spark StatsReportListener 源码

spark StatsReportListener 代码

文件路径：/core/src/main/scala/org/apache/spark/scheduler/StatsReportListener.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.scheduler

import scala.collection.mutable

import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.executor.TaskMetrics
import org.apache.spark.internal.Logging
import org.apache.spark.util.{Distribution, Utils}


/**
 * :: DeveloperApi ::
 * Simple SparkListener that logs a few summary statistics when each stage completes.
 */
@DeveloperApi
class StatsReportListener extends SparkListener with Logging {

  import org.apache.spark.scheduler.StatsReportListener._

  private val taskInfoMetrics = mutable.Buffer[(TaskInfo, TaskMetrics)]()

  override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
    val info = taskEnd.taskInfo
    val metrics = taskEnd.taskMetrics
    if (info != null && metrics != null) {
      taskInfoMetrics += ((info, metrics))
    }
  }

  override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
    implicit val sc = stageCompleted
    this.logInfo(s"Finished stage: ${getStatusDetail(stageCompleted.stageInfo)}")
    showMillisDistribution("task runtime:", (info, _) => info.duration, taskInfoMetrics.toSeq)

    // Shuffle write
    showBytesDistribution("shuffle bytes written:",
      (_, metric) => metric.shuffleWriteMetrics.bytesWritten, taskInfoMetrics.toSeq)

    // Fetch & I/O
    showMillisDistribution("fetch wait time:",
      (_, metric) => metric.shuffleReadMetrics.fetchWaitTime, taskInfoMetrics.toSeq)
    showBytesDistribution("remote bytes read:",
      (_, metric) => metric.shuffleReadMetrics.remoteBytesRead, taskInfoMetrics.toSeq)
    showBytesDistribution("task result size:",
      (_, metric) => metric.resultSize, taskInfoMetrics.toSeq)

    // Runtime breakdown
    val runtimePcts = taskInfoMetrics.map { case (info, metrics) =>
      RuntimePercentage(info.duration, metrics)
    }
    showDistribution("executor (non-fetch) time pct: ",
      Distribution(runtimePcts.map(_.executorPct * 100)), "%2.0f %%")
    showDistribution("fetch wait time pct: ",
      Distribution(runtimePcts.flatMap(_.fetchPct.map(_ * 100))), "%2.0f %%")
    showDistribution("other time pct: ", Distribution(runtimePcts.map(_.other * 100)), "%2.0f %%")
    taskInfoMetrics.clear()
  }

  private def getStatusDetail(info: StageInfo): String = {
    val failureReason = info.failureReason.map("(" + _ + ")").getOrElse("")
    val timeTaken = info.submissionTime.map(
      x => info.completionTime.getOrElse(System.currentTimeMillis()) - x
    ).getOrElse("-")

    s"Stage(${info.stageId}, ${info.attemptNumber}); Name: '${info.name}'; " +
      s"Status: ${info.getStatusString}$failureReason; numTasks: ${info.numTasks}; " +
      s"Took: $timeTaken msec"
  }

}

private[spark] object StatsReportListener extends Logging {

  // For profiling, the extremes are more interesting
  val percentiles = Array[Int](0, 5, 10, 25, 50, 75, 90, 95, 100)
  val probabilities = percentiles.map(_ / 100.0)
  val percentilesHeader = "\t" + percentiles.mkString("%\t") + "%"

  def extractDoubleDistribution(
    taskInfoMetrics: Seq[(TaskInfo, TaskMetrics)],
    getMetric: (TaskInfo, TaskMetrics) => Double): Option[Distribution] = {
    Distribution(taskInfoMetrics.map { case (info, metric) => getMetric(info, metric) })
  }

  // Is there some way to setup the types that I can get rid of this completely?
  def extractLongDistribution(
    taskInfoMetrics: Seq[(TaskInfo, TaskMetrics)],
    getMetric: (TaskInfo, TaskMetrics) => Long): Option[Distribution] = {
    extractDoubleDistribution(
      taskInfoMetrics,
      (info, metric) => { getMetric(info, metric).toDouble })
  }

  def showDistribution(heading: String, d: Distribution, formatNumber: Double => String): Unit = {
    val stats = d.statCounter
    val quantiles = d.getQuantiles(probabilities).map(formatNumber)
    logInfo(heading + stats)
    logInfo(percentilesHeader)
    logInfo("\t" + quantiles.mkString("\t"))
  }

  def showDistribution(
      heading: String,
      dOpt: Option[Distribution],
      formatNumber: Double => String): Unit = {
    dOpt.foreach { d => showDistribution(heading, d, formatNumber)}
  }

  def showDistribution(heading: String, dOpt: Option[Distribution], format: String): Unit = {
    def f(d: Double): String = format.format(d)
    showDistribution(heading, dOpt, f _)
  }

  def showDistribution(
      heading: String,
      format: String,
      getMetric: (TaskInfo, TaskMetrics) => Double,
      taskInfoMetrics: Seq[(TaskInfo, TaskMetrics)]): Unit = {
    showDistribution(heading, extractDoubleDistribution(taskInfoMetrics, getMetric), format)
  }

  def showBytesDistribution(
      heading: String,
      getMetric: (TaskInfo, TaskMetrics) => Long,
      taskInfoMetrics: Seq[(TaskInfo, TaskMetrics)]): Unit = {
    showBytesDistribution(heading, extractLongDistribution(taskInfoMetrics, getMetric))
  }

  def showBytesDistribution(heading: String, dOpt: Option[Distribution]): Unit = {
    dOpt.foreach { dist => showBytesDistribution(heading, dist) }
  }

  def showBytesDistribution(heading: String, dist: Distribution): Unit = {
    showDistribution(heading, dist, (d => Utils.bytesToString(d.toLong)): Double => String)
  }

  def showMillisDistribution(heading: String, dOpt: Option[Distribution]): Unit = {
    showDistribution(heading, dOpt,
      (d => StatsReportListener.millisToString(d.toLong)): Double => String)
  }

  def showMillisDistribution(
      heading: String,
      getMetric: (TaskInfo, TaskMetrics) => Long,
      taskInfoMetrics: Seq[(TaskInfo, TaskMetrics)]): Unit = {
    showMillisDistribution(heading, extractLongDistribution(taskInfoMetrics, getMetric))
  }

  val seconds = 1000L
  val minutes = seconds * 60
  val hours = minutes * 60

  /**
   * Reformat a time interval in milliseconds to a prettier format for output
   */
  def millisToString(ms: Long): String = {
    val (size, units) =
      if (ms > hours) {
        (ms.toDouble / hours, "hours")
      } else if (ms > minutes) {
        (ms.toDouble / minutes, "min")
      } else if (ms > seconds) {
        (ms.toDouble / seconds, "s")
      } else {
        (ms.toDouble, "ms")
      }
    "%.1f %s".format(size, units)
  }
}

private case class RuntimePercentage(executorPct: Double, fetchPct: Option[Double], other: Double)

private object RuntimePercentage {
  def apply(totalTime: Long, metrics: TaskMetrics): RuntimePercentage = {
    val denom = totalTime.toDouble
    val fetchTime = Some(metrics.shuffleReadMetrics.fetchWaitTime)
    val fetch = fetchTime.map(_ / denom)
    val exec = (metrics.executorRunTime - fetchTime.getOrElse(0L)) / denom
    val other = 1.0 - (exec + fetch.getOrElse(0d))
    RuntimePercentage(exec, fetch, other)
  }
}

相关信息

spark 源码目录

相关文章

spark AccumulableInfo 源码

spark ActiveJob 源码

spark AsyncEventQueue 源码

spark BarrierJobAllocationFailed 源码

spark DAGScheduler 源码

spark DAGSchedulerEvent 源码

spark DAGSchedulerSource 源码

spark EventLoggingListener 源码

spark ExecutorDecommissionInfo 源码

spark ExecutorFailuresInTaskSet 源码