[SPARK-12978][SQL] Skip unnecessary final group-by when input data al…

…ready clustered with group-by keys

This ticket targets the optimization to skip an unnecessary group-by operation below;

Without opt.:
```
== Physical Plan ==
TungstenAggregate(key=[col0#159], functions=[(sum(col1#160),mode=Final,isDistinct=false),(avg(col2#161),mode=Final,isDistinct=false)], output=[col0#159,sum(col1)#177,avg(col2)#178])
+- TungstenAggregate(key=[col0#159], functions=[(sum(col1#160),mode=Partial,isDistinct=false),(avg(col2#161),mode=Partial,isDistinct=false)], output=[col0#159,sum#200,sum#201,count#202L])
   +- TungstenExchange hashpartitioning(col0#159,200), None
      +- InMemoryColumnarTableScan [col0#159,col1#160,col2#161], InMemoryRelation [col0#159,col1#160,col2#161], true, 10000, StorageLevel(true, true, false, true, 1), ConvertToUnsafe, None
```

With opt.:
```
== Physical Plan ==
TungstenAggregate(key=[col0#159], functions=[(sum(col1#160),mode=Complete,isDistinct=false),(avg(col2#161),mode=Final,isDistinct=false)], output=[col0#159,sum(col1)#177,avg(col2)#178])
+- TungstenExchange hashpartitioning(col0#159,200), None
  +- InMemoryColumnarTableScan [col0#159,col1#160,col2#161], InMemoryRelation [col0#159,col1#160,col2#161], true, 10000, StorageLevel(true, true, false, true, 1), ConvertToUnsafe, None
```

Author: Takeshi YAMAMURO <[email protected]>

Closes #10896 from maropu/SkipGroupbySpike.

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala

@@ -259,24 +259,17 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         }
 
         val aggregateOperator =
-          if (aggregateExpressions.map(_.aggregateFunction).exists(!_.supportsPartial)) {
-            if (functionsWithDistinct.nonEmpty) {
-              sys.error("Distinct columns cannot exist in Aggregate operator containing " +
-                "aggregate functions which don't support partial aggregation.")
-            } else {
-              aggregate.AggUtils.planAggregateWithoutPartial(
-                groupingExpressions,
-                aggregateExpressions,
-                resultExpressions,
-                planLater(child))
-            }
-          } else if (functionsWithDistinct.isEmpty) {
+          if (functionsWithDistinct.isEmpty) {
             aggregate.AggUtils.planAggregateWithoutDistinct(
               groupingExpressions,
               aggregateExpressions,
               resultExpressions,
               planLater(child))
           } else {
+            if (aggregateExpressions.map(_.aggregateFunction).exists(!_.supportsPartial)) {
+              sys.error("Distinct columns cannot exist in Aggregate operator containing " +
+                "aggregate functions which don't support partial aggregation.")
+            }
             aggregate.AggUtils.planAggregateWithOneDistinct(
               groupingExpressions,
               functionsWithDistinct,

sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggregateExec.scala

@@ -0,0 +1,56 @@
+/*
+ * 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.aggregate
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.UnaryExecNode
+
+/**
+ * A base class for aggregate implementation.
+ */
+abstract class AggregateExec extends UnaryExecNode {
+
+  def requiredChildDistributionExpressions: Option[Seq[Expression]]
+  def groupingExpressions: Seq[NamedExpression]
+  def aggregateExpressions: Seq[AggregateExpression]
+  def aggregateAttributes: Seq[Attribute]
+  def initialInputBufferOffset: Int
+  def resultExpressions: Seq[NamedExpression]
+
+  protected[this] val aggregateBufferAttributes = {
+    aggregateExpressions.flatMap(_.aggregateFunction.aggBufferAttributes)
+  }
+
+  override def producedAttributes: AttributeSet =
+    AttributeSet(aggregateAttributes) ++
+      AttributeSet(resultExpressions.diff(groupingExpressions).map(_.toAttribute)) ++
+      AttributeSet(aggregateBufferAttributes)
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  override def requiredChildDistribution: List[Distribution] = {
+    requiredChildDistributionExpressions match {
+      case Some(exprs) if exprs.isEmpty => AllTuples :: Nil
+      case Some(exprs) if exprs.nonEmpty => ClusteredDistribution(exprs) :: Nil
+      case None => UnspecifiedDistribution :: Nil
+    }
+  }
+}

sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala

@@ -24,7 +24,6 @@ import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics}
 import org.apache.spark.sql.types.{DecimalType, StringType, StructType}
@@ -42,11 +41,7 @@ case class HashAggregateExec(
     initialInputBufferOffset: Int,
     resultExpressions: Seq[NamedExpression],
     child: SparkPlan)
-  extends UnaryExecNode with CodegenSupport {
-
-  private[this] val aggregateBufferAttributes = {
-    aggregateExpressions.flatMap(_.aggregateFunction.aggBufferAttributes)
-  }
+  extends AggregateExec with CodegenSupport {
 
   require(HashAggregateExec.supportsAggregate(aggregateBufferAttributes))
 
@@ -60,21 +55,6 @@ case class HashAggregateExec(
     "spillSize" -> SQLMetrics.createSizeMetric(sparkContext, "spill size"),
     "aggTime" -> SQLMetrics.createTimingMetric(sparkContext, "aggregate time"))
 
-  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
-
-  override def producedAttributes: AttributeSet =
-    AttributeSet(aggregateAttributes) ++
-    AttributeSet(resultExpressions.diff(groupingExpressions).map(_.toAttribute)) ++
-    AttributeSet(aggregateBufferAttributes)
-
-  override def requiredChildDistribution: List[Distribution] = {
-    requiredChildDistributionExpressions match {
-      case Some(exprs) if exprs.isEmpty => AllTuples :: Nil
-      case Some(exprs) if exprs.nonEmpty => ClusteredDistribution(exprs) :: Nil
-      case None => UnspecifiedDistribution :: Nil
-    }
-  }
-
   // This is for testing. We force TungstenAggregationIterator to fall back to the unsafe row hash
   // map and/or the sort-based aggregation once it has processed a given number of input rows.
   private val testFallbackStartsAt: Option[(Int, Int)] = {

sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortAggregateExec.scala

@@ -22,8 +22,7 @@ import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, UnspecifiedDistribution}
-import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
+import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.util.Utils
 
@@ -38,30 +37,11 @@ case class SortAggregateExec(
     initialInputBufferOffset: Int,
     resultExpressions: Seq[NamedExpression],
     child: SparkPlan)
-  extends UnaryExecNode {
-
-  private[this] val aggregateBufferAttributes = {
-    aggregateExpressions.flatMap(_.aggregateFunction.aggBufferAttributes)
-  }
-
-  override def producedAttributes: AttributeSet =
-    AttributeSet(aggregateAttributes) ++
-      AttributeSet(resultExpressions.diff(groupingExpressions).map(_.toAttribute)) ++
-      AttributeSet(aggregateBufferAttributes)
+  extends AggregateExec {
 
   override lazy val metrics = Map(
     "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"))
 
-  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
-
-  override def requiredChildDistribution: List[Distribution] = {
-    requiredChildDistributionExpressions match {
-      case Some(exprs) if exprs.isEmpty => AllTuples :: Nil
-      case Some(exprs) if exprs.nonEmpty => ClusteredDistribution(exprs) :: Nil
-      case None => UnspecifiedDistribution :: Nil
-    }
-  }
-
   override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
     groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
   }

sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala

@@ -21,6 +21,8 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.aggregate.AggUtils
+import org.apache.spark.sql.execution.aggregate.PartialAggregate
 import org.apache.spark.sql.internal.SQLConf
 
 /**
@@ -151,18 +153,30 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] {
   private def ensureDistributionAndOrdering(operator: SparkPlan): SparkPlan = {
     val requiredChildDistributions: Seq[Distribution] = operator.requiredChildDistribution
     val requiredChildOrderings: Seq[Seq[SortOrder]] = operator.requiredChildOrdering
-    var children: Seq[SparkPlan] = operator.children
-    assert(requiredChildDistributions.length == children.length)
-    assert(requiredChildOrderings.length == children.length)
+    assert(requiredChildDistributions.length == operator.children.length)
+    assert(requiredChildOrderings.length == operator.children.length)
 
-    // Ensure that the operator's children satisfy their output distribution requirements:
-    children = children.zip(requiredChildDistributions).map {
-      case (child, distribution) if child.outputPartitioning.satisfies(distribution) =>
-        child
-      case (child, BroadcastDistribution(mode)) =>
-        BroadcastExchangeExec(mode, child)
-      case (child, distribution) =>
-        ShuffleExchange(createPartitioning(distribution, defaultNumPreShufflePartitions), child)
+    def createShuffleExchange(dist: Distribution, child: SparkPlan) =
+      ShuffleExchange(createPartitioning(dist, defaultNumPreShufflePartitions), child)
+
+    var (parent, children) = operator match {
+      case PartialAggregate(childDist) if !operator.outputPartitioning.satisfies(childDist) =>
+        // If an aggregation needs a shuffle and support partial aggregations, a map-side partial
+        // aggregation and a shuffle are added as children.
+        val (mergeAgg, mapSideAgg) = AggUtils.createMapMergeAggregatePair(operator)
+        (mergeAgg, createShuffleExchange(requiredChildDistributions.head, mapSideAgg) :: Nil)
+      case _ =>
+        // Ensure that the operator's children satisfy their output distribution requirements:
+        val childrenWithDist = operator.children.zip(requiredChildDistributions)
+        val newChildren = childrenWithDist.map {
+          case (child, distribution) if child.outputPartitioning.satisfies(distribution) =>
+            child
+          case (child, BroadcastDistribution(mode)) =>
+            BroadcastExchangeExec(mode, child)
+          case (child, distribution) =>
+            createShuffleExchange(distribution, child)
+        }
+        (operator, newChildren)
     }
 
     // If the operator has multiple children and specifies child output distributions (e.g. join),
@@ -246,7 +260,7 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] {
       }
     }
 
-    operator.withNewChildren(children)
+    parent.withNewChildren(children)
   }
 
   def apply(plan: SparkPlan): SparkPlan = plan.transformUp {

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

@@ -1248,17 +1248,17 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   /**
-   * Verifies that there is no Exchange between the Aggregations for `df`
+   * Verifies that there is a single Aggregation for `df`
    */
-  private def verifyNonExchangingAgg(df: DataFrame) = {
+  private def verifyNonExchangingSingleAgg(df: DataFrame) = {
     var atFirstAgg: Boolean = false
     df.queryExecution.executedPlan.foreach {
       case agg: HashAggregateExec =>
-        atFirstAgg = !atFirstAgg
-      case _ =>
         if (atFirstAgg) {
-          fail("Should not have operators between the two aggregations")
+          fail("Should not have back to back Aggregates")
         }
+        atFirstAgg = true
+      case _ =>
     }
   }
 
@@ -1292,9 +1292,10 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     // Group by the column we are distributed by. This should generate a plan with no exchange
     // between the aggregates
     val df3 = testData.repartition($"key").groupBy("key").count()
-    verifyNonExchangingAgg(df3)
-    verifyNonExchangingAgg(testData.repartition($"key", $"value")
+    verifyNonExchangingSingleAgg(df3)
+    verifyNonExchangingSingleAgg(testData.repartition($"key", $"value")
       .groupBy("key", "value").count())
+    verifyNonExchangingSingleAgg(testData.repartition($"key").groupBy("key", "value").count())
 
     // Grouping by just the first distributeBy expr, need to exchange.
     verifyExchangingAgg(testData.repartition($"key", $"value")

sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{execution, Row}
+import org.apache.spark.sql.{execution, DataFrame, Row}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, Literal, SortOrder}
 import org.apache.spark.sql.catalyst.plans.Inner
@@ -37,36 +37,65 @@ class PlannerSuite extends SharedSQLContext {
 
   setupTestData()
 
-  private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
+  private def testPartialAggregationPlan(query: LogicalPlan): Seq[SparkPlan] = {
     val planner = spark.sessionState.planner
     import planner._
-    val plannedOption = Aggregation(query).headOption
-    val planned =
-      plannedOption.getOrElse(
-        fail(s"Could query play aggregation query $query. Is it an aggregation query?"))
-    val aggregations = planned.collect { case n if n.nodeName contains "Aggregate" => n }
-
-    // For the new aggregation code path, there will be four aggregate operator for
-    // distinct aggregations.
-    assert(
-      aggregations.size == 2 || aggregations.size == 4,
-      s"The plan of query $query does not have partial aggregations.")
+    val ensureRequirements = EnsureRequirements(spark.sessionState.conf)
+    val planned = Aggregation(query).headOption.map(ensureRequirements(_))
+      .getOrElse(fail(s"Could query play aggregation query $query. Is it an aggregation query?"))
+    planned.collect { case n if n.nodeName contains "Aggregate" => n }
   }
 
   test("count is partially aggregated") {
     val query = testData.groupBy('value).agg(count('key)).queryExecution.analyzed
-    testPartialAggregationPlan(query)
+    assert(testPartialAggregationPlan(query).size == 2,
+      s"The plan of query $query does not have partial aggregations.")
   }
 
   test("count distinct is partially aggregated") {
     val query = testData.groupBy('value).agg(countDistinct('key)).queryExecution.analyzed
     testPartialAggregationPlan(query)
+    // For the new aggregation code path, there will be four aggregate operator for  distinct
+    // aggregations.
+    assert(testPartialAggregationPlan(query).size == 4,
+      s"The plan of query $query does not have partial aggregations.")
   }
 
   test("mixed aggregates are partially aggregated") {
     val query =
       testData.groupBy('value).agg(count('value), countDistinct('key)).queryExecution.analyzed
-    testPartialAggregationPlan(query)
+    // For the new aggregation code path, there will be four aggregate operator for  distinct
+    // aggregations.
+    assert(testPartialAggregationPlan(query).size == 4,
+      s"The plan of query $query does not have partial aggregations.")
+  }
+
+  test("non-partial aggregation for aggregates") {
+    withTempView("testNonPartialAggregation") {
+      val schema = StructType(StructField(s"value", IntegerType, true) :: Nil)
+      val row = Row.fromSeq(Seq.fill(1)(null))
+      val rowRDD = sparkContext.parallelize(row :: Nil)
+      spark.createDataFrame(rowRDD, schema).repartition($"value")
+        .createOrReplaceTempView("testNonPartialAggregation")
+
+      val planned1 = sql("SELECT SUM(value) FROM testNonPartialAggregation GROUP BY value")
+        .queryExecution.executedPlan
+
+      // If input data are already partitioned and the same columns are used in grouping keys and
+      // aggregation values, no partial aggregation exist in query plans.
+      val aggOps1 = planned1.collect { case n if n.nodeName contains "Aggregate" => n }
+      assert(aggOps1.size == 1, s"The plan $planned1 has partial aggregations.")
+
+      val planned2 = sql(
+        """
+          |SELECT t.value, SUM(DISTINCT t.value)
+          |FROM (SELECT * FROM testNonPartialAggregation ORDER BY value) t
+          |GROUP BY t.value
+        """.stripMargin).queryExecution.executedPlan
+
+      val aggOps2 = planned1.collect { case n if n.nodeName contains "Aggregate" => n }
+      assert(aggOps2.size == 1, s"The plan $planned2 has partial aggregations.")
+    }
   }
 
   test("sizeInBytes estimation of limit operator for broadcast hash join optimization") {