Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/993#discussion_r15333888
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
---
@@ -0,0 +1,197 @@
+/*
+ * 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
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.catalyst.types._
+
+case class AggregateEvaluation(
+ schema: Seq[Attribute],
+ initialValues: Seq[Expression],
+ update: Seq[Expression],
+ result: Expression)
+
+/**
+ * :: DeveloperApi ::
+ * Alternate version of aggregation that leverages projection and thus
code generation.
+ * Aggregations are converted into a set of projections from a aggregation
buffer tuple back onto
+ * itself. Currently only used for simple aggregations like SUM, COUNT, or
AVERAGE are supported.
+ *
+ * @param partial if true then aggregation is done partially on local data
without shuffling to
+ * ensure all values where `groupingExpressions` are equal
are present.
+ * @param groupingExpressions expressions that are evaluated to determine
grouping.
+ * @param aggregateExpressions expressions that are computed for each
group.
+ * @param child the input data source.
+ */
+@DeveloperApi
+case class GeneratedAggregate(
+ partial: Boolean,
+ groupingExpressions: Seq[Expression],
+ aggregateExpressions: Seq[NamedExpression],
+ child: SparkPlan)
+ extends UnaryNode {
+
+ override def requiredChildDistribution =
+ if (partial) {
+ UnspecifiedDistribution :: Nil
+ } else {
+ if (groupingExpressions == Nil) {
+ AllTuples :: Nil
+ } else {
+ ClusteredDistribution(groupingExpressions) :: Nil
+ }
+ }
+
+ override def output = aggregateExpressions.map(_.toAttribute)
+
+ override def execute() = {
+ val aggregatesToCompute = aggregateExpressions.flatMap { a =>
+ a.collect { case agg: AggregateExpression => agg}
+ }
+
+ val computeFunctions = aggregatesToCompute.map {
+ case c @ Count(expr) =>
+ val currentCount = AttributeReference("currentCount", LongType,
nullable = false)()
+ val initialValue = Literal(0L)
+ val updateFunction = If(IsNotNull(expr), Add(currentCount,
Literal(1L)), currentCount)
+ val result = currentCount
+
+ AggregateEvaluation(currentCount :: Nil, initialValue :: Nil,
updateFunction :: Nil, result)
+
+ case Sum(expr) =>
+ val currentSum = AttributeReference("currentSum", expr.dataType,
nullable = false)()
+ val initialValue = Cast(Literal(0L), expr.dataType)
+
+ // Coalasce avoids double calculation...
+ // but really, common sub expression elimination would be
better....
+ val updateFunction = Coalesce(Add(expr, currentSum) :: currentSum
:: Nil)
+ val result = currentSum
+
+ AggregateEvaluation(currentSum :: Nil, initialValue :: Nil,
updateFunction :: Nil, result)
+
+ case a @ Average(expr) =>
+ val currentCount = AttributeReference("currentCount", LongType,
nullable = false)()
+ val currentSum = AttributeReference("currentSum", expr.dataType,
nullable = false)()
+ val initialCount = Literal(0L)
+ val initialSum = Cast(Literal(0L), expr.dataType)
+ val updateCount = If(IsNotNull(expr), Add(currentCount,
Literal(1L)), currentCount)
+ val updateSum = Coalesce(Add(expr, currentSum) :: currentSum ::
Nil)
+
+ val result = Divide(Cast(currentSum, DoubleType),
Cast(currentCount, DoubleType))
+
+ AggregateEvaluation(
+ currentCount :: currentSum :: Nil,
+ initialCount :: initialSum :: Nil,
+ updateCount :: updateSum :: Nil,
+ result
+ )
+ }
+
+ val computationSchema = computeFunctions.flatMap(_.schema)
+
+ val resultMap = aggregatesToCompute.zip(computeFunctions).map {
+ case (agg, func) => agg.id -> func.result
+ }.toMap
+
+ val namedGroups = groupingExpressions.zipWithIndex.map {
+ case (ne: NamedExpression, _) => (ne, ne)
+ case (e, i) => (e, Alias(e, s"GroupingExpr$i")())
+ }
+
+ val groupMap = namedGroups.map { case (k, v) => k ->
v.toAttribute}.toMap
+
+ val resultExpressions = aggregateExpressions.map(_.transform {
+ case e: Expression if resultMap.contains(e.id) => resultMap(e.id)
+ case e: Expression if groupMap.contains(e) => groupMap(e)
+ })
+
+ child.execute().mapPartitions { iter =>
+ // Builds a new custom class for holding the results of aggregation
for a group.
+ @transient
+ val newAggregationBuffer =
+ newProjection(computeFunctions.flatMap(_.initialValues),
child.output)
+
+ // A projection that is used to update the aggregate values for a
group given a new tuple.
+ // This projection should be targeted at the current values for the
group and then applied
+ // to a joined row of the current values with the new input row.
+ @transient
+ val updateProjection =
+ newMutableProjection(
+ computeFunctions.flatMap(_.update),
+ computeFunctions.flatMap(_.schema) ++ child.output)()
+
+ // A projection that computes the group given an input tuple.
+ @transient
+ val groupProjection = newProjection(groupingExpressions,
child.output)
+
+ // A projection that produces the final result, given a computation.
+ @transient
+ val resultProjectionBuilder =
+ newMutableProjection(
+ resultExpressions,
+ (namedGroups.map(_._2.toAttribute) ++ computationSchema).toSeq)
+
+ val joinedRow = new JoinedRow
+
+ if (groupingExpressions.isEmpty) {
+ // TODO: Codegening anything other than the updateProjection is
probably over kill.
+ val buffer =
newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
+ var currentRow: Row = null
+
+ while (iter.hasNext) {
+ currentRow = iter.next()
+ updateProjection.target(buffer)(joinedRow(buffer, currentRow))
--- End diff --
do we need to call target for every row? it seems to me updateProjection is
fixed, and updateProject.target(buffer) is also fixed?
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