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/*
* 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 java.util.Properties
import org.apache.spark.util.{Utils, Distribution}
import org.apache.spark.{Logging, TaskEndReason}
import org.apache.spark.executor.TaskMetrics
sealed trait SparkListenerEvents
case class SparkListenerStageSubmitted(stage: StageInfo, properties: Properties)
extends SparkListenerEvents
case class SparkListenerStageCompleted(stage: StageInfo) extends SparkListenerEvents
case class SparkListenerTaskStart(task: Task[_], taskInfo: TaskInfo) extends SparkListenerEvents
case class SparkListenerTaskGettingResult(
task: Task[_], taskInfo: TaskInfo) extends SparkListenerEvents
case class SparkListenerTaskEnd(task: Task[_], reason: TaskEndReason, taskInfo: TaskInfo,
taskMetrics: TaskMetrics) extends SparkListenerEvents
case class SparkListenerJobStart(job: ActiveJob, stageIds: Array[Int], properties: Properties = null)
extends SparkListenerEvents
case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult)
extends SparkListenerEvents
/** An event used in the listener to shutdown the listener daemon thread. */
private[scheduler] case object SparkListenerShutdown extends SparkListenerEvents
/**
* Interface for listening to events from the Spark scheduler.
*/
trait SparkListener {
/**
* Called when a stage is completed, with information on the completed stage
*/
def onStageCompleted(stageCompleted: SparkListenerStageCompleted) { }
/**
* Called when a stage is submitted
*/
def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) { }
/**
* Called when a task starts
*/
def onTaskStart(taskStart: SparkListenerTaskStart) { }
/**
* Called when a task begins remotely fetching its result (will not be called for tasks that do
* not need to fetch the result remotely).
*/
def onTaskGettingResult(taskGettingResult: SparkListenerTaskGettingResult) { }
/**
* Called when a task ends
*/
def onTaskEnd(taskEnd: SparkListenerTaskEnd) { }
/**
* Called when a job starts
*/
def onJobStart(jobStart: SparkListenerJobStart) { }
/**
* Called when a job ends
*/
def onJobEnd(jobEnd: SparkListenerJobEnd) { }
}
/**
* Simple SparkListener that logs a few summary statistics when each stage completes
*/
class StatsReportListener extends SparkListener with Logging {
override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) {
import org.apache.spark.scheduler.StatsReportListener._
implicit val sc = stageCompleted
this.logInfo("Finished stage: " + stageCompleted.stage)
showMillisDistribution("task runtime:", (info, _) => Some(info.duration))
//shuffle write
showBytesDistribution("shuffle bytes written:",(_,metric) => metric.shuffleWriteMetrics.map{_.shuffleBytesWritten})
//fetch & io
showMillisDistribution("fetch wait time:",(_, metric) => metric.shuffleReadMetrics.map{_.fetchWaitTime})
showBytesDistribution("remote bytes read:", (_, metric) => metric.shuffleReadMetrics.map{_.remoteBytesRead})
showBytesDistribution("task result size:", (_, metric) => Some(metric.resultSize))
//runtime breakdown
val runtimePcts = stageCompleted.stage.taskInfos.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 %%")
}
}
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(stage: SparkListenerStageCompleted,
getMetric: (TaskInfo,TaskMetrics) => Option[Double])
: Option[Distribution] = {
Distribution(stage.stage.taskInfos.flatMap {
case ((info,metric)) => getMetric(info, metric)})
}
//is there some way to setup the types that I can get rid of this completely?
def extractLongDistribution(stage: SparkListenerStageCompleted,
getMetric: (TaskInfo,TaskMetrics) => Option[Long])
: Option[Distribution] = {
extractDoubleDistribution(stage, (info, metric) => getMetric(info,metric).map{_.toDouble})
}
def showDistribution(heading: String, d: Distribution, formatNumber: Double => String) {
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) {
dOpt.foreach { d => showDistribution(heading, d, formatNumber)}
}
def showDistribution(heading: String, dOpt: Option[Distribution], format:String) {
def f(d:Double) = format.format(d)
showDistribution(heading, dOpt, f _)
}
def showDistribution(heading:String, format: String, getMetric: (TaskInfo,TaskMetrics) => Option[Double])
(implicit stage: SparkListenerStageCompleted) {
showDistribution(heading, extractDoubleDistribution(stage, getMetric), format)
}
def showBytesDistribution(heading:String, getMetric: (TaskInfo,TaskMetrics) => Option[Long])
(implicit stage: SparkListenerStageCompleted) {
showBytesDistribution(heading, extractLongDistribution(stage, getMetric))
}
def showBytesDistribution(heading: String, dOpt: Option[Distribution]) {
dOpt.foreach{dist => showBytesDistribution(heading, dist)}
}
def showBytesDistribution(heading: String, dist: Distribution) {
showDistribution(heading, dist, (d => Utils.bytesToString(d.toLong)): Double => String)
}
def showMillisDistribution(heading: String, dOpt: Option[Distribution]) {
showDistribution(heading, dOpt, (d => StatsReportListener.millisToString(d.toLong)): Double => String)
}
def showMillisDistribution(heading: String, getMetric: (TaskInfo, TaskMetrics) => Option[Long])
(implicit stage: SparkListenerStageCompleted) {
showMillisDistribution(heading, extractLongDistribution(stage, 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) = {
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 = metrics.shuffleReadMetrics.map{_.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)
}
}
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