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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
import java.io._
import java.util.concurrent.{ConcurrentHashMap, LinkedBlockingQueue, ThreadPoolExecutor}
import java.util.zip.{GZIPInputStream, GZIPOutputStream}
import scala.collection.JavaConverters._
import scala.collection.mutable.{HashMap, HashSet, ListBuffer, Map}
import scala.concurrent.{ExecutionContext, Future}
import scala.concurrent.duration.Duration
import scala.reflect.ClassTag
import scala.util.control.NonFatal
import org.apache.spark.broadcast.{Broadcast, BroadcastManager}
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.rpc.{RpcCallContext, RpcEndpoint, RpcEndpointRef, RpcEnv}
import org.apache.spark.scheduler.MapStatus
import org.apache.spark.shuffle.MetadataFetchFailedException
import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockId}
import org.apache.spark.util._
/**
* Helper class used by the [[MapOutputTrackerMaster]] to perform bookkeeping for a single
* ShuffleMapStage.
*
* This class maintains a mapping from mapIds to `MapStatus`. It also maintains a cache of
* serialized map statuses in order to speed up tasks' requests for map output statuses.
*
* All public methods of this class are thread-safe.
*/
private class ShuffleStatus(numPartitions: Int) {
// All accesses to the following state must be guarded with `this.synchronized`.
/**
* MapStatus for each partition. The index of the array is the map partition id.
* Each value in the array is the MapStatus for a partition, or null if the partition
* is not available. Even though in theory a task may run multiple times (due to speculation,
* stage retries, etc.), in practice the likelihood of a map output being available at multiple
* locations is so small that we choose to ignore that case and store only a single location
* for each output.
*/
// Exposed for testing
val mapStatuses = new Array[MapStatus](numPartitions)
/**
* The cached result of serializing the map statuses array. This cache is lazily populated when
* [[serializedMapStatus]] is called. The cache is invalidated when map outputs are removed.
*/
private[this] var cachedSerializedMapStatus: Array[Byte] = _
/**
* Broadcast variable holding serialized map output statuses array. When [[serializedMapStatus]]
* serializes the map statuses array it may detect that the result is too large to send in a
* single RPC, in which case it places the serialized array into a broadcast variable and then
* sends a serialized broadcast variable instead. This variable holds a reference to that
* broadcast variable in order to keep it from being garbage collected and to allow for it to be
* explicitly destroyed later on when the ShuffleMapStage is garbage-collected.
*/
private[this] var cachedSerializedBroadcast: Broadcast[Array[Byte]] = _
/**
* Counter tracking the number of partitions that have output. This is a performance optimization
* to avoid having to count the number of non-null entries in the `mapStatuses` array and should
* be equivalent to`mapStatuses.count(_ ne null)`.
*/
private[this] var _numAvailableOutputs: Int = 0
/**
* Register a map output. If there is already a registered location for the map output then it
* will be replaced by the new location.
*/
def addMapOutput(mapId: Int, status: MapStatus): Unit = synchronized {
if (mapStatuses(mapId) == null) {
_numAvailableOutputs += 1
invalidateSerializedMapOutputStatusCache()
}
mapStatuses(mapId) = status
}
/**
* Remove the map output which was served by the specified block manager.
* This is a no-op if there is no registered map output or if the registered output is from a
* different block manager.
*/
def removeMapOutput(mapId: Int, bmAddress: BlockManagerId): Unit = synchronized {
if (mapStatuses(mapId) != null && mapStatuses(mapId).location == bmAddress) {
_numAvailableOutputs -= 1
mapStatuses(mapId) = null
invalidateSerializedMapOutputStatusCache()
}
}
/**
* Removes all shuffle outputs associated with this host. Note that this will also remove
* outputs which are served by an external shuffle server (if one exists).
*/
def removeOutputsOnHost(host: String): Unit = {
removeOutputsByFilter(x => x.host == host)
}
/**
* Removes all map outputs associated with the specified executor. Note that this will also
* remove outputs which are served by an external shuffle server (if one exists), as they are
* still registered with that execId.
*/
def removeOutputsOnExecutor(execId: String): Unit = synchronized {
removeOutputsByFilter(x => x.executorId == execId)
}
/**
* Removes all shuffle outputs which satisfies the filter. Note that this will also
* remove outputs which are served by an external shuffle server (if one exists).
*/
def removeOutputsByFilter(f: (BlockManagerId) => Boolean): Unit = synchronized {
for (mapId <- 0 until mapStatuses.length) {
if (mapStatuses(mapId) != null && f(mapStatuses(mapId).location)) {
_numAvailableOutputs -= 1
mapStatuses(mapId) = null
invalidateSerializedMapOutputStatusCache()
}
}
}
/**
* Number of partitions that have shuffle outputs.
*/
def numAvailableOutputs: Int = synchronized {
_numAvailableOutputs
}
/**
* Returns the sequence of partition ids that are missing (i.e. needs to be computed).
*/
def findMissingPartitions(): Seq[Int] = synchronized {
val missing = (0 until numPartitions).filter(id => mapStatuses(id) == null)
assert(missing.size == numPartitions - _numAvailableOutputs,
s"${missing.size} missing, expected ${numPartitions - _numAvailableOutputs}")
missing
}
/**
* Serializes the mapStatuses array into an efficient compressed format. See the comments on
* `MapOutputTracker.serializeMapStatuses()` for more details on the serialization format.
*
* This method is designed to be called multiple times and implements caching in order to speed
* up subsequent requests. If the cache is empty and multiple threads concurrently attempt to
* serialize the map statuses then serialization will only be performed in a single thread and all
* other threads will block until the cache is populated.
*/
def serializedMapStatus(
broadcastManager: BroadcastManager,
isLocal: Boolean,
minBroadcastSize: Int): Array[Byte] = synchronized {
if (cachedSerializedMapStatus eq null) {
val serResult = MapOutputTracker.serializeMapStatuses(
mapStatuses, broadcastManager, isLocal, minBroadcastSize)
cachedSerializedMapStatus = serResult._1
cachedSerializedBroadcast = serResult._2
}
cachedSerializedMapStatus
}
// Used in testing.
def hasCachedSerializedBroadcast: Boolean = synchronized {
cachedSerializedBroadcast != null
}
/**
* Helper function which provides thread-safe access to the mapStatuses array.
* The function should NOT mutate the array.
*/
def withMapStatuses[T](f: Array[MapStatus] => T): T = synchronized {
f(mapStatuses)
}
/**
* Clears the cached serialized map output statuses.
*/
def invalidateSerializedMapOutputStatusCache(): Unit = synchronized {
if (cachedSerializedBroadcast != null) {
// Prevent errors during broadcast cleanup from crashing the DAGScheduler (see SPARK-21444)
Utils.tryLogNonFatalError {
// Use `blocking = false` so that this operation doesn't hang while trying to send cleanup
// RPCs to dead executors.
cachedSerializedBroadcast.destroy(blocking = false)
}
cachedSerializedBroadcast = null
}
cachedSerializedMapStatus = null
}
}
private[spark] sealed trait MapOutputTrackerMessage
private[spark] case class GetMapOutputStatuses(shuffleId: Int)
extends MapOutputTrackerMessage
private[spark] case object StopMapOutputTracker extends MapOutputTrackerMessage
private[spark] case class GetMapOutputMessage(shuffleId: Int, context: RpcCallContext)
/** RpcEndpoint class for MapOutputTrackerMaster */
private[spark] class MapOutputTrackerMasterEndpoint(
override val rpcEnv: RpcEnv, tracker: MapOutputTrackerMaster, conf: SparkConf)
extends RpcEndpoint with Logging {
logDebug("init") // force eager creation of logger
override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
case GetMapOutputStatuses(shuffleId: Int) =>
val hostPort = context.senderAddress.hostPort
logInfo("Asked to send map output locations for shuffle " + shuffleId + " to " + hostPort)
val mapOutputStatuses = tracker.post(new GetMapOutputMessage(shuffleId, context))
case StopMapOutputTracker =>
logInfo("MapOutputTrackerMasterEndpoint stopped!")
context.reply(true)
stop()
}
}
/**
* Class that keeps track of the location of the map output of a stage. This is abstract because the
* driver and executor have different versions of the MapOutputTracker. In principle the driver-
* and executor-side classes don't need to share a common base class; the current shared base class
* is maintained primarily for backwards-compatibility in order to avoid having to update existing
* test code.
*/
private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging {
/** Set to the MapOutputTrackerMasterEndpoint living on the driver. */
var trackerEndpoint: RpcEndpointRef = _
/**
* The driver-side counter is incremented every time that a map output is lost. This value is sent
* to executors as part of tasks, where executors compare the new epoch number to the highest
* epoch number that they received in the past. If the new epoch number is higher then executors
* will clear their local caches of map output statuses and will re-fetch (possibly updated)
* statuses from the driver.
*/
protected var epoch: Long = 0
protected val epochLock = new AnyRef
/**
* Send a message to the trackerEndpoint and get its result within a default timeout, or
* throw a SparkException if this fails.
*/
protected def askTracker[T: ClassTag](message: Any): T = {
try {
trackerEndpoint.askSync[T](message)
} catch {
case e: Exception =>
logError("Error communicating with MapOutputTracker", e)
throw new SparkException("Error communicating with MapOutputTracker", e)
}
}
/** Send a one-way message to the trackerEndpoint, to which we expect it to reply with true. */
protected def sendTracker(message: Any) {
val response = askTracker[Boolean](message)
if (response != true) {
throw new SparkException(
"Error reply received from MapOutputTracker. Expecting true, got " + response.toString)
}
}
// For testing
def getMapSizesByExecutorId(shuffleId: Int, reduceId: Int)
: Iterator[(BlockManagerId, Seq[(BlockId, Long)])] = {
getMapSizesByExecutorId(shuffleId, reduceId, reduceId + 1)
}
/**
* Called from executors to get the server URIs and output sizes for each shuffle block that
* needs to be read from a given range of map output partitions (startPartition is included but
* endPartition is excluded from the range).
*
* @return A sequence of 2-item tuples, where the first item in the tuple is a BlockManagerId,
* and the second item is a sequence of (shuffle block id, shuffle block size) tuples
* describing the shuffle blocks that are stored at that block manager.
*/
def getMapSizesByExecutorId(shuffleId: Int, startPartition: Int, endPartition: Int)
: Iterator[(BlockManagerId, Seq[(BlockId, Long)])]
/**
* Deletes map output status information for the specified shuffle stage.
*/
def unregisterShuffle(shuffleId: Int): Unit
def stop() {}
}
/**
* Driver-side class that keeps track of the location of the map output of a stage.
*
* The DAGScheduler uses this class to (de)register map output statuses and to look up statistics
* for performing locality-aware reduce task scheduling.
*
* ShuffleMapStage uses this class for tracking available / missing outputs in order to determine
* which tasks need to be run.
*/
private[spark] class MapOutputTrackerMaster(
conf: SparkConf,
broadcastManager: BroadcastManager,
isLocal: Boolean)
extends MapOutputTracker(conf) {
// The size at which we use Broadcast to send the map output statuses to the executors
private val minSizeForBroadcast =
conf.getSizeAsBytes("spark.shuffle.mapOutput.minSizeForBroadcast", "512k").toInt
/** Whether to compute locality preferences for reduce tasks */
private val shuffleLocalityEnabled = conf.getBoolean("spark.shuffle.reduceLocality.enabled", true)
// Number of map and reduce tasks above which we do not assign preferred locations based on map
// output sizes. We limit the size of jobs for which assign preferred locations as computing the
// top locations by size becomes expensive.
private val SHUFFLE_PREF_MAP_THRESHOLD = 1000
// NOTE: This should be less than 2000 as we use HighlyCompressedMapStatus beyond that
private val SHUFFLE_PREF_REDUCE_THRESHOLD = 1000
// Fraction of total map output that must be at a location for it to considered as a preferred
// location for a reduce task. Making this larger will focus on fewer locations where most data
// can be read locally, but may lead to more delay in scheduling if those locations are busy.
private val REDUCER_PREF_LOCS_FRACTION = 0.2
// HashMap for storing shuffleStatuses in the driver.
// Statuses are dropped only by explicit de-registering.
// Exposed for testing
val shuffleStatuses = new ConcurrentHashMap[Int, ShuffleStatus]().asScala
private val maxRpcMessageSize = RpcUtils.maxMessageSizeBytes(conf)
// requests for map output statuses
private val mapOutputRequests = new LinkedBlockingQueue[GetMapOutputMessage]
// Thread pool used for handling map output status requests. This is a separate thread pool
// to ensure we don't block the normal dispatcher threads.
private val threadpool: ThreadPoolExecutor = {
val numThreads = conf.getInt("spark.shuffle.mapOutput.dispatcher.numThreads", 8)
val pool = ThreadUtils.newDaemonFixedThreadPool(numThreads, "map-output-dispatcher")
for (i <- 0 until numThreads) {
pool.execute(new MessageLoop)
}
pool
}
// Make sure that we aren't going to exceed the max RPC message size by making sure
// we use broadcast to send large map output statuses.
if (minSizeForBroadcast > maxRpcMessageSize) {
val msg = s"spark.shuffle.mapOutput.minSizeForBroadcast ($minSizeForBroadcast bytes) must " +
s"be <= spark.rpc.message.maxSize ($maxRpcMessageSize bytes) to prevent sending an rpc " +
"message that is too large."
logError(msg)
throw new IllegalArgumentException(msg)
}
def post(message: GetMapOutputMessage): Unit = {
mapOutputRequests.offer(message)
}
/** Message loop used for dispatching messages. */
private class MessageLoop extends Runnable {
override def run(): Unit = {
try {
while (true) {
try {
val data = mapOutputRequests.take()
if (data == PoisonPill) {
// Put PoisonPill back so that other MessageLoops can see it.
mapOutputRequests.offer(PoisonPill)
return
}
val context = data.context
val shuffleId = data.shuffleId
val hostPort = context.senderAddress.hostPort
logDebug("Handling request to send map output locations for shuffle " + shuffleId +
" to " + hostPort)
val shuffleStatus = shuffleStatuses.get(shuffleId).head
context.reply(
shuffleStatus.serializedMapStatus(broadcastManager, isLocal, minSizeForBroadcast))
} catch {
case NonFatal(e) => logError(e.getMessage, e)
}
}
} catch {
case ie: InterruptedException => // exit
}
}
}
/** A poison endpoint that indicates MessageLoop should exit its message loop. */
private val PoisonPill = new GetMapOutputMessage(-99, null)
// Used only in unit tests.
private[spark] def getNumCachedSerializedBroadcast: Int = {
shuffleStatuses.valuesIterator.count(_.hasCachedSerializedBroadcast)
}
def registerShuffle(shuffleId: Int, numMaps: Int) {
if (shuffleStatuses.put(shuffleId, new ShuffleStatus(numMaps)).isDefined) {
throw new IllegalArgumentException("Shuffle ID " + shuffleId + " registered twice")
}
}
def registerMapOutput(shuffleId: Int, mapId: Int, status: MapStatus) {
shuffleStatuses(shuffleId).addMapOutput(mapId, status)
}
/** Unregister map output information of the given shuffle, mapper and block manager */
def unregisterMapOutput(shuffleId: Int, mapId: Int, bmAddress: BlockManagerId) {
shuffleStatuses.get(shuffleId) match {
case Some(shuffleStatus) =>
shuffleStatus.removeMapOutput(mapId, bmAddress)
incrementEpoch()
case None =>
throw new SparkException("unregisterMapOutput called for nonexistent shuffle ID")
}
}
/** Unregister all map output information of the given shuffle. */
def unregisterAllMapOutput(shuffleId: Int) {
shuffleStatuses.get(shuffleId) match {
case Some(shuffleStatus) =>
shuffleStatus.removeOutputsByFilter(x => true)
incrementEpoch()
case None =>
throw new SparkException(
s"unregisterAllMapOutput called for nonexistent shuffle ID $shuffleId.")
}
}
/** Unregister shuffle data */
def unregisterShuffle(shuffleId: Int) {
shuffleStatuses.remove(shuffleId).foreach { shuffleStatus =>
shuffleStatus.invalidateSerializedMapOutputStatusCache()
}
}
/**
* Removes all shuffle outputs associated with this host. Note that this will also remove
* outputs which are served by an external shuffle server (if one exists).
*/
def removeOutputsOnHost(host: String): Unit = {
shuffleStatuses.valuesIterator.foreach { _.removeOutputsOnHost(host) }
incrementEpoch()
}
/**
* Removes all shuffle outputs associated with this executor. Note that this will also remove
* outputs which are served by an external shuffle server (if one exists), as they are still
* registered with this execId.
*/
def removeOutputsOnExecutor(execId: String): Unit = {
shuffleStatuses.valuesIterator.foreach { _.removeOutputsOnExecutor(execId) }
incrementEpoch()
}
/** Check if the given shuffle is being tracked */
def containsShuffle(shuffleId: Int): Boolean = shuffleStatuses.contains(shuffleId)
def getNumAvailableOutputs(shuffleId: Int): Int = {
shuffleStatuses.get(shuffleId).map(_.numAvailableOutputs).getOrElse(0)
}
/**
* Returns the sequence of partition ids that are missing (i.e. needs to be computed), or None
* if the MapOutputTrackerMaster doesn't know about this shuffle.
*/
def findMissingPartitions(shuffleId: Int): Option[Seq[Int]] = {
shuffleStatuses.get(shuffleId).map(_.findMissingPartitions())
}
/**
* Grouped function of Range, this is to avoid traverse of all elements of Range using
* IterableLike's grouped function.
*/
def rangeGrouped(range: Range, size: Int): Seq[Range] = {
val start = range.start
val step = range.step
val end = range.end
for (i <- start.until(end, size * step)) yield {
i.until(i + size * step, step)
}
}
/**
* To equally divide n elements into m buckets, basically each bucket should have n/m elements,
* for the remaining n%m elements, add one more element to the first n%m buckets each.
*/
def equallyDivide(numElements: Int, numBuckets: Int): Seq[Seq[Int]] = {
val elementsPerBucket = numElements / numBuckets
val remaining = numElements % numBuckets
val splitPoint = (elementsPerBucket + 1) * remaining
if (elementsPerBucket == 0) {
rangeGrouped(0.until(splitPoint), elementsPerBucket + 1)
} else {
rangeGrouped(0.until(splitPoint), elementsPerBucket + 1) ++
rangeGrouped(splitPoint.until(numElements), elementsPerBucket)
}
}
/**
* Return statistics about all of the outputs for a given shuffle.
*/
def getStatistics(dep: ShuffleDependency[_, _, _]): MapOutputStatistics = {
shuffleStatuses(dep.shuffleId).withMapStatuses { statuses =>
val totalSizes = new Array[Long](dep.partitioner.numPartitions)
val parallelAggThreshold = conf.get(
SHUFFLE_MAP_OUTPUT_PARALLEL_AGGREGATION_THRESHOLD)
val parallelism = math.min(
Runtime.getRuntime.availableProcessors(),
statuses.length.toLong * totalSizes.length / parallelAggThreshold + 1).toInt
if (parallelism <= 1) {
for (s <- statuses) {
for (i <- 0 until totalSizes.length) {
totalSizes(i) += s.getSizeForBlock(i)
}
}
} else {
val threadPool = ThreadUtils.newDaemonFixedThreadPool(parallelism, "map-output-aggregate")
try {
implicit val executionContext = ExecutionContext.fromExecutor(threadPool)
val mapStatusSubmitTasks = equallyDivide(totalSizes.length, parallelism).map {
reduceIds => Future {
for (s <- statuses; i <- reduceIds) {
totalSizes(i) += s.getSizeForBlock(i)
}
}
}
ThreadUtils.awaitResult(Future.sequence(mapStatusSubmitTasks), Duration.Inf)
} finally {
threadPool.shutdown()
}
}
new MapOutputStatistics(dep.shuffleId, totalSizes)
}
}
/**
* Return the preferred hosts on which to run the given map output partition in a given shuffle,
* i.e. the nodes that the most outputs for that partition are on.
*
* @param dep shuffle dependency object
* @param partitionId map output partition that we want to read
* @return a sequence of host names
*/
def getPreferredLocationsForShuffle(dep: ShuffleDependency[_, _, _], partitionId: Int)
: Seq[String] = {
if (shuffleLocalityEnabled && dep.rdd.partitions.length < SHUFFLE_PREF_MAP_THRESHOLD &&
dep.partitioner.numPartitions < SHUFFLE_PREF_REDUCE_THRESHOLD) {
val blockManagerIds = getLocationsWithLargestOutputs(dep.shuffleId, partitionId,
dep.partitioner.numPartitions, REDUCER_PREF_LOCS_FRACTION)
if (blockManagerIds.nonEmpty) {
blockManagerIds.get.map(_.host)
} else {
Nil
}
} else {
Nil
}
}
/**
* Return a list of locations that each have fraction of map output greater than the specified
* threshold.
*
* @param shuffleId id of the shuffle
* @param reducerId id of the reduce task
* @param numReducers total number of reducers in the shuffle
* @param fractionThreshold fraction of total map output size that a location must have
* for it to be considered large.
*/
def getLocationsWithLargestOutputs(
shuffleId: Int,
reducerId: Int,
numReducers: Int,
fractionThreshold: Double)
: Option[Array[BlockManagerId]] = {
val shuffleStatus = shuffleStatuses.get(shuffleId).orNull
if (shuffleStatus != null) {
shuffleStatus.withMapStatuses { statuses =>
if (statuses.nonEmpty) {
// HashMap to add up sizes of all blocks at the same location
val locs = new HashMap[BlockManagerId, Long]
var totalOutputSize = 0L
var mapIdx = 0
while (mapIdx < statuses.length) {
val status = statuses(mapIdx)
// status may be null here if we are called between registerShuffle, which creates an
// array with null entries for each output, and registerMapOutputs, which populates it
// with valid status entries. This is possible if one thread schedules a job which
// depends on an RDD which is currently being computed by another thread.
if (status != null) {
val blockSize = status.getSizeForBlock(reducerId)
if (blockSize > 0) {
locs(status.location) = locs.getOrElse(status.location, 0L) + blockSize
totalOutputSize += blockSize
}
}
mapIdx = mapIdx + 1
}
val topLocs = locs.filter { case (loc, size) =>
size.toDouble / totalOutputSize >= fractionThreshold
}
// Return if we have any locations which satisfy the required threshold
if (topLocs.nonEmpty) {
return Some(topLocs.keys.toArray)
}
}
}
}
None
}
def incrementEpoch() {
epochLock.synchronized {
epoch += 1
logDebug("Increasing epoch to " + epoch)
}
}
/** Called to get current epoch number. */
def getEpoch: Long = {
epochLock.synchronized {
return epoch
}
}
// Get blocks sizes by executor Id. Note that zero-sized blocks are excluded in the result.
// This method is only called in local-mode.
def getMapSizesByExecutorId(shuffleId: Int, startPartition: Int, endPartition: Int)
: Iterator[(BlockManagerId, Seq[(BlockId, Long)])] = {
logDebug(s"Fetching outputs for shuffle $shuffleId, partitions $startPartition-$endPartition")
shuffleStatuses.get(shuffleId) match {
case Some (shuffleStatus) =>
shuffleStatus.withMapStatuses { statuses =>
MapOutputTracker.convertMapStatuses(shuffleId, startPartition, endPartition, statuses)
}
case None =>
Iterator.empty
}
}
override def stop() {
mapOutputRequests.offer(PoisonPill)
threadpool.shutdown()
sendTracker(StopMapOutputTracker)
trackerEndpoint = null
shuffleStatuses.clear()
}
}
/**
* Executor-side client for fetching map output info from the driver's MapOutputTrackerMaster.
* Note that this is not used in local-mode; instead, local-mode Executors access the
* MapOutputTrackerMaster directly (which is possible because the master and worker share a comon
* superclass).
*/
private[spark] class MapOutputTrackerWorker(conf: SparkConf) extends MapOutputTracker(conf) {
val mapStatuses: Map[Int, Array[MapStatus]] =
new ConcurrentHashMap[Int, Array[MapStatus]]().asScala
/** Remembers which map output locations are currently being fetched on an executor. */
private val fetching = new HashSet[Int]
// Get blocks sizes by executor Id. Note that zero-sized blocks are excluded in the result.
override def getMapSizesByExecutorId(shuffleId: Int, startPartition: Int, endPartition: Int)
: Iterator[(BlockManagerId, Seq[(BlockId, Long)])] = {
logDebug(s"Fetching outputs for shuffle $shuffleId, partitions $startPartition-$endPartition")
val statuses = getStatuses(shuffleId)
try {
MapOutputTracker.convertMapStatuses(shuffleId, startPartition, endPartition, statuses)
} catch {
case e: MetadataFetchFailedException =>
// We experienced a fetch failure so our mapStatuses cache is outdated; clear it:
mapStatuses.clear()
throw e
}
}
/**
* Get or fetch the array of MapStatuses for a given shuffle ID. NOTE: clients MUST synchronize
* on this array when reading it, because on the driver, we may be changing it in place.
*
* (It would be nice to remove this restriction in the future.)
*/
private def getStatuses(shuffleId: Int): Array[MapStatus] = {
val statuses = mapStatuses.get(shuffleId).orNull
if (statuses == null) {
logInfo("Don't have map outputs for shuffle " + shuffleId + ", fetching them")
val startTime = System.currentTimeMillis
var fetchedStatuses: Array[MapStatus] = null
fetching.synchronized {
// Someone else is fetching it; wait for them to be done
while (fetching.contains(shuffleId)) {
try {
fetching.wait()
} catch {
case e: InterruptedException =>
}
}
// Either while we waited the fetch happened successfully, or
// someone fetched it in between the get and the fetching.synchronized.
fetchedStatuses = mapStatuses.get(shuffleId).orNull
if (fetchedStatuses == null) {
// We have to do the fetch, get others to wait for us.
fetching += shuffleId
}
}
if (fetchedStatuses == null) {
// We won the race to fetch the statuses; do so
logInfo("Doing the fetch; tracker endpoint = " + trackerEndpoint)
// This try-finally prevents hangs due to timeouts:
try {
val fetchedBytes = askTracker[Array[Byte]](GetMapOutputStatuses(shuffleId))
fetchedStatuses = MapOutputTracker.deserializeMapStatuses(fetchedBytes)
logInfo("Got the output locations")
mapStatuses.put(shuffleId, fetchedStatuses)
} finally {
fetching.synchronized {
fetching -= shuffleId
fetching.notifyAll()
}
}
}
logDebug(s"Fetching map output statuses for shuffle $shuffleId took " +
s"${System.currentTimeMillis - startTime} ms")
if (fetchedStatuses != null) {
fetchedStatuses
} else {
logError("Missing all output locations for shuffle " + shuffleId)
throw new MetadataFetchFailedException(
shuffleId, -1, "Missing all output locations for shuffle " + shuffleId)
}
} else {
statuses
}
}
/** Unregister shuffle data. */
def unregisterShuffle(shuffleId: Int): Unit = {
mapStatuses.remove(shuffleId)
}
/**
* Called from executors to update the epoch number, potentially clearing old outputs
* because of a fetch failure. Each executor task calls this with the latest epoch
* number on the driver at the time it was created.
*/
def updateEpoch(newEpoch: Long): Unit = {
epochLock.synchronized {
if (newEpoch > epoch) {
logInfo("Updating epoch to " + newEpoch + " and clearing cache")
epoch = newEpoch
mapStatuses.clear()
}
}
}
}
private[spark] object MapOutputTracker extends Logging {
val ENDPOINT_NAME = "MapOutputTracker"
private val DIRECT = 0
private val BROADCAST = 1
// Serialize an array of map output locations into an efficient byte format so that we can send
// it to reduce tasks. We do this by compressing the serialized bytes using GZIP. They will
// generally be pretty compressible because many map outputs will be on the same hostname.
def serializeMapStatuses(statuses: Array[MapStatus], broadcastManager: BroadcastManager,
isLocal: Boolean, minBroadcastSize: Int): (Array[Byte], Broadcast[Array[Byte]]) = {
val out = new ByteArrayOutputStream
out.write(DIRECT)
val objOut = new ObjectOutputStream(new GZIPOutputStream(out))
Utils.tryWithSafeFinally {
// Since statuses can be modified in parallel, sync on it
statuses.synchronized {
objOut.writeObject(statuses)
}
} {
objOut.close()
}
val arr = out.toByteArray
if (arr.length >= minBroadcastSize) {
// Use broadcast instead.
// Important arr(0) is the tag == DIRECT, ignore that while deserializing !
val bcast = broadcastManager.newBroadcast(arr, isLocal)
// toByteArray creates copy, so we can reuse out
out.reset()
out.write(BROADCAST)
val oos = new ObjectOutputStream(new GZIPOutputStream(out))
oos.writeObject(bcast)
oos.close()
val outArr = out.toByteArray
logInfo("Broadcast mapstatuses size = " + outArr.length + ", actual size = " + arr.length)
(outArr, bcast)
} else {
(arr, null)
}
}
// Opposite of serializeMapStatuses.
def deserializeMapStatuses(bytes: Array[Byte]): Array[MapStatus] = {
assert (bytes.length > 0)
def deserializeObject(arr: Array[Byte], off: Int, len: Int): AnyRef = {
val objIn = new ObjectInputStream(new GZIPInputStream(
new ByteArrayInputStream(arr, off, len)))
Utils.tryWithSafeFinally {
objIn.readObject()
} {
objIn.close()
}
}
bytes(0) match {
case DIRECT =>
deserializeObject(bytes, 1, bytes.length - 1).asInstanceOf[Array[MapStatus]]
case BROADCAST =>
// deserialize the Broadcast, pull .value array out of it, and then deserialize that
val bcast = deserializeObject(bytes, 1, bytes.length - 1).
asInstanceOf[Broadcast[Array[Byte]]]
logInfo("Broadcast mapstatuses size = " + bytes.length +
", actual size = " + bcast.value.length)
// Important - ignore the DIRECT tag ! Start from offset 1
deserializeObject(bcast.value, 1, bcast.value.length - 1).asInstanceOf[Array[MapStatus]]
case _ => throw new IllegalArgumentException("Unexpected byte tag = " + bytes(0))
}
}
/**
* Given an array of map statuses and a range of map output partitions, returns a sequence that,
* for each block manager ID, lists the shuffle block IDs and corresponding shuffle block sizes
* stored at that block manager.
* Note that empty blocks are filtered in the result.
*
* If any of the statuses is null (indicating a missing location due to a failed mapper),
* throws a FetchFailedException.
*
* @param shuffleId Identifier for the shuffle
* @param startPartition Start of map output partition ID range (included in range)
* @param endPartition End of map output partition ID range (excluded from range)
* @param statuses List of map statuses, indexed by map ID.
* @return A sequence of 2-item tuples, where the first item in the tuple is a BlockManagerId,
* and the second item is a sequence of (shuffle block ID, shuffle block size) tuples
* describing the shuffle blocks that are stored at that block manager.
*/
def convertMapStatuses(
shuffleId: Int,
startPartition: Int,
endPartition: Int,
statuses: Array[MapStatus]): Iterator[(BlockManagerId, Seq[(BlockId, Long)])] = {
assert (statuses != null)
val splitsByAddress = new HashMap[BlockManagerId, ListBuffer[(BlockId, Long)]]
for ((status, mapId) <- statuses.iterator.zipWithIndex) {
if (status == null) {
val errorMessage = s"Missing an output location for shuffle $shuffleId"
logError(errorMessage)
throw new MetadataFetchFailedException(shuffleId, startPartition, errorMessage)
} else {
for (part <- startPartition until endPartition) {
val size = status.getSizeForBlock(part)
if (size != 0) {
splitsByAddress.getOrElseUpdate(status.location, ListBuffer()) +=
((ShuffleBlockId(shuffleId, mapId, part), size))
}
}
}
}
splitsByAddress.iterator
}
}
