现在回过头来， 打算看一下spark core部分代码， 就先找了下saveAsTextFile这个方法作为入口， 看一下是怎么保存文档到hadoop中，并且怎么切分stage以及提交Task。 中间也会触碰到DAGScheduler， 也能明白为什么大家都说DAGScheduler是作业调度的核心了
看一下saveAsTextFile代码：
def saveAsTextFile(path: String): Unit = withScope {
// https://issues.apache.org/jira/browse/SPARK-2075
//
// NullWritable is a `Comparable` in Hadoop 1.+, so the compiler cannot find an implicit
// Ordering for it and will use the default `null`. However, it's a `Comparable[NullWritable]`
// in Hadoop 2.+, so the compiler will call the implicit `Ordering.ordered` method to create an
// Ordering for `NullWritable`. That's why the compiler will generate different anonymous
// classes for `saveAsTextFile` in Hadoop 1.+ and Hadoop 2.+.
//
// Therefore, here we provide an explicit Ordering `null` to make sure the compiler generate
// same bytecodes for `saveAsTextFile`.
val nullWritableClassTag = implicitly[ClassTag[NullWritable]]
val textClassTag = implicitly[ClassTag[Text]]
val r = this.mapPartitions { iter =>
val text = new Text()
iter.map { x =>
text.set(x.toString)
(NullWritable.get(), text)
}
}
RDD.rddToPairRDDFunctions(r)(nullWritableClassTag, textClassTag, null)
.saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path)
}
这里很简单， 就是定义了一些输出类， 给每个partition设置了一下， 然后通过执行saveAsHadoopFile 来创建hadoop的txt文件， 看一下saveAsHadoopFile：
def saveAsHadoopFile[F <: OutputFormat[K, V]](
path: String)(implicit fm: ClassTag[F]): Unit = self.withScope {
saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]])
}
直接调用了saveAsHadoopFile， 那么我们继续跟进去：
def saveAsHadoopFile(
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[_ <: OutputFormat[_, _]],
conf: JobConf = new JobConf(self.context.hadoopConfiguration),
codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
// Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
val hadoopConf = conf
hadoopConf.setOutputKeyClass(keyClass)
hadoopConf.setOutputValueClass(valueClass)
conf.setOutputFormat(outputFormatClass)
for (c <- codec) {
hadoopConf.setCompressMapOutput(true)
hadoopConf.set("mapred.output.compress", "true")
hadoopConf.setMapOutputCompressorClass(c)
hadoopConf.set("mapred.output.compression.codec", c.getCanonicalName)
hadoopConf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
}
// Use configured output committer if already set
if (conf.getOutputCommitter == null) {
hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
}
// When speculation is on and output committer class name contains "Direct", we should warn
// users that they may loss data if they are using a direct output committer.
val speculationEnabled = self.conf.getBoolean("spark.speculation", false)
val outputCommitterClass = hadoopConf.get("mapred.output.committer.class", "")
if (speculationEnabled && outputCommitterClass.contains("Direct")) {
val warningMessage =
s"$outputCommitterClass may be an output committer that writes data directly to " +
"the final location. Because speculation is enabled, this output committer may " +
"cause data loss (see the case in SPARK-10063). If possible, please use a output " +
"committer that does not have this behavior (e.g. FileOutputCommitter)."
logWarning(warningMessage)
}
FileOutputFormat.setOutputPath(hadoopConf,
SparkHadoopWriter.createPathFromString(path, hadoopConf))
saveAsHadoopDataset(hadoopConf)
}
这个里面其实主要就是设置了hadoopconf的属性，然后设置到了FileOutputFormat里面， 再通过saveAsHadoopDataset继续执行saveAsTextFile：
def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope {
// Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
val hadoopConf = conf
val outputFormatInstance = hadoopConf.getOutputFormat
val keyClass = hadoopConf.getOutputKeyClass
val valueClass = hadoopConf.getOutputValueClass
if (outputFormatInstance == null) {
throw new SparkException("Output format class not set")
}
if (keyClass == null) {
throw new SparkException("Output key class not set")
}
if (valueClass == null) {
throw new SparkException("Output value class not set")
}
SparkHadoopUtil.get.addCredentials(hadoopConf)
logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
valueClass.getSimpleName + ")")
if (isOutputSpecValidationEnabled) {
// FileOutputFormat ignores the filesystem parameter
val ignoredFs = FileSystem.get(hadoopConf)
hadoopConf.getOutputFormat.checkOutputSpecs(ignoredFs, hadoopConf)
}
val writer = new SparkHadoopWriter(hadoopConf)
writer.preSetup()
val writeToFile = (context: TaskContext, iter: Iterator[(K, V)]) => {
// Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
// around by taking a mod. We expect that no task will be attempted 2 billion times.
val taskAttemptId = (context.taskAttemptId % Int.MaxValue).toInt
val (outputMetrics, bytesWrittenCallback) = initHadoopOutputMetrics(context)
writer.setup(context.stageId, context.partitionId, taskAttemptId)
writer.open()
var recordsWritten = 0L
Utils.tryWithSafeFinallyAndFailureCallbacks {
while (iter.hasNext) {
val record = iter.next()
writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef])
// Update bytes written metric every few records
maybeUpdateOutputMetrics(bytesWrittenCallback, outputMetrics, recordsWritten)
recordsWritten += 1
}
}(finallyBlock = writer.close())
writer.commit()
bytesWrittenCallback.foreach { fn => outputMetrics.setBytesWritten(fn()) }
outputMetrics.setRecordsWritten(recordsWritten)
}
self.context.runJob(self, writeToFile)
writer.commitJob()
}
这里主要做了几件事：
1.创建了一个writer： val writer = new SparkHadoopWriter(hadoopConf)
2. 创建了writeToFile这个function， 这个function会被作为一个Job提交： self.context.runJob(self, writeToFile)
writeToFile其实就是从Job中获取数据写到对应的partition里面去
主要还是要看runJob里面做了什么：
def runJob[T, U: ClassTag](rdd: RDD[T], func: (TaskContext, Iterator[T]) => U): Array[U] = {
runJob(rdd, func, 0 until rdd.partitions.length)
}
继续：
def runJob[T, U: ClassTag](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int]): Array[U] = {
val results = new Array[U](partitions.size)
runJob[T, U](rdd, func, partitions, (index, res) => results(index) = res)
results
}
再继续， 这里有一个回调函数(index, res) => results(index) = res， 就是把计算的result存到results里面:
def runJob[T, U: ClassTag](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
resultHandler: (Int, U) => Unit): Unit = {
if (stopped.get()) {
throw new IllegalStateException("SparkContext has been shutdown")
}
val callSite = getCallSite
val cleanedFunc = clean(func)
logInfo("Starting job: " + callSite.shortForm)
if (conf.getBoolean("spark.logLineage", false)) {
logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
}
dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
progressBar.foreach(_.finishAll())
rdd.doCheckpoint()
}
注意这里的参数func一直就是在最开始定义的 将数据写到partition里面的writeToFile。
看到这里有调用到dagScheduler， 在初始化SparkContext之前， dagScheduler已经被构造了： （回头会写一下SparkContext的初始化）
private[spark] def dagScheduler: DAGScheduler = _dagScheduler
_dagScheduler = new DAGScheduler(this)
我们看一下dagScheduler里面的runJob干了什么：
def runJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): Unit = {
val start = System.nanoTime
val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
waiter.awaitResult() match {
case JobSucceeded =>
logInfo("Job %d finished: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
case JobFailed(exception: Exception) =>
logInfo("Job %d failed: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
// SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
val callerStackTrace = Thread.currentThread().getStackTrace.tail
exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
throw exception
}
}
里面调用了submitJob, 所以我们说Job是通过DAGScheduler去提交的， 可以看到Job提交后会有waiter一直awaitResult()， 将结果打印到日志里面， Job结束的时候writeToFile也执行完成了， txt文件也存到hadoop里面了。
那么接下来看一下DAGScheduler怎么提交Job的， 进入submitJob：
def submitJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): JobWaiter[U] = {
// Check to make sure we are not launching a task on a partition that does not exist.
val maxPartitions = rdd.partitions.length
partitions.find(p => p >= maxPartitions || p < 0).foreach { p =>
throw new IllegalArgumentException(
"Attempting to access a non-existent partition: " + p + ". " +
"Total number of partitions: " + maxPartitions)
}
val jobId = nextJobId.getAndIncrement()
if (partitions.size == 0) {
// Return immediately if the job is running 0 tasks
return new JobWaiter[U](this, jobId, 0, resultHandler)
}
assert(partitions.size > 0)
val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
eventProcessLoop.post(JobSubmitted(
jobId, rdd, func2, partitions.toArray, callSite, waiter,
SerializationUtils.clone(properties)))
waiter
}
里面先确定patition的数量是正常范围内， 然后创建JobId， 如果partions是0 代表最终没有task， 所以直接返回JobWaiter， 如果定partition大于0， 则创建JobWaiter用来返回去执行 awaitResult， 然后通过eventProcessLoop 把JobSubmitted的event加入进去， 那么 eventProcessLoop 是什么呢：
private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)
看到eventProcessLoop 其实是DAGSchedulerEventProcessLoop，（继承自EventLoop） 那么问题来了， 放进去的event是怎么被调用的呢， 那么我们要回到DAGScheduler的构造过程中， 看到创建DAGScheduler里面执行了eventProcessLoop.start()
这个start直接调用的是EventLoop的start方法：
def start(): Unit = {
if (stopped.get) {
throw new IllegalStateException(name + " has already been stopped")
}
// Call onStart before starting the event thread to make sure it happens before onReceive
onStart()
eventThread.start()
}
在DAGSchedulerEventProcessLoop没有定义onStart方法， 所以其实有用的是eventThread.start()方法， 这个方法如下：
private val eventThread = new Thread(name) {
setDaemon(true)
override def run(): Unit = {
try {
while (!stopped.get) {
val event = eventQueue.take()
try {
onReceive(event)
} catch {
case NonFatal(e) => {
try {
onError(e)
} catch {
case NonFatal(e) => logError("Unexpected error in " + name, e)
}
}
}
}
} catch {
case ie: InterruptedException => // exit even if eventQueue is not empty
case NonFatal(e) => logError("Unexpected error in " + name, e)
}
}
}
他就是一个thread， 然后start方法回去跑run里面的东西， 所以调用了onReceive(event)方法， 如下： （DAGSchedulerEventProcessLoop的onReceive）
override def onReceive(event: DAGSchedulerEvent): Unit = {
val timerContext = timer.time()
try {
doOnReceive(event)
} finally {
timerContext.stop()
}
}
继续调用doOnReceive(event) ：
private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
case StageCancelled(stageId) =>
dagScheduler.handleStageCancellation(stageId)
case JobCancelled(jobId) =>
dagScheduler.handleJobCancellation(jobId)
case JobGroupCancelled(groupId) =>
dagScheduler.handleJobGroupCancelled(groupId)
case AllJobsCancelled =>
dagScheduler.doCancelAllJobs()
case ExecutorAdded(execId, host) =>
dagScheduler.handleExecutorAdded(execId, host)
case ExecutorLost(execId) =>
dagScheduler.handleExecutorLost(execId, fetchFailed = false)
case BeginEvent(task, taskInfo) =>
dagScheduler.handleBeginEvent(task, taskInfo)
case GettingResultEvent(taskInfo) =>
dagScheduler.handleGetTaskResult(taskInfo)
case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
dagScheduler.handleTaskCompletion(completion)
case TaskSetFailed(taskSet, reason, exception) =>
dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
case ResubmitFailedStages =>
dagScheduler.resubmitFailedStages()
}
好啦， 看到了case JobSubmitted。。 这个就是我们event加进去后， 执行的时候就会走到这个case里面， 去执行
dagScheduler.handleJobSubmitted
那么在handleJobSubmitted里面做了什么呢：
private[scheduler] def handleJobSubmitted(jobId: Int,
finalRDD: RDD[_],
func: (TaskContext, Iterator[_]) => _,
partitions: Array[Int],
callSite: CallSite,
listener: JobListener,
properties: Properties) {
var finalStage: ResultStage = null
try {
// New stage creation may throw an exception if, for example, jobs are run on a
// HadoopRDD whose underlying HDFS files have been deleted.
finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
} catch {
case e: Exception =>
logWarning("Creating new stage failed due to exception - job: " + jobId, e)
listener.jobFailed(e)
return
}
val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
clearCacheLocs()
logInfo("Got job %s (%s) with %d output partitions".format(
job.jobId, callSite.shortForm, partitions.length))
logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
logInfo("Parents of final stage: " + finalStage.parents)
logInfo("Missing parents: " + getMissingParentStages(finalStage))
val jobSubmissionTime = clock.getTimeMillis()
jobIdToActiveJob(jobId) = job
activeJobs += job
finalStage.setActiveJob(job)
val stageIds = jobIdToStageIds(jobId).toArray
val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
listenerBus.post(
SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
submitStage(finalStage)
submitWaitingStages()
}
先拿到
finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
在newResultStage里面的代码：
private def newResultStage(
rdd: RDD[_],
func: (TaskContext, Iterator[_]) => _,
partitions: Array[Int],
jobId: Int,
callSite: CallSite): ResultStage = {
val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId)
val stage = new ResultStage(id, rdd, func, partitions, parentStages, jobId, callSite)
stageIdToStage(id) = stage
updateJobIdStageIdMaps(jobId, stage)
stage
}
他会通过getParentStagesAndId拿到parents和stage ID然后根据这两个参数创建一个resultStage返回， 那么getParentStagesAndId里面是怎么做的呢：
private def getParentStagesAndId(rdd: RDD[_], firstJobId: Int): (List[Stage], Int) = {
val parentStages = getParentStages(rdd, firstJobId)
val id = nextStageId.getAndIncrement()
(parentStages, id)
}
stageID是从一个increment里面创建的， parentStages是从getParentStages方法里面拿的：
private def getParentStages(rdd: RDD[_], firstJobId: Int): List[Stage] = {
val parents = new HashSet[Stage]
val visited = new HashSet[RDD[_]]
// We are manually maintaining a stack here to prevent StackOverflowError
// caused by recursively visiting
val waitingForVisit = new Stack[RDD[_]]
def visit(r: RDD[_]) {
if (!visited(r)) {
visited += r
// Kind of ugly: need to register RDDs with the cache here since
// we can't do it in its constructor because # of partitions is unknown
for (dep <- r.dependencies) {
dep match {
case shufDep: ShuffleDependency[_, _, _] =>
parents += getShuffleMapStage(shufDep, firstJobId)
case _ =>
waitingForVisit.push(dep.rdd)
}
}
}
}
waitingForVisit.push(rdd)
while (waitingForVisit.nonEmpty) {
visit(waitingForVisit.pop())
}
parents.toList
}
这里可以看到实际上spark是根据rdd的dependence， 如果是ShuffleDependency那么就分割出来， 如果不是那么放到waitingForVisit的列表中继续查找他的父rdd， 直到循环结束， 或者父rdd的dependence是ShuffleDependency为止， 然后getShuffleMapStage返回到parents里面再返回到前面调用的方法. 所以我们可以看到stage的划分其实是根据rdd的dependence是不是ShuffleDependency来分的。
接下来看一下getShuffleMapStage里面做了什么：
private def getShuffleMapStage(
shuffleDep: ShuffleDependency[_, _, _],
firstJobId: Int): ShuffleMapStage = {
shuffleToMapStage.get(shuffleDep.shuffleId) match {
case Some(stage) => stage
case None =>
// We are going to register ancestor shuffle dependencies
getAncestorShuffleDependencies(shuffleDep.rdd).foreach { dep =>
shuffleToMapStage(dep.shuffleId) = newOrUsedShuffleStage(dep, firstJobId)
}
// Then register current shuffleDep
val stage = newOrUsedShuffleStage(shuffleDep, firstJobId)
shuffleToMapStage(shuffleDep.shuffleId) = stage
stage
}
}
其实就是创建一个shuffleStage及返回。 顺便再通过getAncestorShuffleDependencies 把所有和当前stage相关联的ShuffleDependency全部加到shuffleToMapStage， 以备后用。 好了现在知道了之前创建的那个resultStage其实是根据一堆ShuffleDependency的stage创建出来的， 那么我们回到handleJobSubmitted方法里面， 在拿到了finalStage （一个resultStage）后会根据其创建一个ActiveJob：
val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
再通过finalStage.setActiveJob(job) 和finalStage关联起来， 最后通过submitStage(finalStage)提交。
submitStage里面：
private def submitStage(stage: Stage) {
val jobId = activeJobForStage(stage)
if (jobId.isDefined) {
logDebug("submitStage(" + stage + ")")
if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) {
val missing = getMissingParentStages(stage).sortBy(_.id)
logDebug("missing: " + missing)
if (missing.isEmpty) {
logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
submitMissingTasks(stage, jobId.get)
} else {
for (parent <- missing) {
submitStage(parent)
}
waitingStages += stage
}
}
} else {
abortStage(stage, "No active job for stage " + stage.id, None)
}
}
这里面其实做的就是先去查祖先stage是不是都active了， 如果不是active的话就放到missing里面， 先提交所有的inactive的stage，并且把当前stage放入waitingStages里面 把所有当前stage的祖先stage都submit后才有可能submit当前的stage。 所以stage都是有关联顺序的， 只有所有祖先stage都提交了， 才会去执行当前stage。 执行当前stage的时候其实是调用submitMissingTasks这个方法是根据stage提交task， 后面有机会说一下。 waitingStages 会通过submitWaitingStages方法去执行：
private def submitWaitingStages() {
// TODO: We might want to run this less often, when we are sure that something has become
// runnable that wasn't before.
logTrace("Checking for newly runnable parent stages")
logTrace("running: " + runningStages)
logTrace("waiting: " + waitingStages)
logTrace("failed: " + failedStages)
val waitingStagesCopy = waitingStages.toArray
waitingStages.clear()
for (stage <- waitingStagesCopy.sortBy(_.firstJobId)) {
submitStage(stage)
}
}
可以看到里面其实也是调用submitStage去对所有的waitingstage做处理， 最后以task提交。 当task提交后我们的writeToFile就会被执行， 数据就会写到指定的hadoop路径中， 整个过程大概就是这个样子， 哪里不对的麻烦指正一下