Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/295#discussion_r11279472
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala ---
@@ -55,61 +54,135 @@ 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
+ case class ComputedAggregate(
+ unbound: AggregateExpression, // Unbound aggregate used for
result substitution
+ aggregate: AggregateExpression, // A bound copy of this
aggregate used to create a buffer
+ resultAttribute: AttributeReference) // An attribute used to refer
to the result of this agg
+
+ // 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) {
+ aggregateResults(i) = currentBuffer(i).apply(EmptyRow)
--- End diff --
add one line comment explaining this jsut returns the agg result (no need
for a row)
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