Shuffle Read
对于每个stage来说，它的上边界，要么从外部存储读取数据，要么读取上一个stage的输出。而下边界要么是写入到本地文件系统(需要有shuffle)，一共child stage进行读取，要么就是最后一个stage，需要输出结果。这里的stage在运行时就可以以流水线的方式进行运行一组Task，除了最后一个stage对应的ResultTask，其余的stage全部对应的shuffle Map Task。
除了需要从外部存储读取数据和RDD已经做过cache或者checkPoint的Task。一般的Task都是从Shuffle RDD的ShuffleRead开始的
一、整体流程
ShuffleReade从 org.apache.spark.rdd#compute 的方法开始。
package org.apache.spark.rdd
override def compute(split: Partition, context: TaskContext): Iterator[(K, C)] = {
val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, C]]
SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, split.index + 1, context)
.read()
.asInstanceOf[Iterator[(K, C)]]
}
通过调用 org.apache.spark.shuffle.SortShuffleManager#getReader 方法，获取到 org.apache.spark.shuffle.ShuffleReader,。
package org.apache.spark.shuffle.sort
private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager with Logging {
... //省略部分代码
/**
* Get a reader for a range of reduce partitions (startPartition to endPartition-1, inclusive).
* Called on executors by reduce tasks.
*/
override def getReader[K, C](
handle: ShuffleHandle,
startPartition: Int,
endPartition: Int,
context: TaskContext): ShuffleReader[K, C] = {
new BlockStoreShuffleReader(
handle.asInstanceOf[BaseShuffleHandle[K, _, C]], startPartition, endPartition, context)
}
... //省略部分代码
}
然后调用其read方法进行读取
/** Read the combined key-values for this reduce task */
override def read(): Iterator[Product2[K, C]] = {
val wrappedStreams = new ShuffleBlockFetcherIterator(
context,
blockManager.shuffleClient,
blockManager,
mapOutputTracker.getMapSizesByExecutorId(handle.shuffleId, startPartition, endPartition), // 获取数据的meta信息
serializerManager.wrapStream,
// Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024,
SparkEnv.get.conf.getInt("spark.reducer.maxReqsInFlight", Int.MaxValue),
SparkEnv.get.conf.get(config.REDUCER_MAX_REQ_SIZE_SHUFFLE_TO_MEM),
SparkEnv.get.conf.getBoolean("spark.shuffle.detectCorrupt", true))
val serializerInstance = dep.serializer.newInstance()
// Create a key/value iterator for each stream
val recordIter = wrappedStreams.flatMap { case (blockId, wrappedStream) =>
// Note: the asKeyValueIterator below wraps a key/value iterator inside of a
// NextIterator. The NextIterator makes sure that close() is called on the
// underlying InputStream when all records have been read.
serializerInstance.deserializeStream(wrappedStream).asKeyValueIterator
}
// Update the context task metrics for each record read.
val readMetrics = context.taskMetrics.createTempShuffleReadMetrics()
val metricIter = CompletionIterator[(Any, Any), Iterator[(Any, Any)]](
recordIter.map { record =>
readMetrics.incRecordsRead(1)
record
},
context.taskMetrics().mergeShuffleReadMetrics())
// An interruptible iterator must be used here in order to support task cancellation
val interruptibleIter = new InterruptibleIterator[(Any, Any)](context, metricIter)
// 判断是否需要聚合
val aggregatedIter: Iterator[Product2[K, C]] = if (dep.aggregator.isDefined) { // 需要聚合
if (dep.mapSideCombine) { // 需要map端聚合
// We are reading values that are already combined
val combinedKeyValuesIterator = interruptibleIter.asInstanceOf[Iterator[(K, C)]]
dep.aggregator.get.combineCombinersByKey(combinedKeyValuesIterator, context)
} else { // 否则只需要reduce端聚合
// We don't know the value type, but also don't care -- the dependency *should*
// have made sure its compatible w/ this aggregator, which will convert the value
// type to the combined type C
val keyValuesIterator = interruptibleIter.asInstanceOf[Iterator[(K, Nothing)]]
dep.aggregator.get.combineValuesByKey(keyValuesIterator, context)
}
} else { // 无需聚合操作
require(!dep.mapSideCombine, "Map-side combine without Aggregator specified!")
interruptibleIter.asInstanceOf[Iterator[Product2[K, C]]]
}
// Sort the output if there is a sort ordering defined.
dep.keyOrdering match { // 判断是否需要排序
case Some(keyOrd: Ordering[K]) => // 对于需要排序的情况，使用ExtenrnalSorter进行排序，这里需要注意，如果spark.shuffle.spill是false的话，数据是不会写入硬盘的。
// Create an ExternalSorter to sort the data.
val sorter =
new ExternalSorter[K, C, C](context, ordering = Some(keyOrd), serializer = dep.serializer)
sorter.insertAll(aggregatedIter)
context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
context.taskMetrics().incPeakExecutionMemory(sorter.peakMemoryUsedBytes)
CompletionIterator[Product2[K, C], Iterator[Product2[K, C]]](sorter.iterator, sorter.stop())
case None => // 无需排序
aggregatedIter
}
}
read方法通过 org.apache.spark.storage.ShuffleBlockFetcherIterator 来进行读取数据.首先会通过 org.apache.spark#getMapSizesByExecutorId 来获取数据的meta信息，最终调用的是 org.apache.spark#getStatuses 方法，具体实现如下：
package org.apache.spark
/**
* 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)
: Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
logDebug(s"Fetching outputs for shuffle $shuffleId, partitions $startPartition-$endPartition")
val statuses = getStatuses(shuffleId)
// Synchronize on the returned array because, on the driver, it gets mutated in place
statuses.synchronized {
return MapOutputTracker.convertMapStatuses(shuffleId, startPartition, endPartition, statuses)
}
}
/**
* 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)) // 需要向MapOutputTrackerMasterActor发送读请求
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) {
return fetchedStatuses
} else {
logError("Missing all output locations for shuffle " + shuffleId)
throw new MetadataFetchFailedException(
shuffleId, -1, "Missing all output locations for shuffle " + shuffleId)
}
} else {
return statuses
}
}
这个过程有可能需要向 org.apache.spark.MapOutputTrackerMasterActor 发送读请求，如上代码中标注。在获取到这些meta信息之后，会将其存入到 Seq[(BlockManagerId, Seq[(BlockId, Long)])] 中，然后构造一个 ShuffleBlockFetcherIterator 的一个实例，这个实例中有一个 initialize() 方法，会首先会调用这个方法，这个方法中会调用 splitLocalRemoteBlocks() 方法，进行获取本地或远程端的数据块。
package org.apache.spark.storage private[this] def initialize(): Unit = { // Add a task completion callback (called in both success case and failure case) to cleanup. context.addTaskCompletionListener(_ => cleanup()) // Split local and remote blocks. val remoteRequests = splitLocalRemoteBlocks() // Add the remote requests into our queue in a random order fetchRequests ++= Utils.randomize(remoteRequests) // 打乱remoteRequest的顺序，并添加到队列中 assert ((0 == reqsInFlight) == (0 == bytesInFlight), // 判断当前正在运行的请求的数量以及当前正在从请求中消失的字节数 "expected reqsInFlight = 0 but found reqsInFlight = " + reqsInFlight + ", expected bytesInFlight = 0 but found bytesInFlight = " + bytesInFlight) // Send out initial requests for blocks, up to our maxBytesInFlight fetchUpToMaxBytes() val numFetches = remoteRequests.size - fetchRequests.size logInfo("Started " + numFetches + " remote fetches in" + Utils.getUsedTimeMs(startTime)) // Get Local Blocks fetchLocalBlocks() // 本地数据块的读取 logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime)) }
该方法中通过遍历 Seq[(BlockManagerId, Seq[(BlockId, Long)])] 元数据信息，分割本地和远程数据块。分割出需要远程获取的请求，即 remotRequest
package org.apache.spark.storage
private[this] def splitLocalRemoteBlocks(): ArrayBuffer[FetchRequest] = {
// Make remote requests at most maxBytesInFlight / 5 in length; the reason to keep them
// smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5
// nodes, rather than blocking on reading output from one node.
val targetRequestSize = math.max(maxBytesInFlight / 5, 1L)
logDebug("maxBytesInFlight: " + maxBytesInFlight + ", targetRequestSize: " + targetRequestSize)
// Split local and remote blocks. Remote blocks are further split into FetchRequests of size
// at most maxBytesInFlight in order to limit the amount of data in flight.
val remoteRequests = new ArrayBuffer[FetchRequest]
// Tracks total number of blocks (including zero sized blocks)
var totalBlocks = 0
for ((address, blockInfos) <- blocksByAddress) {
totalBlocks += blockInfos.size
if (address.executorId == blockManager.blockManagerId.executorId) {
// Filter out zero-sized blocks
localBlocks ++= blockInfos.filter(_._2 != 0).map(_._1)
numBlocksToFetch += localBlocks.size
} else {
val iterator = blockInfos.iterator
var curRequestSize = 0L
var curBlocks = new ArrayBuffer[(BlockId, Long)]
while (iterator.hasNext) {
val (blockId, size) = iterator.next()
// Skip empty blocks
if (size > 0) {
curBlocks += ((blockId, size))
remoteBlocks += blockId
numBlocksToFetch += 1
curRequestSize += size
} else if (size < 0) {
throw new BlockException(blockId, "Negative block size " + size)
}
if (curRequestSize >= targetRequestSize) {
// Add this FetchRequest
remoteRequests += new FetchRequest(address, curBlocks)
curBlocks = new ArrayBuffer[(BlockId, Long)]
logDebug(s"Creating fetch request of $curRequestSize at $address")
curRequestSize = 0
}
}
// Add in the final request
if (curBlocks.nonEmpty) {
remoteRequests += new FetchRequest(address, curBlocks)
}
}
}
logInfo(s"Getting $numBlocksToFetch non-empty blocks out of $totalBlocks blocks")
remoteRequests
}
返回的，remoteRequest是一个数组，进阶着会将其打乱，然后添加到队列中。然后调用 fetchUpToMaxBytes() 进行获取远程端的block块，最终调用的是sendRequest方法通过 package org.apache.spark.network.shuffle#fetchBlocks 进行获取：
private def fetchUpToMaxBytes(): Unit = {
// Send fetch requests up to maxBytesInFlight
while (fetchRequests.nonEmpty &&
(bytesInFlight == 0 ||
(reqsInFlight + 1 <= maxReqsInFlight &&
bytesInFlight + fetchRequests.front.size <= maxBytesInFlight))) {
sendRequest(fetchRequests.dequeue())
}
}
private[this] def sendRequest(req: FetchRequest) {
logDebug("Sending request for %d blocks (%s) from %s".format(
req.blocks.size, Utils.bytesToString(req.size), req.address.hostPort))
bytesInFlight += req.size
reqsInFlight += 1
// so we can look up the size of each blockID
val sizeMap = req.blocks.map { case (blockId, size) => (blockId.toString, size) }.toMap
val remainingBlocks = new HashSet[String]() ++= sizeMap.keys
val blockIds = req.blocks.map(_._1.toString)
val address = req.address
val blockFetchingListener = new BlockFetchingListener {
override def onBlockFetchSuccess(blockId: String, buf: ManagedBuffer): Unit = {
// Only add the buffer to results queue if the iterator is not zombie,
// i.e. cleanup() has not been called yet.
ShuffleBlockFetcherIterator.this.synchronized {
if (!isZombie) {
// Increment the ref count because we need to pass this to a different thread.
// This needs to be released after use.
buf.retain()
remainingBlocks -= blockId
results.put(new SuccessFetchResult(BlockId(blockId), address, sizeMap(blockId), buf,
remainingBlocks.isEmpty))
logDebug("remainingBlocks: " + remainingBlocks)
}
}
logTrace("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime))
}
override def onBlockFetchFailure(blockId: String, e: Throwable): Unit = {
logError(s"Failed to get block(s) from ${req.address.host}:${req.address.port}", e)
results.put(new FailureFetchResult(BlockId(blockId), address, e))
}
}
// Fetch remote shuffle blocks to disk when the request is too large. Since the shuffle data is
// already encrypted and compressed over the wire(w.r.t. the related configs), we can just fetch
// the data and write it to file directly.
if (req.size > maxReqSizeShuffleToMem) {
val shuffleFiles = blockIds.map { _ =>
blockManager.diskBlockManager.createTempLocalBlock()._2
}.toArray
shuffleFilesSet ++= shuffleFiles
shuffleClient.fetchBlocks(address.host, address.port, address.executorId, blockIds.toArray,
blockFetchingListener, shuffleFiles)
} else {
shuffleClient.fetchBlocks(address.host, address.port, address.executorId, blockIds.toArray,
blockFetchingListener, null)
}
}
package org.apache.spark.network.shuffle;
import java.io.Closeable;
import java.io.File;
/** Provides an interface for reading shuffle files, either from an Executor or external service. */
public abstract class ShuffleClient implements Closeable {
/**
* Initializes the ShuffleClient, specifying this Executor's appId.
* Must be called before any other method on the ShuffleClient.
*/
public void init(String appId) { }
/**
* Fetch a sequence of blocks from a remote node asynchronously,
*
* Note that this API takes a sequence so the implementation can batch requests, and does not
* return a future so the underlying implementation can invoke onBlockFetchSuccess as soon as
* the data of a block is fetched, rather than waiting for all blocks to be fetched.
*/
public abstract void fetchBlocks(
String host,
int port,
String execId,
String[] blockIds,
BlockFetchingListener listener,
File[] shuffleFiles);
}
spark1.6之前，通过远程端获取的方式分别有nio与netty的方式，用户可以指定，在1.6之后，shuffleClient的类型只有netty的方式。
/**
* A BlockTransferService that uses Netty to fetch a set of blocks at time.
*/
private[spark] class NettyBlockTransferService(
conf: SparkConf,
securityManager: SecurityManager,
bindAddress: String,
override val hostName: String,
_port: Int,
numCores: Int)
extends BlockTransferService {
。。。
}
然后，通过调用 fetchLocalBlocks() 方法获取本地数据块的读取。
二、数据读取策略的划分
org.apache.spark.storage.ShuffleBlockFetcherIterator 它会通过 splitLocalRemoteBlocks 划分数据的读取策略，如果数据在本地的话，则直接可以通过BlockManager进行获取，如果需要从其他节点获取的话，则要通过网络。由于shuffle的数据量可能会很大，所以这里网络读取分为以下几种策略：
- 每次最多启动5个线程到5个节点上读取数据。
- 每次请求的数据大小不超过48M(SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024) 的五分之一,
// Make remote requests at most maxBytesInFlight / 5 in length; the reason to keep them // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5 // nodes, rather than blocking on reading output from one node. val targetRequestSize = math.max(maxBytesInFlight / 5, 1L)
主要原因有以下几点：
-
- 避免占用目标机器过多的带宽，如果机器使用的是万兆网卡的话，可以通过设置 spark.reducer.maxSizeInFight 来充分利用带宽
- 请求数据可以并行化，这样可以大大减少请求数据的时间。请求数据总时间就是请求中耗时最长的。这样的话，可以缓解一个节点出现网络拥塞是的影响。
主要实现：
package org.apache.spark.shuffle
/** Read the combined key-values for this reduce task */
override def read(): Iterator[Product2[K, C]] = {
val wrappedStreams = new ShuffleBlockFetcherIterator(
context,
blockManager.shuffleClient,
blockManager,
mapOutputTracker.getMapSizesByExecutorId(handle.shuffleId, startPartition, endPartition),
serializerManager.wrapStream,
// Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024,
SparkEnv.get.conf.getInt("spark.reducer.maxReqsInFlight", Int.MaxValue),
SparkEnv.get.conf.get(config.REDUCER_MAX_REQ_SIZE_SHUFFLE_TO_MEM),
SparkEnv.get.conf.getBoolean("spark.shuffle.detectCorrupt", true))
... // 省略部分代码
}
package org.apache.spark.storage private[this] def splitLocalRemoteBlocks(): ArrayBuffer[FetchRequest] = { // Make remote requests at most maxBytesInFlight / 5 in length; the reason to keep them // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5 // nodes, rather than blocking on reading output from one node. val targetRequestSize = math.max(maxBytesInFlight / 5, 1L) logDebug("maxBytesInFlight: " + maxBytesInFlight + ", targetRequestSize: " + targetRequestSize) // Split local and remote blocks. Remote blocks are further split into FetchRequests of size // at most maxBytesInFlight in order to limit the amount of data in flight. val remoteRequests = new ArrayBuffer[FetchRequest] // Tracks total number of blocks (including zero sized blocks) var totalBlocks = 0 for ((address, blockInfos) <- blocksByAddress) { totalBlocks += blockInfos.size if (address.executorId == blockManager.blockManagerId.executorId) { // Filter out zero-sized blocks localBlocks ++= blockInfos.filter(_._2 != 0).map(_._1) numBlocksToFetch += localBlocks.size } else { val iterator = blockInfos.iterator var curRequestSize = 0L var curBlocks = new ArrayBuffer[(BlockId, Long)] while (iterator.hasNext) { val (blockId, size) = iterator.next() // Skip empty blocks if (size > 0) { curBlocks += ((blockId, size)) remoteBlocks += blockId numBlocksToFetch += 1 curRequestSize += size } else if (size < 0) { throw new BlockException(blockId, "Negative block size " + size) } if (curRequestSize >= targetRequestSize) { // Add this FetchRequest remoteRequests += new FetchRequest(address, curBlocks) curBlocks = new ArrayBuffer[(BlockId, Long)] logDebug(s"Creating fetch request of $curRequestSize at $address") curRequestSize = 0 } } // Add in the final request if (curBlocks.nonEmpty) { remoteRequests += new FetchRequest(address, curBlocks) } } } logInfo(s"Getting $numBlocksToFetch non-empty blocks out of $totalBlocks blocks") remoteRequests }
三、本地读取
fetchLocalBlocks() 负责本地block的读取，在 splitLocalRemoteBlocks 中已经将本地的block列表存入了 localBlocks:localBlocks ++= blockInfos.filter(_._2 != 0) ,
/** Local blocks to fetch, excluding zero-sized blocks. */ private[this] val localBlocks = new ArrayBuffer[BlockId]()
具体实现过程如下：
/**
* Fetch the local blocks while we are fetching remote blocks. This is ok because
* `ManagedBuffer`'s memory is allocated lazily when we create the input stream, so all we
* track in-memory are the ManagedBuffer references themselves.
*/
private[this] def fetchLocalBlocks() {
val iter = localBlocks.iterator
while (iter.hasNext) {
val blockId = iter.next()
try {
val buf = blockManager.getBlockData(blockId)
shuffleMetrics.incLocalBlocksFetched(1)
shuffleMetrics.incLocalBytesRead(buf.size)
buf.retain()
results.put(new SuccessFetchResult(blockId, blockManager.blockManagerId, 0, buf, false))
} catch {
case e: Exception =>
// If we see an exception, stop immediately.
logError(s"Error occurred while fetching local blocks", e)
results.put(new FailureFetchResult(blockId, blockManager.blockManagerId, e))
return
}
}
}
而BlockManager.getBlockData(blockId)的实现是：
/**
* Interface to get local block data. Throws an exception if the block cannot be found or
* cannot be read successfully.
*/
override def getBlockData(blockId: BlockId): ManagedBuffer = {
if (blockId.isShuffle) {
shuffleManager.shuffleBlockResolver.getBlockData(blockId.asInstanceOf[ShuffleBlockId])
} else {
getLocalBytes(blockId) match {
case Some(blockData) =>
new BlockManagerManagedBuffer(blockInfoManager, blockId, blockData, true)
case None =>
// If this block manager receives a request for a block that it doesn't have then it's
// likely that the master has outdated block statuses for this block. Therefore, we send
// an RPC so that this block is marked as being unavailable from this block manager.
reportBlockStatus(blockId, BlockStatus.empty)
throw new BlockNotFoundException(blockId.toString)
}
}
}
四、远程读取
org.apache.spark.storage.ShuffleBlockFetcherIterator#sendRequest 回向远程节点发送读取block的请求：
private[this] def sendRequest(req: FetchRequest) {
logDebug("Sending request for %d blocks (%s) from %s".format(
req.blocks.size, Utils.bytesToString(req.size), req.address.hostPort))
bytesInFlight += req.size
reqsInFlight += 1
// so we can look up the size of each blockID
val sizeMap = req.blocks.map { case (blockId, size) => (blockId.toString, size) }.toMap
val remainingBlocks = new HashSet[String]() ++= sizeMap.keys
val blockIds = req.blocks.map(_._1.toString)
val address = req.address
val blockFetchingListener = new BlockFetchingListener {
override def onBlockFetchSuccess(blockId: String, buf: ManagedBuffer): Unit = {
// Only add the buffer to results queue if the iterator is not zombie,
// i.e. cleanup() has not been called yet.
ShuffleBlockFetcherIterator.this.synchronized {
if (!isZombie) {
// Increment the ref count because we need to pass this to a different thread.
// This needs to be released after use.
buf.retain()
remainingBlocks -= blockId
results.put(new SuccessFetchResult(BlockId(blockId), address, sizeMap(blockId), buf,
remainingBlocks.isEmpty))
logDebug("remainingBlocks: " + remainingBlocks)
}
}
logTrace("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime))
}
override def onBlockFetchFailure(blockId: String, e: Throwable): Unit = {
logError(s"Failed to get block(s) from ${req.address.host}:${req.address.port}", e)
results.put(new FailureFetchResult(BlockId(blockId), address, e))
}
}
// Fetch remote shuffle blocks to disk when the request is too large. Since the shuffle data is
// already encrypted and compressed over the wire(w.r.t. the related configs), we can just fetch
// the data and write it to file directly.
if (req.size > maxReqSizeShuffleToMem) {
val shuffleFiles = blockIds.map { _ =>
blockManager.diskBlockManager.createTempLocalBlock()._2
}.toArray
shuffleFilesSet ++= shuffleFiles
shuffleClient.fetchBlocks(address.host, address.port, address.executorId, blockIds.toArray,
blockFetchingListener, shuffleFiles)
} else {
shuffleClient.fetchBlocks(address.host, address.port, address.executorId, blockIds.toArray,
blockFetchingListener, null)
}
}
shuffleClient 在默认情况下就是 blockTransferService 。
package org.apache.spark.storage private[spark] val externalShuffleServiceEnabled = conf.getBoolean("spark.shuffle.service.enabled", false) // Client to read other executors' shuffle files. This is either an external service, or just the // standard BlockTransferService to directly connect to other Executors. private[spark] val shuffleClient = if (externalShuffleServiceEnabled) { val transConf = SparkTransportConf.fromSparkConf(conf, "shuffle", numUsableCores) new ExternalShuffleClient(transConf, securityManager, securityManager.isAuthenticationEnabled()) } else { blockTransferService }
而 blockTransferService 是在 sparkEnv 中创建的，而且就是制定了netty模式，并没有nio模式
package org.apache.spark
val blockTransferService =
new NettyBlockTransferService(conf, securityManager, bindAddress, advertiseAddress,
blockManagerPort, numUsableCores)
至此，spark shuffle Read结束