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• ShuffledBatchRDD.scala

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org.apache.spark.sql.rapids.execution.ShuffledBatchRDD.scala Maven / Gradle / Ivy

/*
 * Licensed 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.
 */

// Most of this code came from ShuffledRowRDD in spark, with minor modifications

// In order to have metrics and preferred locations work we need to be in a spark sql package
package org.apache.spark.sql.rapids.execution

import java.util.Arrays

import com.nvidia.spark.rapids.GpuMetric

import org.apache.spark.{Dependency, Partition, Partitioner, ShuffleDependency, TaskContext}
import org.apache.spark.rapids.shims.ShuffledBatchRDDUtil
import org.apache.spark.rdd.RDD
import org.apache.spark.shuffle.sort.SortShuffleManager
import org.apache.spark.sql.execution.{CoalescedPartitioner, CoalescedPartitionSpec, ShufflePartitionSpec}
import org.apache.spark.sql.execution.metric.{SQLMetric, SQLShuffleReadMetricsReporter}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.vectorized.ColumnarBatch

case class ShuffledBatchRDDPartition(index: Int, spec: ShufflePartitionSpec) extends Partition

/**
 * A dummy partitioner for use with records whose partition ids have been pre-computed (i.e. for
 * use on RDDs of (Int, Row) pairs where the Int is a partition id in the expected range).
 */
class BatchPartitionIdPassthrough(override val numPartitions: Int) extends Partitioner {
  override def getPartition(key: Any): Int = key.asInstanceOf[Int]
}

/**
 * A Partitioner that might group together one or more partitions from the parent.
 *
 * @param parent a parent partitioner
 * @param partitionStartIndices indices of partitions in parent that should create new partitions
 *   in child (this should be an array of increasing partition IDs). For example, if we have a
 *   parent with 5 partitions, and partitionStartIndices is [0, 2, 4], we get three output
 *   partitions, corresponding to partition ranges [0, 1], [2, 3] and [4] of the parent partitioner.
 */
class CoalescedBatchPartitioner(val parent: Partitioner, val partitionStartIndices: Array[Int])
  extends Partitioner {

  @transient private lazy val parentPartitionMapping: Array[Int] = {
    val n = parent.numPartitions
    val result = new Array[Int](n)
    for (i <- 0 until partitionStartIndices.length) {
      val start = partitionStartIndices(i)
      val end = if (i < partitionStartIndices.length - 1) partitionStartIndices(i + 1) else n
      for (j <- start until end) {
        result(j) = i
      }
    }
    result
  }

  override def numPartitions: Int = partitionStartIndices.length

  override def getPartition(key: Any): Int = {
    parentPartitionMapping(parent.getPartition(key))
  }

  override def equals(other: Any): Boolean = other match {
    case c: CoalescedBatchPartitioner =>
      c.parent == parent && Arrays.equals(c.partitionStartIndices, partitionStartIndices)
    case _ =>
      false
  }

  override def hashCode(): Int = 31 * parent.hashCode() + Arrays.hashCode(partitionStartIndices)
}

/**
 * This is a specialized version of `org.apache.spark.rdd.ShuffledRDD` that is optimized for
 * shuffling `ColumnarBatch` instead of Java key-value pairs.
 *
 * This RDD takes a `ShuffleDependency` (`dependency`),
 * and an array of `ShufflePartitionSpec` as input arguments.
 *
 * The `dependency` has the parent RDD of this RDD, which represents the dataset before shuffle
 * (i.e. map output). Elements of this RDD are (partitionId, Row) pairs.
 * Partition ids should be in the range [0, numPartitions - 1].
 * `dependency.partitioner` is the original partitioner used to partition
 * map output, and `dependency.partitioner.numPartitions` is the number of pre-shuffle partitions
 * (i.e. the number of partitions of the map output).
 *
 * This code is made to try and match the Spark code as closely as possible to make maintenance
 * simpler. Fixing compiler or IDE warnings in this code may not be ideal if the same warnings are
 * in Spark.
 */
class ShuffledBatchRDD(
    var dependency: ShuffleDependency[Int, ColumnarBatch, ColumnarBatch],
    metrics: Map[String, SQLMetric],
    partitionSpecs: Array[ShufflePartitionSpec])
  extends RDD[ColumnarBatch](dependency.rdd.context, Nil) {

  def this(
      dependency: ShuffleDependency[Int, ColumnarBatch, ColumnarBatch],
      metrics: Map[String, SQLMetric]) = {
    this(dependency, metrics,
      Array.tabulate(dependency.partitioner.numPartitions)(i => CoalescedPartitionSpec(i, i + 1)))
  }

  dependency.rdd.context.setLocalProperty(
    SortShuffleManager.FETCH_SHUFFLE_BLOCKS_IN_BATCH_ENABLED_KEY,
    SQLConf.get.fetchShuffleBlocksInBatch.toString)

  override def getDependencies: Seq[Dependency[_]] = List(dependency)

  override val partitioner: Option[Partitioner] =
    if (partitionSpecs.forall(_.isInstanceOf[CoalescedPartitionSpec])) {
      val indices = partitionSpecs.map(_.asInstanceOf[CoalescedPartitionSpec].startReducerIndex)
      // TODO this check is based on assumptions of callers' behavior but is sufficient for now.
      if (indices.toSet.size == partitionSpecs.length) {
        Some(new CoalescedPartitioner(dependency.partitioner, indices))
      } else {
        None
      }
    } else {
      None
    }

  override def getPartitions: Array[Partition] = {
    Array.tabulate[Partition](partitionSpecs.length) { i =>
      ShuffledBatchRDDPartition(i, partitionSpecs(i))
    }
  }

  override def getPreferredLocations(partition: Partition): Seq[String] =
    ShuffledBatchRDDUtil.preferredLocations(partition, dependency)

  override def compute(split: Partition, context: TaskContext): Iterator[ColumnarBatch] = {
    val tempMetrics = context.taskMetrics().createTempShuffleReadMetrics()
    // `SQLShuffleReadMetricsReporter` will update its own metrics for SQL exchange operator,
    // as well as the `tempMetrics` for basic shuffle metrics.
    val sqlMetricsReporter = new SQLShuffleReadMetricsReporter(tempMetrics, metrics)
    val (reader, partitionSize) = ShuffledBatchRDDUtil.getReaderAndPartSize(split, context,
      dependency, sqlMetricsReporter)
    metrics(GpuMetric.NUM_PARTITIONS).add(1)
    metrics(GpuMetric.PARTITION_SIZE).add(partitionSize)
    reader.read().asInstanceOf[Iterator[Product2[Int, ColumnarBatch]]].map(_._2)
  }

  override def clearDependencies(): Unit = {
    super.clearDependencies()
    dependency = null
  }
}