[SPARK-32330][SQL] Preserve shuffled hash join build side partitioning

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

Currently `ShuffledHashJoin.outputPartitioning` inherits from `HashJoin.outputPartitioning`, which only preserves stream side partitioning (`HashJoin.scala`):

```
override def outputPartitioning: Partitioning = streamedPlan.outputPartitioning
```

This loses build side partitioning information, and causes extra shuffle if there's another join / group-by after this join.

Example:

```
withSQLConf(
    SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "50",
    SQLConf.SHUFFLE_PARTITIONS.key -> "2",
    SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
  val df1 = spark.range(10).select($"id".as("k1"))
  val df2 = spark.range(30).select($"id".as("k2"))
  Seq("inner", "cross").foreach(joinType => {
    val plan = df1.join(df2, $"k1" === $"k2", joinType).groupBy($"k1").count()
      .queryExecution.executedPlan
    assert(plan.collect { case _: ShuffledHashJoinExec => true }.size === 1)
    // No extra shuffle before aggregate
    assert(plan.collect { case _: ShuffleExchangeExec => true }.size === 2)
  })
}
```

Current physical plan (having an extra shuffle on `k1` before aggregate)

```
*(4) HashAggregate(keys=[k1#220L], functions=[count(1)], output=[k1#220L, count#235L])
+- Exchange hashpartitioning(k1#220L, 2), true, [id=#117]
   +- *(3) HashAggregate(keys=[k1#220L], functions=[partial_count(1)], output=[k1#220L, count#239L])
      +- *(3) Project [k1#220L]
         +- ShuffledHashJoin [k1#220L], [k2#224L], Inner, BuildLeft
            :- Exchange hashpartitioning(k1#220L, 2), true, [id=#109]
            :  +- *(1) Project [id#218L AS k1#220L]
            :     +- *(1) Range (0, 10, step=1, splits=2)
            +- Exchange hashpartitioning(k2#224L, 2), true, [id=#111]
               +- *(2) Project [id#222L AS k2#224L]
                  +- *(2) Range (0, 30, step=1, splits=2)
```

Ideal physical plan (no shuffle on `k1` before aggregate)

```
*(3) HashAggregate(keys=[k1#220L], functions=[count(1)], output=[k1#220L, count#235L])
+- *(3) HashAggregate(keys=[k1#220L], functions=[partial_count(1)], output=[k1#220L, count#239L])
   +- *(3) Project [k1#220L]
      +- ShuffledHashJoin [k1#220L], [k2#224L], Inner, BuildLeft
         :- Exchange hashpartitioning(k1#220L, 2), true, [id=#107]
         :  +- *(1) Project [id#218L AS k1#220L]
         :     +- *(1) Range (0, 10, step=1, splits=2)
         +- Exchange hashpartitioning(k2#224L, 2), true, [id=#109]
            +- *(2) Project [id#222L AS k2#224L]
               +- *(2) Range (0, 30, step=1, splits=2)
```

This can be fixed by overriding `outputPartitioning` method in `ShuffledHashJoinExec`, similar to `SortMergeJoinExec`.
In addition, also fix one typo in `HashJoin`, as that code path is shared between broadcast hash join and shuffled hash join.

### Why are the changes needed?

To avoid shuffle (for queries having multiple joins or group-by), for saving CPU and IO.

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

No.

### How was this patch tested?

Added unit test in `JoinSuite`.

Closes #29130 from c21/shj.

Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala

@@ -215,7 +215,7 @@ trait HashJoin extends BaseJoinExec {
         existenceJoin(streamedIter, hashed)
       case x =>
         throw new IllegalArgumentException(
-          s"BroadcastHashJoin should not take $x as the JoinType")
+          s"HashJoin should not take $x as the JoinType")
     }
 
     val resultProj = createResultProjection

sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoinExec.scala

@@ -40,15 +40,14 @@ case class ShuffledHashJoinExec(
     condition: Option[Expression],
     left: SparkPlan,
     right: SparkPlan)
-  extends HashJoin {
+  extends HashJoin with ShuffledJoin {
 
   override lazy val metrics = Map(
     "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"),
     "buildDataSize" -> SQLMetrics.createSizeMetric(sparkContext, "data size of build side"),
     "buildTime" -> SQLMetrics.createTimingMetric(sparkContext, "time to build hash map"))
 
-  override def requiredChildDistribution: Seq[Distribution] =
+  override def outputPartitioning: Partitioning = super[ShuffledJoin].outputPartitioning
-    HashClusteredDistribution(leftKeys) :: HashClusteredDistribution(rightKeys) :: Nil
 
   private def buildHashedRelation(iter: Iterator[InternalRow]): HashedRelation = {
     val buildDataSize = longMetric("buildDataSize")

sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledJoin.scala

@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.catalyst.plans.{FullOuter, InnerLike, LeftExistence, LeftOuter, RightOuter}
+import org.apache.spark.sql.catalyst.plans.physical.{Distribution, HashClusteredDistribution, Partitioning, PartitioningCollection, UnknownPartitioning}
+
+/**
+ * Holds common logic for join operators by shuffling two child relations
+ * using the join keys.
+ */
+trait ShuffledJoin extends BaseJoinExec {
+  override def requiredChildDistribution: Seq[Distribution] = {
+    HashClusteredDistribution(leftKeys) :: HashClusteredDistribution(rightKeys) :: Nil
+  }
+
+  override def outputPartitioning: Partitioning = joinType match {
+    case _: InnerLike =>
+      PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
+    case LeftOuter => left.outputPartitioning
+    case RightOuter => right.outputPartitioning
+    case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
+    case LeftExistence(_) => left.outputPartitioning
+    case x =>
+      throw new IllegalArgumentException(
+        s"ShuffledJoin should not take $x as the JoinType")
+  }
+}

sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala

@@ -41,7 +41,7 @@ case class SortMergeJoinExec(
     condition: Option[Expression],
     left: SparkPlan,
     right: SparkPlan,
-    isSkewJoin: Boolean = false) extends BaseJoinExec with CodegenSupport {
+    isSkewJoin: Boolean = false) extends ShuffledJoin with CodegenSupport {
 
   override lazy val metrics = Map(
     "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"))
@@ -72,26 +72,13 @@ case class SortMergeJoinExec(
     }
   }
 
-  override def outputPartitioning: Partitioning = joinType match {
-    case _: InnerLike =>
-      PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
-    // For left and right outer joins, the output is partitioned by the streamed input's join keys.
-    case LeftOuter => left.outputPartitioning
-    case RightOuter => right.outputPartitioning
-    case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
-    case LeftExistence(_) => left.outputPartitioning
-    case x =>
-      throw new IllegalArgumentException(
-        s"${getClass.getSimpleName} should not take $x as the JoinType")
-  }
-
   override def requiredChildDistribution: Seq[Distribution] = {
     if (isSkewJoin) {
       // We re-arrange the shuffle partitions to deal with skew join, and the new children
       // partitioning doesn't satisfy `HashClusteredDistribution`.
       UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
     } else {
-      HashClusteredDistribution(leftKeys) :: HashClusteredDistribution(rightKeys) :: Nil
+      super.requiredChildDistribution
     }
   }
 

sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala

@@ -31,6 +31,7 @@ import org.apache.spark.sql.catalyst.expressions.{Ascending, GenericRow, SortOrd
 import org.apache.spark.sql.catalyst.plans.logical.Filter
 import org.apache.spark.sql.execution.{BinaryExecNode, FilterExec, SortExec, SparkPlan}
 import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
+import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec
 import org.apache.spark.sql.execution.joins._
 import org.apache.spark.sql.execution.python.BatchEvalPythonExec
 import org.apache.spark.sql.internal.SQLConf
@@ -1086,4 +1087,21 @@ class JoinSuite extends QueryTest with SharedSparkSession with AdaptiveSparkPlan
       assert(df2.join(df1, "id").collect().isEmpty)
     }
   }
+
+  test("SPARK-32330: Preserve shuffled hash join build side partitioning") {
+    withSQLConf(
+        SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "50",
+        SQLConf.SHUFFLE_PARTITIONS.key -> "2",
+        SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
+      val df1 = spark.range(10).select($"id".as("k1"))
+      val df2 = spark.range(30).select($"id".as("k2"))
+      Seq("inner", "cross").foreach(joinType => {
+        val plan = df1.join(df2, $"k1" === $"k2", joinType).groupBy($"k1").count()
+          .queryExecution.executedPlan
+        assert(plan.collect { case _: ShuffledHashJoinExec => true }.size === 1)
+        // No extra shuffle before aggregate
+        assert(plan.collect { case _: ShuffleExchangeExec => true }.size === 2)
+      })
+    }
+  }
 }