[SQL] SPARK-1371 Hash Aggregation Improvements

Given:
```scala
case class Data(a: Int, b: Int)
val rdd =
  sparkContext
    .parallelize(1 to 200)
    .flatMap(_ => (1 to 50000).map(i => Data(i % 100, i)))
rdd.registerAsTable("data")
cacheTable("data")
```
Before:
```
SELECT COUNT(*) FROM data:[10000000]
16795.567ms
SELECT a, SUM(b) FROM data GROUP BY a
7536.436ms
SELECT SUM(b) FROM data
10954.1ms
```

After:
```
SELECT COUNT(*) FROM data:[10000000]
1372.175ms
SELECT a, SUM(b) FROM data GROUP BY a
2070.446ms
SELECT SUM(b) FROM data
958.969ms
```

Author: Michael Armbrust <michael@databricks.com>

Closes #295 from marmbrus/hashAgg and squashes the following commits:

ec63575 [Michael Armbrust] Add comment.
d0495a9 [Michael Armbrust] Use scaladoc instead.
b4a6887 [Michael Armbrust] Address review comments.
a2d90ba [Michael Armbrust] Capture child output statically to avoid issues with generators and serialization.
7c13112 [Michael Armbrust] Rewrite Aggregate operator to stream input and use projections.  Remove unused local RDD functions implicits.
5096f99 [Michael Armbrust] Make HiveUDAF fields transient since object inspectors are not serializable.
6a4b671 [Michael Armbrust] Add option to avoid binding operators expressions automatically.
92cca08 [Michael Armbrust] Always include serialization debug info when running tests.
1279df2 [Michael Armbrust] Increase default number of partitions.

project/SparkBuild.scala

@@ -178,6 +178,7 @@ object SparkBuild extends Build {
     fork := true,
     javaOptions in Test += "-Dspark.home=" + sparkHome,
     javaOptions in Test += "-Dspark.testing=1",
+    javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark").map { case (k,v) => s"-D$k=$v" }.toSeq,
     javaOptions += "-Xmx3g",
     // Show full stack trace and duration in test cases.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala

@@ -48,11 +48,17 @@ case class BoundReference(ordinal: Int, baseReference: Attribute)
   override def apply(input: Row): Any = input(ordinal)
 }
 
+/**
+ * Used to denote operators that do their own binding of attributes internally.
+ */
+trait NoBind { self: trees.TreeNode[_] => }
+
 class BindReferences[TreeNode <: QueryPlan[TreeNode]] extends Rule[TreeNode] {
   import BindReferences._
 
   def apply(plan: TreeNode): TreeNode = {
     plan.transform {
+      case n: NoBind => n.asInstanceOf[TreeNode]
       case leafNode if leafNode.children.isEmpty => leafNode
       case unaryNode if unaryNode.children.size == 1 => unaryNode.transformExpressions { case e =>
         bindReference(e, unaryNode.children.head.output)

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala

@@ -28,9 +28,9 @@ class Projection(expressions: Seq[Expression]) extends (Row => Row) {
 
   protected val exprArray = expressions.toArray
   def apply(input: Row): Row = {
-    val outputArray = new Array[Any](exprArray.size)
+    val outputArray = new Array[Any](exprArray.length)
     var i = 0
-    while (i < exprArray.size) {
+    while (i < exprArray.length) {
       outputArray(i) = exprArray(i).apply(input)
       i += 1
     }
@@ -57,7 +57,7 @@ case class MutableProjection(expressions: Seq[Expression]) extends (Row => Row)
 
   def apply(input: Row): Row = {
     var i = 0
-    while (i < exprArray.size) {
+    while (i < exprArray.length) {
       mutableRow(i) = exprArray(i).apply(input)
       i += 1
     }

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala

@@ -27,7 +27,7 @@ abstract class AggregateExpression extends Expression {
    * Creates a new instance that can be used to compute this aggregate expression for a group
    * of input rows/
    */
-  def newInstance: AggregateFunction
+  def newInstance(): AggregateFunction
 }
 
 /**
@@ -75,7 +75,7 @@ abstract class AggregateFunction
   override def apply(input: Row): Any
 
   // Do we really need this?
-  def newInstance = makeCopy(productIterator.map { case a: AnyRef => a }.toArray)
+  def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray)
 }
 
 case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -89,7 +89,7 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod
     SplitEvaluation(Sum(partialCount.toAttribute), partialCount :: Nil)
   }
 
-  override def newInstance = new CountFunction(child, this)
+  override def newInstance()= new CountFunction(child, this)
 }
 
 case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpression {
@@ -98,7 +98,7 @@ case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpressi
   def nullable = false
   def dataType = IntegerType
   override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")}})"
-  override def newInstance = new CountDistinctFunction(expressions, this)
+  override def newInstance()= new CountDistinctFunction(expressions, this)
 }
 
 case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -118,7 +118,7 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN
       partialCount :: partialSum :: Nil)
   }
 
-  override def newInstance = new AverageFunction(child, this)
+  override def newInstance()= new AverageFunction(child, this)
 }
 
 case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -134,7 +134,7 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[
       partialSum :: Nil)
   }
 
-  override def newInstance = new SumFunction(child, this)
+  override def newInstance()= new SumFunction(child, this)
 }
 
 case class SumDistinct(child: Expression)
@@ -145,7 +145,7 @@ case class SumDistinct(child: Expression)
   def dataType = child.dataType
   override def toString = s"SUM(DISTINCT $child)"
 
-  override def newInstance = new SumDistinctFunction(child, this)
+  override def newInstance()= new SumDistinctFunction(child, this)
 }
 
 case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -160,7 +160,7 @@ case class First(child: Expression) extends PartialAggregate with trees.UnaryNod
       First(partialFirst.toAttribute),
       partialFirst :: Nil)
   }
-  override def newInstance = new FirstFunction(child, this)
+  override def newInstance()= new FirstFunction(child, this)
 }
 
 case class AverageFunction(expr: Expression, base: AggregateExpression)

sql/core/src/main/scala/org/apache/spark/rdd/PartitionLocalRDDFunctions.scala

-/*
- * 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.rdd
-
-import scala.language.implicitConversions
-
-import scala.reflect._
-import scala.collection.mutable.ArrayBuffer
-
-import org.apache.spark.{Aggregator, InterruptibleIterator, Logging}
-import org.apache.spark.util.collection.AppendOnlyMap
-
-/* Implicit conversions */
-import org.apache.spark.SparkContext._
-
-/**
- * Extra functions on RDDs that perform only local operations.  These can be used when data has
- * already been partitioned correctly.
- */
-private[spark] class PartitionLocalRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)])
-  extends Logging
-  with Serializable {
-
-  /**
-   * Cogroup corresponding partitions of `this` and `other`. These two RDDs should have
-   * the same number of partitions. Partitions of these two RDDs are cogrouped
-   * according to the indexes of partitions. If we have two RDDs and
-   * each of them has n partitions, we will cogroup the partition i from `this`
-   * with the partition i from `other`.
-   * This function will not introduce a shuffling operation.
-   */
-  def cogroupLocally[W](other: RDD[(K, W)]): RDD[(K, (Seq[V], Seq[W]))] = {
-    val cg = self.zipPartitions(other)((iter1:Iterator[(K, V)], iter2:Iterator[(K, W)]) => {
-      val map = new AppendOnlyMap[K, Seq[ArrayBuffer[Any]]]
-
-      val update: (Boolean, Seq[ArrayBuffer[Any]]) => Seq[ArrayBuffer[Any]] = (hadVal, oldVal) => {
-        if (hadVal) oldVal else Array.fill(2)(new ArrayBuffer[Any])
-      }
-
-      val getSeq = (k: K) => {
-        map.changeValue(k, update)
-      }
-
-      iter1.foreach { kv => getSeq(kv._1)(0) += kv._2 }
-      iter2.foreach { kv => getSeq(kv._1)(1) += kv._2 }
-
-      map.iterator
-    }).mapValues { case Seq(vs, ws) => (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]])}
-
-    cg
-  }
-
-  /**
-   * Group the values for each key within a partition of the RDD into a single sequence.
-   * This function will not introduce a shuffling operation.
-   */
-  def groupByKeyLocally(): RDD[(K, Seq[V])] = {
-    def createCombiner(v: V) = ArrayBuffer(v)
-    def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
-    val aggregator = new Aggregator[K, V, ArrayBuffer[V]](createCombiner, mergeValue, _ ++ _)
-    val bufs = self.mapPartitionsWithContext((context, iter) => {
-      new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
-    }, preservesPartitioning = true)
-    bufs.asInstanceOf[RDD[(K, Seq[V])]]
-  }
-
-  /**
-   * Join corresponding partitions of `this` and `other`.
-   * If we have two RDDs and each of them has n partitions,
-   * we will join the partition i from `this` with the partition i from `other`.
-   * This function will not introduce a shuffling operation.
-   */
-  def joinLocally[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = {
-    cogroupLocally(other).flatMapValues {
-      case (vs, ws) => for (v <- vs.iterator; w <- ws.iterator) yield (v, w)
-    }
-  }
-}
-
-private[spark] object PartitionLocalRDDFunctions {
-  implicit def rddToPartitionLocalRDDFunctions[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]) =
-    new PartitionLocalRDDFunctions(rdd)
-}
-
-

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

@@ -76,7 +76,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
  */
 object AddExchange extends Rule[SparkPlan] {
   // TODO: Determine the number of partitions.
-  val numPartitions = 8
+  val numPartitions = 150
 
   def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
     case operator: SparkPlan =>

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

@@ -17,14 +17,13 @@
 
 package org.apache.spark.sql.execution
 
+import java.util.HashMap
+
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 
-/* Implicit conversions */
-import org.apache.spark.rdd.PartitionLocalRDDFunctions._
-
 /**
  * Groups input data by `groupingExpressions` and computes the `aggregateExpressions` for each
  * group.
@@ -40,7 +39,7 @@ case class Aggregate(
     groupingExpressions: Seq[Expression],
     aggregateExpressions: Seq[NamedExpression],
     child: SparkPlan)(@transient sc: SparkContext)
-  extends UnaryNode {
+  extends UnaryNode with NoBind {
 
   override def requiredChildDistribution =
     if (partial) {
@@ -55,61 +54,149 @@ case class Aggregate(
 
   override def otherCopyArgs = sc :: Nil
 
+  // HACK: Generators don't correctly preserve their output through serializations so we grab
+  // out child's output attributes statically here.
+  val childOutput = child.output
+
   def output = aggregateExpressions.map(_.toAttribute)
 
-  /* Replace all aggregate expressions with spark functions that will compute the result. */
-  def createAggregateImplementations() = aggregateExpressions.map { agg =>
-    val impl = agg transform {
-      case a: AggregateExpression => a.newInstance
+  /**
+   * An aggregate that needs to be computed for each row in a group.
+   *
+   * @param unbound Unbound version of this aggregate, used for result substitution.
+   * @param aggregate A bound copy of this aggregate used to create a new aggregation buffer.
+   * @param resultAttribute An attribute used to refer to the result of this aggregate in the final
+   *                        output.
+   */
+  case class ComputedAggregate(
+      unbound: AggregateExpression,
+      aggregate: AggregateExpression,
+      resultAttribute: AttributeReference)
+
+  /** A list of aggregates that need to be computed for each group. */
+  @transient
+  lazy val computedAggregates = aggregateExpressions.flatMap { agg =>
+    agg.collect {
+      case a: AggregateExpression =>
+        ComputedAggregate(
+          a,
+          BindReferences.bindReference(a, childOutput).asInstanceOf[AggregateExpression],
+          AttributeReference(s"aggResult:$a", a.dataType, nullable = true)())
     }
+  }.toArray
+
+  /** The schema of the result of all aggregate evaluations */
+  @transient
+  lazy val computedSchema = computedAggregates.map(_.resultAttribute)
+
+  /** Creates a new aggregate buffer for a group. */
+  def newAggregateBuffer(): Array[AggregateFunction] = {
+    val buffer = new Array[AggregateFunction](computedAggregates.length)
+    var i = 0
+    while (i < computedAggregates.length) {
+      buffer(i) = computedAggregates(i).aggregate.newInstance()
+      i += 1
+    }
+    buffer
+  }
 
-    val remainingAttributes = impl.collect { case a: Attribute => a }
-    // If any references exist that are not inside agg functions then the must be grouping exprs
-    // in this case we must rebind them to the grouping tuple.
-    if (remainingAttributes.nonEmpty) {
-      val unaliasedAggregateExpr = agg transform { case Alias(c, _) => c }
-
-      // An exact match with a grouping expression
-      val exactGroupingExpr = groupingExpressions.indexOf(unaliasedAggregateExpr) match {
-        case -1 => None
-        case ordinal => Some(BoundReference(ordinal, Alias(impl, "AGGEXPR")().toAttribute))
-      }
+  /** Named attributes used to substitute grouping attributes into the final result. */
+  @transient
+  lazy val namedGroups = groupingExpressions.map {
+    case ne: NamedExpression => ne -> ne.toAttribute
+    case e => e -> Alias(e, s"groupingExpr:$e")().toAttribute
+  }
 
-      exactGroupingExpr.getOrElse(
-        sys.error(s"$agg is not in grouping expressions: $groupingExpressions"))
-    } else {
-      impl
+  /**
+   * A map of substitutions that are used to insert the aggregate expressions and grouping
+   * expression into the final result expression.
+   */
+  @transient
+  lazy val resultMap =
+    (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute} ++ namedGroups).toMap
+
+  /**
+   * Substituted version of aggregateExpressions expressions which are used to compute final
+   * output rows given a group and the result of all aggregate computations.
+   */
+  @transient
+  lazy val resultExpressions = aggregateExpressions.map { agg =>
+    agg.transform {
+      case e: Expression if resultMap.contains(e) => resultMap(e)
     }
   }
 
   def execute() = attachTree(this, "execute") {
-    // TODO: If the child of it is an [[catalyst.execution.Exchange]],
-    // do not evaluate the groupingExpressions again since we have evaluated it
-    // in the [[catalyst.execution.Exchange]].
-    val grouped = child.execute().mapPartitions { iter =>
-      val buildGrouping = new Projection(groupingExpressions)
-      iter.map(row => (buildGrouping(row), row.copy()))
-    }.groupByKeyLocally()
-
-    val result = grouped.map { case (group, rows) =>
-      val aggImplementations = createAggregateImplementations()
-
-      // Pull out all the functions so we can feed each row into them.
-      val aggFunctions = aggImplementations.flatMap(_ collect { case f: AggregateFunction => f })
-
-      rows.foreach { row =>
-        aggFunctions.foreach(_.update(row))
+    if (groupingExpressions.isEmpty) {
+      child.execute().mapPartitions { iter =>
+        val buffer = newAggregateBuffer()
+        var currentRow: Row = null
+        while (iter.hasNext) {
+          currentRow = iter.next()
+          var i = 0
+          while (i < buffer.length) {
+            buffer(i).update(currentRow)
+            i += 1
+          }
+        }
+        val resultProjection = new Projection(resultExpressions, computedSchema)
+        val aggregateResults = new GenericMutableRow(computedAggregates.length)
+
+        var i = 0
+        while (i < buffer.length) {
+          aggregateResults(i) = buffer(i).apply(EmptyRow)
+          i += 1
+        }
+
+        Iterator(resultProjection(aggregateResults))
       }
-      buildRow(aggImplementations.map(_.apply(group)))
-    }
-
-    // TODO: THIS BREAKS PIPELINING, DOUBLE COMPUTES THE ANSWER, AND USES TOO MUCH MEMORY...
-    if (groupingExpressions.isEmpty && result.count == 0) {
-      // When there there is no output to the Aggregate operator, we still output an empty row.
-      val aggImplementations = createAggregateImplementations()
-      sc.makeRDD(buildRow(aggImplementations.map(_.apply(null))) :: Nil)
     } else {
-      result
+      child.execute().mapPartitions { iter =>
+        val hashTable = new HashMap[Row, Array[AggregateFunction]]
+        val groupingProjection = new MutableProjection(groupingExpressions, childOutput)
+
+        var currentRow: Row = null
+        while (iter.hasNext) {
+          currentRow = iter.next()
+          val currentGroup = groupingProjection(currentRow)
+          var currentBuffer = hashTable.get(currentGroup)
+          if (currentBuffer == null) {
+            currentBuffer = newAggregateBuffer()
+            hashTable.put(currentGroup.copy(), currentBuffer)
+          }
+
+          var i = 0
+          while (i < currentBuffer.length) {
+            currentBuffer(i).update(currentRow)
+            i += 1
+          }
+        }
+
+        new Iterator[Row] {
+          private[this] val hashTableIter = hashTable.entrySet().iterator()
+          private[this] val aggregateResults = new GenericMutableRow(computedAggregates.length)
+          private[this] val resultProjection =
+            new MutableProjection(resultExpressions, computedSchema ++ namedGroups.map(_._2))
+          private[this] val joinedRow = new JoinedRow
+
+          override final def hasNext: Boolean = hashTableIter.hasNext
+
+          override final def next(): Row = {
+            val currentEntry = hashTableIter.next()
+            val currentGroup = currentEntry.getKey
+            val currentBuffer = currentEntry.getValue
+
+            var i = 0
+            while (i < currentBuffer.length) {
+              // Evaluating an aggregate buffer returns the result.  No row is required since we
+              // already added all rows in the group using update.
+              aggregateResults(i) = currentBuffer(i).apply(EmptyRow)
+              i += 1
+            }
+            resultProjection(joinedRow(aggregateResults, currentGroup))
+          }
+        }
+      }
     }
   }
 }

sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala

@@ -337,13 +337,16 @@ case class HiveGenericUdaf(
 
   type UDFType = AbstractGenericUDAFResolver
 
+  @transient
   protected lazy val resolver: AbstractGenericUDAFResolver = createFunction(name)
 
+  @transient
   protected lazy val objectInspector  = {
     resolver.getEvaluator(children.map(_.dataType.toTypeInfo).toArray)
       .init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors.toArray)
   }
 
+  @transient
   protected lazy val inspectors = children.map(_.dataType).map(toInspector)
 
   def dataType: DataType = inspectorToDataType(objectInspector)