revert PR#10896 and PR#14865

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

@@ -259,17 +259,24 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         }
 
         val aggregateOperator =
-          if (functionsWithDistinct.isEmpty) {
+          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) {
             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/HashAggregateExec.scala

@@ -24,6 +24,7 @@ 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}
@@ -41,7 +42,11 @@ case class HashAggregateExec(
     initialInputBufferOffset: Int,
     resultExpressions: Seq[NamedExpression],
     child: SparkPlan)
-  extends AggregateExec with CodegenSupport {
+  extends UnaryExecNode with CodegenSupport {
+
+  private[this] val aggregateBufferAttributes = {
+    aggregateExpressions.flatMap(_.aggregateFunction.aggBufferAttributes)
+  }
 
   require(HashAggregateExec.supportsAggregate(aggregateBufferAttributes))
 
@@ -55,6 +60,21 @@ 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

@@ -22,7 +22,8 @@ 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.execution.SparkPlan
+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.metric.SQLMetrics
 import org.apache.spark.util.Utils
 
@@ -37,11 +38,30 @@ case class SortAggregateExec(
     initialInputBufferOffset: Int,
     resultExpressions: Seq[NamedExpression],
     child: SparkPlan)
-  extends AggregateExec {
+  extends UnaryExecNode {
+
+  private[this] val aggregateBufferAttributes = {
+    aggregateExpressions.flatMap(_.aggregateFunction.aggBufferAttributes)
+  }
+
+  override def producedAttributes: AttributeSet =
+    AttributeSet(aggregateAttributes) ++
+      AttributeSet(resultExpressions.diff(groupingExpressions).map(_.toAttribute)) ++
+      AttributeSet(aggregateBufferAttributes)
 
   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

@@ -21,8 +21,6 @@ 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
 
 /**
@@ -153,31 +151,18 @@ 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
-    assert(requiredChildDistributions.length == operator.children.length)
-    assert(requiredChildOrderings.length == operator.children.length)
+    var children: Seq[SparkPlan] = operator.children
+    assert(requiredChildDistributions.length == children.length)
+    assert(requiredChildOrderings.length == children.length)
 
-    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, ensureDistributionAndOrdering(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)
+    // 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)
     }
 
     // If the operator has multiple children and specifies child output distributions (e.g. join),
@@ -270,7 +255,7 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] {
       }
     }
 
-    parent.withNewChildren(children)
+    operator.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 a single Aggregation for `df`
+   * Verifies that there is no Exchange between the Aggregations for `df`
    */
-  private def verifyNonExchangingSingleAgg(df: DataFrame) = {
+  private def verifyNonExchangingAgg(df: DataFrame) = {
     var atFirstAgg: Boolean = false
     df.queryExecution.executedPlan.foreach {
       case agg: HashAggregateExec =>
+        atFirstAgg = !atFirstAgg
+      case _ =>
         if (atFirstAgg) {
-          fail("Should not have back to back Aggregates")
+          fail("Should not have operators between the two aggregations")
         }
-        atFirstAgg = true
-      case _ =>
     }
   }
 
@@ -1292,10 +1292,9 @@ 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($"key").groupBy("key").count()
-    verifyNonExchangingSingleAgg(df3)
-    verifyNonExchangingSingleAgg(testData.repartition($"key", $"value")
+    verifyNonExchangingAgg(df3)
+    verifyNonExchangingAgg(testData.repartition($"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

@@ -24,7 +24,6 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Repartition}
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.execution.aggregate.SortAggregateExec
 import org.apache.spark.sql.execution.columnar.InMemoryRelation
 import org.apache.spark.sql.execution.exchange.{EnsureRequirements, ReusedExchangeExec, ReuseExchange, ShuffleExchange}
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, SortMergeJoinExec}
@@ -38,84 +37,36 @@ class PlannerSuite extends SharedSQLContext {
 
   setupTestData()
 
-  private def testPartialAggregationPlan(query: LogicalPlan): Seq[SparkPlan] = {
+  private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
     val planner = spark.sessionState.planner
     import planner._
-    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 }
+    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.")
   }
 
   test("count is partially aggregated") {
     val query = testData.groupBy('value).agg(count('key)).queryExecution.analyzed
-    assert(testPartialAggregationPlan(query).size == 2,
-      s"The plan of query $query does not have partial aggregations.")
+    testPartialAggregationPlan(query)
   }
 
   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
-    // 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("SPARK-17289 sort-based partial aggregation needs a sort operator as a child") {
-    withTempView("testSortBasedPartialAggregation") {
-      val schema = StructType(
-        StructField(s"key", IntegerType, true) :: StructField(s"value", StringType, true) :: Nil)
-      val rowRDD = sparkContext.parallelize((0 until 1000).map(d => Row(d % 2, d.toString)))
-      spark.createDataFrame(rowRDD, schema)
-        .createOrReplaceTempView("testSortBasedPartialAggregation")
-
-      // This test assumes a query below uses sort-based aggregations
-      val planned = sql("SELECT MAX(value) FROM testSortBasedPartialAggregation GROUP BY key")
-        .queryExecution.executedPlan
-      // This line extracts both SortAggregate and Sort operators
-      val extractedOps = planned.collect { case n if n.nodeName contains "Sort" => n }
-      val aggOps = extractedOps.collect { case n if n.nodeName contains "SortAggregate" => n }
-      assert(extractedOps.size == 4 && aggOps.size == 2,
-        s"The plan $planned does not have correct sort-based partial aggregate pairs.")
-    }
-  }
-
-  test("non-partial aggregation for aggregates") {
-    withTempView("testNonPartialAggregation") {
-      val schema = StructType(StructField(s"value", IntegerType, true) :: Nil)
-      val row = Row.fromSeq(Seq.fill(1)(null))
-      val rowRDD = sparkContext.parallelize(row :: Nil)
-      spark.createDataFrame(rowRDD, schema).repartition($"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.")
-    }
+    testPartialAggregationPlan(query)
   }
 
   test("sizeInBytes estimation of limit operator for broadcast hash join optimization") {