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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.executor
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import org.apache.spark._
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.scheduler.AccumulableInfo
import org.apache.spark.storage.{BlockId, BlockStatus}
/**
* :: DeveloperApi ::
* Metrics tracked during the execution of a task.
*
* This class is wrapper around a collection of internal accumulators that represent metrics
* associated with a task. The local values of these accumulators are sent from the executor
* to the driver when the task completes. These values are then merged into the corresponding
* accumulator previously registered on the driver.
*
* The accumulator updates are also sent to the driver periodically (on executor heartbeat)
* and when the task failed with an exception. The [[TaskMetrics]] object itself should never
* be sent to the driver.
*
* @param initialAccums the initial set of accumulators that this [[TaskMetrics]] depends on.
* Each accumulator in this initial set must be uniquely named and marked
* as internal. Additional accumulators registered later need not satisfy
* these requirements.
*/
@DeveloperApi
class TaskMetrics(initialAccums: Seq[Accumulator[_]]) extends Serializable {
import InternalAccumulator._
// Needed for Java tests
def this() {
this(InternalAccumulator.create())
}
/**
* All accumulators registered with this task.
*/
private val accums = new ArrayBuffer[Accumulable[_, _]]
accums ++= initialAccums
/**
* A map for quickly accessing the initial set of accumulators by name.
*/
private val initialAccumsMap: Map[String, Accumulator[_]] = {
val map = new mutable.HashMap[String, Accumulator[_]]
initialAccums.foreach { a =>
val name = a.name.getOrElse {
throw new IllegalArgumentException(
"initial accumulators passed to TaskMetrics must be named")
}
require(a.isInternal,
s"initial accumulator '$name' passed to TaskMetrics must be marked as internal")
require(!map.contains(name),
s"detected duplicate accumulator name '$name' when constructing TaskMetrics")
map(name) = a
}
map.toMap
}
// Each metric is internally represented as an accumulator
private val _executorDeserializeTime = getAccum(EXECUTOR_DESERIALIZE_TIME)
private val _executorRunTime = getAccum(EXECUTOR_RUN_TIME)
private val _resultSize = getAccum(RESULT_SIZE)
private val _jvmGCTime = getAccum(JVM_GC_TIME)
private val _resultSerializationTime = getAccum(RESULT_SERIALIZATION_TIME)
private val _memoryBytesSpilled = getAccum(MEMORY_BYTES_SPILLED)
private val _diskBytesSpilled = getAccum(DISK_BYTES_SPILLED)
private val _peakExecutionMemory = getAccum(PEAK_EXECUTION_MEMORY)
private val _updatedBlockStatuses =
TaskMetrics.getAccum[Seq[(BlockId, BlockStatus)]](initialAccumsMap, UPDATED_BLOCK_STATUSES)
/**
* Time taken on the executor to deserialize this task.
*/
def executorDeserializeTime: Long = _executorDeserializeTime.localValue
/**
* Time the executor spends actually running the task (including fetching shuffle data).
*/
def executorRunTime: Long = _executorRunTime.localValue
/**
* The number of bytes this task transmitted back to the driver as the TaskResult.
*/
def resultSize: Long = _resultSize.localValue
/**
* Amount of time the JVM spent in garbage collection while executing this task.
*/
def jvmGCTime: Long = _jvmGCTime.localValue
/**
* Amount of time spent serializing the task result.
*/
def resultSerializationTime: Long = _resultSerializationTime.localValue
/**
* The number of in-memory bytes spilled by this task.
*/
def memoryBytesSpilled: Long = _memoryBytesSpilled.localValue
/**
* The number of on-disk bytes spilled by this task.
*/
def diskBytesSpilled: Long = _diskBytesSpilled.localValue
/**
* Peak memory used by internal data structures created during shuffles, aggregations and
* joins. The value of this accumulator should be approximately the sum of the peak sizes
* across all such data structures created in this task. For SQL jobs, this only tracks all
* unsafe operators and ExternalSort.
*/
def peakExecutionMemory: Long = _peakExecutionMemory.localValue
/**
* Storage statuses of any blocks that have been updated as a result of this task.
*/
def updatedBlockStatuses: Seq[(BlockId, BlockStatus)] = _updatedBlockStatuses.localValue
@deprecated("use updatedBlockStatuses instead", "2.0.0")
def updatedBlocks: Option[Seq[(BlockId, BlockStatus)]] = {
if (updatedBlockStatuses.nonEmpty) Some(updatedBlockStatuses) else None
}
// Setters and increment-ers
private[spark] def setExecutorDeserializeTime(v: Long): Unit =
_executorDeserializeTime.setValue(v)
private[spark] def setExecutorRunTime(v: Long): Unit = _executorRunTime.setValue(v)
private[spark] def setResultSize(v: Long): Unit = _resultSize.setValue(v)
private[spark] def setJvmGCTime(v: Long): Unit = _jvmGCTime.setValue(v)
private[spark] def setResultSerializationTime(v: Long): Unit =
_resultSerializationTime.setValue(v)
private[spark] def incMemoryBytesSpilled(v: Long): Unit = _memoryBytesSpilled.add(v)
private[spark] def incDiskBytesSpilled(v: Long): Unit = _diskBytesSpilled.add(v)
private[spark] def incPeakExecutionMemory(v: Long): Unit = _peakExecutionMemory.add(v)
private[spark] def incUpdatedBlockStatuses(v: Seq[(BlockId, BlockStatus)]): Unit =
_updatedBlockStatuses.add(v)
private[spark] def setUpdatedBlockStatuses(v: Seq[(BlockId, BlockStatus)]): Unit =
_updatedBlockStatuses.setValue(v)
/**
* Get a Long accumulator from the given map by name, assuming it exists.
* Note: this only searches the initial set of accumulators passed into the constructor.
*/
private[spark] def getAccum(name: String): Accumulator[Long] = {
TaskMetrics.getAccum[Long](initialAccumsMap, name)
}
/* ========================== *
| INPUT METRICS |
* ========================== */
private var _inputMetrics: Option[InputMetrics] = None
/**
* Metrics related to reading data from a [[org.apache.spark.rdd.HadoopRDD]] or from persisted
* data, defined only in tasks with input.
*/
def inputMetrics: Option[InputMetrics] = _inputMetrics
/**
* Get or create a new [[InputMetrics]] associated with this task.
*/
private[spark] def registerInputMetrics(readMethod: DataReadMethod.Value): InputMetrics = {
synchronized {
val metrics = _inputMetrics.getOrElse {
val metrics = new InputMetrics(initialAccumsMap)
metrics.setReadMethod(readMethod)
_inputMetrics = Some(metrics)
metrics
}
// If there already exists an InputMetric with the same read method, we can just return
// that one. Otherwise, if the read method is different from the one previously seen by
// this task, we return a new dummy one to avoid clobbering the values of the old metrics.
// In the future we should try to store input metrics from all different read methods at
// the same time (SPARK-5225).
if (metrics.readMethod == readMethod) {
metrics
} else {
val m = new InputMetrics
m.setReadMethod(readMethod)
m
}
}
}
/* ============================ *
| OUTPUT METRICS |
* ============================ */
private var _outputMetrics: Option[OutputMetrics] = None
/**
* Metrics related to writing data externally (e.g. to a distributed filesystem),
* defined only in tasks with output.
*/
def outputMetrics: Option[OutputMetrics] = _outputMetrics
/**
* Get or create a new [[OutputMetrics]] associated with this task.
*/
private[spark] def registerOutputMetrics(
writeMethod: DataWriteMethod.Value): OutputMetrics = synchronized {
_outputMetrics.getOrElse {
val metrics = new OutputMetrics(initialAccumsMap)
metrics.setWriteMethod(writeMethod)
_outputMetrics = Some(metrics)
metrics
}
}
/* ================================== *
| SHUFFLE READ METRICS |
* ================================== */
private var _shuffleReadMetrics: Option[ShuffleReadMetrics] = None
/**
* Metrics related to shuffle read aggregated across all shuffle dependencies.
* This is defined only if there are shuffle dependencies in this task.
*/
def shuffleReadMetrics: Option[ShuffleReadMetrics] = _shuffleReadMetrics
/**
* Temporary list of [[ShuffleReadMetrics]], one per shuffle dependency.
*
* A task may have multiple shuffle readers for multiple dependencies. To avoid synchronization
* issues from readers in different threads, in-progress tasks use a [[ShuffleReadMetrics]] for
* each dependency and merge these metrics before reporting them to the driver.
*/
@transient private lazy val tempShuffleReadMetrics = new ArrayBuffer[ShuffleReadMetrics]
/**
* Create a temporary [[ShuffleReadMetrics]] for a particular shuffle dependency.
*
* All usages are expected to be followed by a call to [[mergeShuffleReadMetrics]], which
* merges the temporary values synchronously. Otherwise, all temporary data collected will
* be lost.
*/
private[spark] def registerTempShuffleReadMetrics(): ShuffleReadMetrics = synchronized {
val readMetrics = new ShuffleReadMetrics
tempShuffleReadMetrics += readMetrics
readMetrics
}
/**
* Merge values across all temporary [[ShuffleReadMetrics]] into `_shuffleReadMetrics`.
* This is expected to be called on executor heartbeat and at the end of a task.
*/
private[spark] def mergeShuffleReadMetrics(): Unit = synchronized {
if (tempShuffleReadMetrics.nonEmpty) {
val metrics = new ShuffleReadMetrics(initialAccumsMap)
metrics.setRemoteBlocksFetched(tempShuffleReadMetrics.map(_.remoteBlocksFetched).sum)
metrics.setLocalBlocksFetched(tempShuffleReadMetrics.map(_.localBlocksFetched).sum)
metrics.setFetchWaitTime(tempShuffleReadMetrics.map(_.fetchWaitTime).sum)
metrics.setRemoteBytesRead(tempShuffleReadMetrics.map(_.remoteBytesRead).sum)
metrics.setLocalBytesRead(tempShuffleReadMetrics.map(_.localBytesRead).sum)
metrics.setRecordsRead(tempShuffleReadMetrics.map(_.recordsRead).sum)
_shuffleReadMetrics = Some(metrics)
}
}
/* =================================== *
| SHUFFLE WRITE METRICS |
* =================================== */
private var _shuffleWriteMetrics: Option[ShuffleWriteMetrics] = None
/**
* Metrics related to shuffle write, defined only in shuffle map stages.
*/
def shuffleWriteMetrics: Option[ShuffleWriteMetrics] = _shuffleWriteMetrics
/**
* Get or create a new [[ShuffleWriteMetrics]] associated with this task.
*/
private[spark] def registerShuffleWriteMetrics(): ShuffleWriteMetrics = synchronized {
_shuffleWriteMetrics.getOrElse {
val metrics = new ShuffleWriteMetrics(initialAccumsMap)
_shuffleWriteMetrics = Some(metrics)
metrics
}
}
/* ========================== *
| OTHER THINGS |
* ========================== */
private[spark] def registerAccumulator(a: Accumulable[_, _]): Unit = {
accums += a
}
/**
* Return the latest updates of accumulators in this task.
*
* The [[AccumulableInfo.update]] field is always defined and the [[AccumulableInfo.value]]
* field is always empty, since this represents the partial updates recorded in this task,
* not the aggregated value across multiple tasks.
*/
def accumulatorUpdates(): Seq[AccumulableInfo] = {
accums.map { a => a.toInfo(Some(a.localValue), None) }
}
// If we are reconstructing this TaskMetrics on the driver, some metrics may already be set.
// If so, initialize all relevant metrics classes so listeners can access them downstream.
{
var (hasShuffleRead, hasShuffleWrite, hasInput, hasOutput) = (false, false, false, false)
initialAccums
.filter { a => a.localValue != a.zero }
.foreach { a =>
a.name.get match {
case sr if sr.startsWith(SHUFFLE_READ_METRICS_PREFIX) => hasShuffleRead = true
case sw if sw.startsWith(SHUFFLE_WRITE_METRICS_PREFIX) => hasShuffleWrite = true
case in if in.startsWith(INPUT_METRICS_PREFIX) => hasInput = true
case out if out.startsWith(OUTPUT_METRICS_PREFIX) => hasOutput = true
case _ =>
}
}
if (hasShuffleRead) { _shuffleReadMetrics = Some(new ShuffleReadMetrics(initialAccumsMap)) }
if (hasShuffleWrite) { _shuffleWriteMetrics = Some(new ShuffleWriteMetrics(initialAccumsMap)) }
if (hasInput) { _inputMetrics = Some(new InputMetrics(initialAccumsMap)) }
if (hasOutput) { _outputMetrics = Some(new OutputMetrics(initialAccumsMap)) }
}
}
private[spark] object TaskMetrics extends Logging {
def empty: TaskMetrics = new TaskMetrics
/**
* Get an accumulator from the given map by name, assuming it exists.
*/
def getAccum[T](accumMap: Map[String, Accumulator[_]], name: String): Accumulator[T] = {
require(accumMap.contains(name), s"metric '$name' is missing")
val accum = accumMap(name)
try {
// Note: we can't do pattern matching here because types are erased by compile time
accum.asInstanceOf[Accumulator[T]]
} catch {
case e: ClassCastException =>
throw new SparkException(s"accumulator $name was of unexpected type", e)
}
}
/**
* Construct a [[TaskMetrics]] object from a list of accumulator updates, called on driver only.
*
* Executors only send accumulator updates back to the driver, not [[TaskMetrics]]. However, we
* need the latter to post task end events to listeners, so we need to reconstruct the metrics
* on the driver.
*
* This assumes the provided updates contain the initial set of accumulators representing
* internal task level metrics.
*/
def fromAccumulatorUpdates(accumUpdates: Seq[AccumulableInfo]): TaskMetrics = {
// Initial accumulators are passed into the TaskMetrics constructor first because these
// are required to be uniquely named. The rest of the accumulators from this task are
// registered later because they need not satisfy this requirement.
val (initialAccumInfos, otherAccumInfos) = accumUpdates
.filter { info => info.update.isDefined }
.partition { info => info.name.exists(_.startsWith(InternalAccumulator.METRICS_PREFIX)) }
val initialAccums = initialAccumInfos.map { info =>
val accum = InternalAccumulator.create(info.name.get)
accum.setValueAny(info.update.get)
accum
}
// We don't know the types of the rest of the accumulators, so we try to find the same ones
// that were previously registered here on the driver and make copies of them. It is important
// that we copy the accumulators here since they are used across many tasks and we want to
// maintain a snapshot of their local task values when we post them to listeners downstream.
val otherAccums = otherAccumInfos.flatMap { info =>
val id = info.id
val acc = Accumulators.get(id).map { a =>
val newAcc = a.copy()
newAcc.setValueAny(info.update.get)
newAcc
}
if (acc.isEmpty) {
logWarning(s"encountered unregistered accumulator $id when reconstructing task metrics.")
}
acc
}
val metrics = new TaskMetrics(initialAccums)
otherAccums.foreach(metrics.registerAccumulator)
metrics
}
}
|