spark MergingSessionsExec 源码

spark MergingSessionsExec 代码

文件路径：/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/MergingSessionsExec.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.sql.execution.aggregate

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, Expression, MutableProjection, NamedExpression, SortOrder, UnsafeRow}
import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
import org.apache.spark.sql.execution.SparkPlan
import org.apache.spark.sql.execution.metric.SQLMetrics

/**
 * This node is a variant of SortAggregateExec which merges the session windows based on the fact
 * child node will provide inputs as sorted by group keys + the start time of session window.
 *
 * When merging windows, it also applies aggregations on merged window, which eliminates the
 * necessity on buffering inputs (which requires copying rows) and update the session spec
 * for each input.
 *
 * This class receives requiredChildDistribution from caller, to enable merging session in
 * local partition before shuffling. Specifying both parameters to None won't trigger shuffle,
 * but sort would still happen per local partition.
 *
 * Refer [[MergingSessionsIterator]] for more details.
 */
case class MergingSessionsExec(
    requiredChildDistributionExpressions: Option[Seq[Expression]],
    isStreaming: Boolean,
    numShufflePartitions: Option[Int],
    groupingExpressions: Seq[NamedExpression],
    sessionExpression: NamedExpression,
    aggregateExpressions: Seq[AggregateExpression],
    aggregateAttributes: Seq[Attribute],
    initialInputBufferOffset: Int,
    resultExpressions: Seq[NamedExpression],
    child: SparkPlan) extends BaseAggregateExec {

  private val keyWithoutSessionExpressions = groupingExpressions.diff(Seq(sessionExpression))

  override lazy val metrics = Map(
    "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"))

  override def output: Seq[Attribute] = child.output

  override def outputOrdering: Seq[SortOrder] = child.outputOrdering

  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
    Seq((keyWithoutSessionExpressions ++ Seq(sessionExpression)).map(SortOrder(_, Ascending)))
  }

  override protected def doExecute(): RDD[InternalRow] = {
    val numOutputRows = longMetric("numOutputRows")
    child.execute().mapPartitionsWithIndexInternal { (partIndex, iter) =>
      // Because the constructor of an aggregation iterator will read at least the first row,
      // we need to get the value of iter.hasNext first.
      val hasInput = iter.hasNext
      if (!hasInput && groupingExpressions.nonEmpty) {
        // This is a grouped aggregate and the input iterator is empty,
        // so return an empty iterator.
        Iterator[UnsafeRow]()
      } else {
        val outputIter = new MergingSessionsIterator(
          partIndex,
          groupingExpressions,
          sessionExpression,
          child.output,
          iter,
          aggregateExpressions,
          aggregateAttributes,
          initialInputBufferOffset,
          resultExpressions,
          (expressions, inputSchema) =>
            MutableProjection.create(expressions, inputSchema),
          numOutputRows)
        if (!hasInput && groupingExpressions.isEmpty) {
          // There is no input and there is no grouping expressions.
          // We need to output a single row as the output.
          numOutputRows += 1
          Iterator[UnsafeRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
        } else {
          outputIter
        }
      }
    }
  }

  override protected def withNewChildInternal(newChild: SparkPlan): MergingSessionsExec =
    copy(child = newChild)
}

相关信息

spark 源码目录

相关文章

spark AggUtils 源码

spark AggregateCodegenSupport 源码

spark AggregationIterator 源码

spark BaseAggregateExec 源码

spark HashAggregateExec 源码

spark HashMapGenerator 源码

spark MergingSessionsIterator 源码

spark ObjectAggregationIterator 源码

spark ObjectAggregationMap 源码

spark ObjectHashAggregateExec 源码