC NULLS FIRST, isnull(c#18L) ASC NULLS FIRST, coalesce(d#19L, 0) ASC NULLS FIRST, isnull(d#19L) ASC NULLS FIRST], false, 0
               +- Exchange hashpartitioning(coalesce(c#18L, 0), isnull(c#18L), coalesce(d#19L, 0), isnull(d#19L), 5), ENSURE_REQUIREMENTS, [id=#68]
                  +- HashAggregate(keys=[c#18L, d#19L], functions=[])
                     +- Exchange hashpartitioning(c#18L, d#19L, 5), ENSURE_REQUIREMENTS, [id=#63]
                        +- HashAggregate(keys=[c#18L, d#19L], functions=[])
                           +- FileScan parquet default.t2[c#18L,d#19L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/spark/spark-warehouse/org.apache.spark.sql.Data..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<c:bigint,d:bigint>
```

### Why are the changes needed?

1. Pushdown LeftSemi/LeftAnti over Aggregate will affect performance.
2. It will remove user added DISTINCT operator, e.g.: [q38](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q38.sql), [q87](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q87.sql).

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Unit test and benchmark test.

SQL | Before this PR(Seconds) | After this PR(Seconds)
-- | -- | --
q14a | 660 | 594
q14b | 660 | 600
q38 | 55 | 29
q87 | 66 | 35

Before this pr:
![image](https://user-images.githubusercontent.com/5399861/104452849-8789fc80-55de-11eb-88da-44059899f9a9.png)

After this pr:
![image](https://user-images.githubusercontent.com/5399861/104452899-9a043600-55de-11eb-9286-d8f3a23ca3b8.png)

Closes #31145 from wangyum/SPARK-34081.

Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

Author: wangyum

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/PushDownLeftSemiAntiJoin.scala

@@ -31,7 +31,9 @@ import org.apache.spark.sql.catalyst.rules.Rule
  *  4) Aggregate
  *  5) Other permissible unary operators. please see [[PushPredicateThroughNonJoin.canPushThrough]].
  */
-object PushDownLeftSemiAntiJoin extends Rule[LogicalPlan] with PredicateHelper {
+object PushDownLeftSemiAntiJoin extends Rule[LogicalPlan]
+  with PredicateHelper
+  with JoinSelectionHelper {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     // LeftSemi/LeftAnti over Project
     case Join(p @ Project(pList, gChild), rightOp, LeftSemiOrAnti(joinType), joinCond, hint)
@@ -51,10 +53,11 @@ object PushDownLeftSemiAntiJoin extends Rule[LogicalPlan] with PredicateHelper {
         p.copy(child = Join(gChild, rightOp, joinType, newJoinCond, hint))
       }
 
-    // LeftSemi/LeftAnti over Aggregate
+    // LeftSemi/LeftAnti over Aggregate, only push down if join can be planned as broadcast join.
     case join @ Join(agg: Aggregate, rightOp, LeftSemiOrAnti(_), _, _)
         if agg.aggregateExpressions.forall(_.deterministic) && agg.groupingExpressions.nonEmpty &&
-        !agg.aggregateExpressions.exists(ScalarSubquery.hasCorrelatedScalarSubquery) =>
+          !agg.aggregateExpressions.exists(ScalarSubquery.hasCorrelatedScalarSubquery) &&
+          canPlanAsBroadcastHashJoin(join, conf) =>
       val aliasMap = getAliasMap(agg)
       val canPushDownPredicate = (predicate: Expression) => {
         val replaced = replaceAlias(predicate, aliasMap)

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/joins.scala

@@ -307,6 +307,13 @@ trait JoinSelectionHelper {
     }
   }
 
+  def canPlanAsBroadcastHashJoin(join: Join, conf: SQLConf): Boolean = {
+    getBroadcastBuildSide(join.left, join.right, join.joinType,
+      join.hint, hintOnly = true, conf).isDefined ||
+      getBroadcastBuildSide(join.left, join.right, join.joinType,
+        join.hint, hintOnly = false, conf).isDefined
+  }
+
   def hintToBroadcastLeft(hint: JoinHint): Boolean = {
     hint.leftHint.exists(_.strategy.contains(BROADCAST))
   }

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LeftSemiAntiJoinPushDownSuite.scala

@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types.IntegerType
 
 class LeftSemiPushdownSuite extends PlanTest {
@@ -443,4 +444,28 @@ class LeftSemiPushdownSuite extends PlanTest {
     }
   }
 
+  Seq(LeftSemi, LeftAnti).foreach { jt =>
+    test(s"SPARK-34081: $jt only push down if join can be planned as broadcast join") {
+      Seq(-1, 100000).foreach { threshold =>
+        withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> threshold.toString) {
+          val originalQuery = testRelation
+            .groupBy('b)('b)
+            .join(testRelation1, joinType = jt, condition = Some('b <=> 'd))
+
+          val optimized = Optimize.execute(originalQuery.analyze)
+          val correctAnswer = if (threshold > 0) {
+            testRelation
+              .join(testRelation1, joinType = jt, condition = Some('b <=> 'd))
+              .groupBy('b)('b)
+              .analyze
+          } else {
+            originalQuery.analyze
+          }
+
+          comparePlans(optimized, correctAnswer)
+        }
+      }
+    }
+  }
+
 }