spark join shuffle 数据读取的过程
spark join shuffle 数据读取的过程
在spark中，当数据要shuffle时，这个拉取过程RDD是怎么和ShuffleMapTask 关联起来的。
在CoGroupedRDD通过调用如下函数去读取指定分区的数据
SparkEnv.get.shuffleManager
.getReader(shuffleDependency.shuffleHandle, split.index, split.index + 1, context)
.read()
通过上面的方法，就可以知道调用那个依赖的RDD，读取那个分片数据。
然后创建BlockStoreShuffleReader读取对象。在该类中执行下面的方法
// 下面就是对这个shuffler中的分片数据进行读取并进行相关的aggregate操作了
val blockFetcherItr = new ShuffleBlockFetcherIterator(
context,
blockManager.shuffleClient,
blockManager,
mapOutputTracker.getMapSizesByExecutorId(handle.shuffleId, startPartition, endPartition),
// Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024)
可以看到首先要通过mapOutputTracker去拉取该分区的地址信息
def getMapSizesByExecutorId(shuffleId: Int, startPartition: Int, endPartition: Int)
: Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
logDebug(s"Fetching outputs for shuffle $shuffleId, partitions $startPartition-$endPartition")
// 拉取这些状态数据回来了
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)
}
}
然后在 getStatuses函数中，发起远程调用，读取这个shuffle的结果地址数据
try {
// 拉取这个shuffle的状态数据
val fetchedBytes = askTracker[Array[Byte]](GetMapOutputStatuses(shuffleId))
// 这个status是那些数据分片的地址来的
fetchedStatuses = MapOutputTracker.deserializeMapStatuses(fetchedBytes)
logInfo("Got the output locations")
mapStatuses.put(shuffleId, fetchedStatuses)
}
在MapOutputTrackerMaster中的MapOutputTrackerMasterEndpoint 接收线程中，接收到相关的消息
override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
case GetMapOutputStatuses(shuffleId: Int) =>
// 问这个shuffler的地址信息
val hostPort = context.senderAddress.hostPort
logInfo("Asked to send map output locations for shuffle " + shuffleId + " to " + hostPort)
// 去这个tracker里面去拉取了
val mapOutputStatuses = tracker.getSerializedMapOutputStatuses(shuffleId)
val serializedSize = mapOutputStatuses.length
if (serializedSize > maxAkkaFrameSize) {
val msg = s"Map output statuses were $serializedSize bytes which " +
s"exceeds spark.akka.frameSize ($maxAkkaFrameSize bytes)."
/* For SPARK-1244 we'll opt for just logging an error and then sending it to the sender.
* A bigger refactoring (SPARK-1239) will ultimately remove this entire code path. */
val exception = new SparkException(msg)
logError(msg, exception)
context.sendFailure(exception)
} else {
context.reply(mapOutputStatuses)
}
在tracker 保存着shuffle的执行结果。这些数据是通过DAGScheduler 在调用ShuffleMapTask 的时候，运行的结果存放的
case smt: ShuffleMapTask =>
val shuffleStage = stage.asInstanceOf[ShuffleMapStage]
updateAccumulators(event)
val status = event.result.asInstanceOf[MapStatus]
val execId = status.location.executorId
logDebug("ShuffleMapTask finished on " + execId)
if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) {
// 这是一个失败的任务
logInfo(s"Ignoring possibly bogus $smt completion from executor $execId")
} else {
// 记录这个分区的运行结果
shuffleStage.addOutputLoc(smt.partitionId, status)
}
if (runningStages.contains(shuffleStage) && shuffleStage.pendingPartitions.isEmpty) {
markStageAsFinished(shuffleStage)
logInfo("looking for newly runnable stages")
logInfo("running: " + runningStages)
logInfo("waiting: " + waitingStages)
logInfo("failed: " + failedStages)
// We supply true to increment the epoch number here in case this is a
// recomputation of the map outputs. In that case, some nodes may have cached
// locations with holes (from when we detected the error) and will need the
// epoch incremented to refetch them.
// TODO: Only increment the epoch number if this is not the first time
// we registered these map outputs.
// 把当前shuffler的执行结果存放在这里了
mapOutputTracker.registerMapOutputs(
shuffleStage.shuffleDep.shuffleId,
shuffleStage.outputLocInMapOutputTrackerFormat(),
changeEpoch = true)
详情可查看 《SPARK TASK 任务状态管理》 ，在DAGScheduler 中当ShuffleMapTask 完成任务时，把对应的shuffleid
的计算结果路径写到mapOutputTracker中，然后在其它地方就可以请求到这个数据了。
private def convertMapStatuses(
shuffleId: Int,
startPartition: Int,
endPartition: Int,
statuses: Array[MapStatus]): Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
assert (statuses != null)
val splitsByAddress = new HashMap[BlockManagerId, ArrayBuffer[(BlockId, Long)]]
// statuses是包含着所有的地址信息
for ((status, mapId) <- statuses.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) {
// 就是拿这个地址中这个分片里面的数据
splitsByAddress.getOrElseUpdate(status.location, ArrayBuffer()) +=
((ShuffleBlockId(shuffleId, mapId, part), status.getSizeForBlock(part)))
}
}
}
splitsByAddress.toSeq
}
然后就可以拉取指定分片里面的数据了，通过ShuffleBlockFetcherIterator 类的功能，
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
// so we can look up the size of each blockID
val sizeMap = req.blocks.map { case (blockId, size) => (blockId.toString, size) }.toMap
val blockIds = req.blocks.map(_._1.toString)
// 去这些地址拉取数据了,同时注意block对象是 ShuffleBlockId 里面包含着当前请求的是那个分片数据
// 在拉取的时候，还要对block块数据进行分片
val address = req.address
shuffleClient.fetchBlocks(address.host, address.port, address.executorId, blockIds.toArray,
new BlockFetchingListener {
override def onBlockFetchSuccess(blockId: String, buf: ManagedBuffer): Unit = {
}
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))
}
}
)
}
然后就可以通过shuffleClient （NettyBlockTransferService ）进行远程的拉取了。
override def fetchBlocks(
host: String,
port: Int,
execId: String,
blockIds: Array[String],
listener: BlockFetchingListener): Unit = {
logTrace(s"Fetch blocks from $host:$port (executor id $execId)")
// 通过netty的方式去拉取block文件
try {
val blockFetchStarter = new RetryingBlockFetcher.BlockFetchStarter {
override def createAndStart(blockIds: Array[String], listener: BlockFetchingListener) {
// 这里就是创建netty客户端进行拉取数据了
val client = clientFactory.createClient(host, port)
new OneForOneBlockFetcher(client, appId, execId, blockIds.toArray, listener).start()
}
}
// 如果有重试器，则就创建一个包装对象进行重试
val maxRetries = transportConf.maxIORetries()
if (maxRetries > 0) {
// Note this Fetcher will correctly handle maxRetries == 0; we avoid it just in case there's
// a bug in this code. We should remove the if statement once we're sure of the stability.
new RetryingBlockFetcher(transportConf, blockFetchStarter, blockIds, listener).start()
} else {
blockFetchStarter.createAndStart(blockIds, listener)
}
} catch {
case e: Exception =>
logError("Exception while beginning fetchBlocks", e)
blockIds.foreach(listener.onBlockFetchFailure(_, e))
}
}
当拉取回来后，就可以对这个iterator数据进行后缀的处理了。然后回到BlockStoreShuffleReader类中
override def read(): Iterator[Product2[K, C]] = {
// 读取这个分片的数据了,生成iteracor对象
// 下面就是对这个shuffler中的分片数据进行读取并进行相关的aggregate操作了
val blockFetcherItr = new ShuffleBlockFetcherIterator(
context,
blockManager.shuffleClient,
blockManager,
mapOutputTracker.getMapSizesByExecutorId(handle.shuffleId, startPartition, endPartition),
// Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024)
// Wrap the streams for compression based on configuration
val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
blockManager.wrapForCompression(blockId, inputStream)
}
val ser = Serializer.getSerializer(dep.serializer)
val serializerInstance = ser.newInstance()
// Create a key/value iterator for each stream
val recordIter = wrappedStreams.flatMap { 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.
// metrics记录数据量
val readMetrics = context.taskMetrics.createShuffleReadMetricsForDependency()
val metricIter = CompletionIterator[(Any, Any), Iterator[(Any, Any)]](
recordIter.map(record => {
readMetrics.incRecordsRead(1)
record
}),
context.taskMetrics().updateShuffleReadMetrics())
// An interruptible iterator must be used here in order to support task cancellation
val interruptibleIter = new InterruptibleIterator[(Any, Any)](context, metricIter)
val aggregatedIter: Iterator[Product2[K, C]] = if (dep.aggregator.isDefined) {
if (dep.mapSideCombine) {
// We are reading values that are already combined
// 在这执行聚合方法了，在map端已经进行合并的了，这个数据先分组再count操作
val combinedKeyValuesIterator = interruptibleIter.asInstanceOf[Iterator[(K, C)]]
dep.aggregator.get.combineCombinersByKey(combinedKeyValuesIterator, context)
} else {
// 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
// 在shullfer阶段已经执行这些方法的了,这个在最后所有数据进行count操作
val keyValuesIterator = interruptibleIter.asInstanceOf[Iterator[(K, Nothing)]]
dep.aggregator.get.combineValuesByKey(keyValuesIterator, context)
}
} else {
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 {
case Some(keyOrd: Ordering[K]) =>
// Create an ExternalSorter to sort the data. Note that if spark.shuffle.spill is disabled,
// the ExternalSorter won't spill to disk.
// 对这数据进行排序
val sorter =
new ExternalSorter[K, C, C](context, ordering = Some(keyOrd), serializer = Some(ser))
sorter.insertAll(aggregatedIter)
context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
context.internalMetricsToAccumulators(
InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.peakMemoryUsedBytes)
CompletionIterator[Product2[K, C], Iterator[Product2[K, C]]](sorter.iterator, sorter.stop())
case None =>
aggregatedIter
}
}
当数据拉取回来后生成iteraror ,然后判断是否有aggregator聚合函数，如果有就执行，
所以这样就可以在shuffle分片数据的过程就可以提前执行聚合函数，减少传输到后面的数据量。
所以像一些count 或者 sum的操作，其实可以直接在这里进行执行，这样在这个过程中就只要把这个
结果传到后面就可以了，数据量就大大减少了。
同时如果要对Shuffle的分片数据进行排序的需求keyOrdering，就直接在这里创建一个ExternalSorter
对象，对上面的数据进行排序返回，所以就在这个shffle的分片阶段中可以实现aggregate聚合函数和keyordering
对字段排序的功能。
总结
- 请求读取指定的分片数据split
- 去MapOutputTrackerMaster拉取该shuffleid的分片地址信息
- 通过netty到相关的地址拉取指定Partition的数据
- 去拉取回来的数据执行聚合函数操作
- 去执行后的iterator数据执行 keyorder排序数据，然后最后返回