Spark任务调度流程及调度策略分析
Spark任务调度
TaskScheduler调度入口：
（1）CoarseGrainedSchedulerBackend 在启动时会创建DriverEndPoint. 而DriverEndPoint中存在一定时任务，每隔一定时间（spark.scheduler.revive.interval, 默认为1s）进行一次调度（给自身发送ReviveOffers消息， 进行调用makeOffers进行调度）。代码如下所示
override def onStart() {
// Periodically revive offers to allow delay scheduling to work
val reviveIntervalMs = conf.getTimeAsMs("spark.scheduler.revive.interval", "1s")
reviveThread.scheduleAtFixedRate(new Runnable {
override def run(): Unit = Utils.tryLogNonFatalError {
Option(self).foreach(_.send(ReviveOffers))
}
}, 0, reviveIntervalMs, TimeUnit.MILLISECONDS)
}
（2）当Executor执行完成已分配任务时，会向Driver发送StatusUpdate消息,当Driver接收到消息后会调用 makeOffers(executorId)方法，进行任务调度, CoarseGrainedExecutorBackend 状态变化时向Driver (DriverEndPoint)发送StatusUpdate消息
override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
val msg = StatusUpdate(executorId, taskId, state, data)
driver match {
case Some(driverRef) => driverRef.send(msg)
case None => logWarning(s"Drop $msg because has not yet connected to driver")
}
}
Dirver接收到StatusUpdate消息时将会触发设调度（makeOffers）,为完成任务的Executor分配任务。
override def receive: PartialFunction[Any, Unit] = {
case StatusUpdate(executorId, taskId, state, data) =>
scheduler.statusUpdate(taskId, state, data.value)
if (TaskState.isFinished(state)) {
executorDataMap.get(executorId) match {
case Some(executorInfo) =>
executorInfo.freeCores += scheduler.CPUS_PER_TASK
makeOffers(executorId)
case None =>
// Ignoring the update since we don't know about the executor.
logWarning(s"Ignored task status update ($taskId state $state) " +
s"from unknown executor with ID $executorId")
}
}
case ReviveOffers =>
makeOffers()
case KillTask(taskId, executorId, interruptThread) =>
executorDataMap.get(executorId) match {
case Some(executorInfo) =>
executorInfo.executorEndpoint.send(KillTask(taskId, executorId, interruptThread))
case None =>
// Ignoring the task kill since the executor is not registered.
logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.")
}
}
其中makeOffers方法，会调用TaskSchedulerImpl中的resourceOffers方法，依其调度策略为Executor分配适合的任务。具体代码如下：
a、为所有资源分配任务
// Make fake resource offers on all executors
private def makeOffers() {
// Filter out executors under killing
val activeExecutors = executorDataMap.filterKeys(!executorsPendingToRemove.contains(_))
val workOffers = activeExecutors.map { case (id, executorData) =>
new WorkerOffer(id, executorData.executorHost, executorData.freeCores)
}.toSeq
launchTasks(scheduler.resourceOffers(workOffers))
}
b、为单个executor分配任务
// Make fake resource offers on just one executor
private def makeOffers(executorId: String) {
// Filter out executors under killing
if (!executorsPendingToRemove.contains(executorId)) {
val executorData = executorDataMap(executorId)
val workOffers = Seq(
new WorkerOffer(executorId, executorData.executorHost, executorData.freeCores))
launchTasks(scheduler.resourceOffers(workOffers))
}
}
分配完任务后，向Executor发送LaunchTask指令，启动任务，执行用户逻辑代码
// Launch tasks returned by a set of resource offers
private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
for (task <- tasks.flatten) {
val serializedTask = ser.serialize(task)
if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
scheduler.taskIdToTaskSetManager.get(task.taskId).foreach { taskSetMgr =>
try {
var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
"spark.akka.frameSize (%d bytes) - reserved (%d bytes). Consider increasing " +
"spark.akka.frameSize or using broadcast variables for large values."
msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize,
AkkaUtils.reservedSizeBytes)
taskSetMgr.abort(msg)
} catch {
case e: Exception => logError("Exception in error callback", e)
}
}
}
else {
val executorData = executorDataMap(task.executorId)
executorData.freeCores -= scheduler.CPUS_PER_TASK
executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
}
}
}
Spark任务调度策略
1、FIFO
FIFO(先进先出)方式调度Job，如下图所示，每个Job被切分成多个Stage.第一个Job优先获取所有可用资源，接下来第二个Job再获取剩余可用资源。（每个Stage对应一个TaskSetManager）
2、FAIR
FAIR共享模式调度下，Spark以在多Job之间轮询方式为任务分配资源，所有的任务拥有大致相当的优先级来共享集群的资源。FAIR调度模型如下图：
下面从源码的角度对调度策略进行说明:
当触发调度时，会调用TaskSchedulerImpl的resourceOffers方法，方法中会依照调度策略选出要执行的TaskSet, 然后取出适合(考虑本地性)的task交由Executor执行， 其代码如下：
/**
* Called by cluster manager to offer resources on slaves. We respond by asking our active task
* sets for tasks in order of priority. We fill each node with tasks in a round-robin manner so
* that tasks are balanced across the cluster.
*/
def resourceOffers(offers: Seq[WorkerOffer]): Seq[Seq[TaskDescription]] = synchronized {
// Mark each slave as alive and remember its hostname
// Also track if new executor is added
var newExecAvail = false
for (o <- offers) {
executorIdToHost(o.executorId) = o.host
activeExecutorIds += o.executorId
if (!executorsByHost.contains(o.host)) {
executorsByHost(o.host) = new HashSet[String]()
executorAdded(o.executorId, o.host)
newExecAvail = true
}
for (rack <- getRackForHost(o.host)) {
hostsByRack.getOrElseUpdate(rack, new HashSet[String]()) += o.host
}
}
// Randomly shuffle offers to avoid always placing tasks on the same set of workers.
val shuffledOffers = Random.shuffle(offers)
// Build a list of tasks to assign to each worker.
val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores))
val availableCpus = shuffledOffers.map(o => o.cores).toArray
val sortedTaskSets = rootPool.getSortedTaskSetQueue
for (taskSet <- sortedTaskSets) {
logDebug("parentName: %s, name: %s, runningTasks: %s".format(
taskSet.parent.name, taskSet.name, taskSet.runningTasks))
if (newExecAvail) {
taskSet.executorAdded()
}
}
// Take each TaskSet in our scheduling order, and then offer it each node in increasing order
// of locality levels so that it gets a chance to launch local tasks on all of them.
// NOTE: the preferredLocality order: PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY
var launchedTask = false
for (taskSet <- sortedTaskSets; maxLocality <- taskSet.myLocalityLevels) {
do {
launchedTask = resourceOfferSingleTaskSet(
taskSet, maxLocality, shuffledOffers, availableCpus, tasks)
} while (launchedTask)
}
if (tasks.size > 0) {
hasLaunchedTask = true
}
return tasks
}
经过分析可知，通过rootPool.getSortedTaskSetQueue对队列中的TaskSet进行排序，getSortedTaskSetQueue的具体实现如下：
override def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] = {
var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]
val sortedSchedulableQueue =
schedulableQueue.asScala.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator)
for (schedulable <- sortedSchedulableQueue) {
sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue
}
sortedTaskSetQueue
}
由上述代码可知，其通过算法做为比较器对taskSet进行排序， 其中调度算法有FIFO和FAIR两种，下面分别进行介绍。
FIFO
优先级（Priority）： 在DAGscheduler创建TaskSet时使用JobId做为优先级的值。
FIFO调度算法实现如下所示
private[spark] class FIFOSchedulingAlgorithm extends SchedulingAlgorithm {
override def comparator(s1: Schedulable, s2: Schedulable): Boolean = {
val priority1 = s1.priority
val priority2 = s2.priority
var res = math.signum(priority1 - priority2)
if (res == 0) {
val stageId1 = s1.stageId
val stageId2 = s2.stageId
res = math.signum(stageId1 - stageId2)
}
if (res < 0) {
true
} else {
false
}
}
}
由源码可知，FIFO依据JobId进行挑选较小值。因为越早提交的作业，JobId越小。
对同一个作业(Job)来说越先生成的Stage,其StageId越小，有依赖关系的多个Stage之间，DAGScheduler会控制Stage是否会被提交到调度队列中（若其依赖的Stage未执行完前，此Stage不会被提交），其调度顺序可通过此来保证。但若某Job中有两个无入度的Stage的话，则先调度StageId小的Stage.
Fair
Fair调度队列相比FIFO较复杂，其可存在多个调度队列，且队列呈树型结构(现阶段Spark的Fair调度只支持两层树结构)，每用户可以使用sc.setLocalProperty(“spark.scheduler.pool”, “poolName”)来指定要加入的队列，默认情况下会加入到buildDefaultPool。每个队列中还可指定自己内部的调度策略，且Fair还存在一些特殊的属性：
schedulingMode: 设置调度池的调度模式FIFO或FAIR, 默认为FIFO.
minShare：最少资源保证量，当一个队列最少资源未满足时，它将优先于其它同级队列获取资源。
weight: 在一个队列内部分配资源时，默认情况下，采用公平轮询的方法将资源分配给各个应用程序，而该参数则将打破这种平衡。例如，如果用户配置一个指定调度池权重为2， 那么这个调度池将会获得相对于权重为1的调度池2倍的资源。
以上参数，可通过conf/fairscheduler.xml文件配置调度池的属性。
Fair调度算法实现如下所示：
private[spark] class FairSchedulingAlgorithm extends SchedulingAlgorithm {
override def comparator(s1: Schedulable, s2: Schedulable): Boolean = {
val minShare1 = s1.minShare
val minShare2 = s2.minShare
val runningTasks1 = s1.runningTasks
val runningTasks2 = s2.runningTasks
val s1Needy = runningTasks1 < minShare1
val s2Needy = runningTasks2 < minShare2
val minShareRatio1 = runningTasks1.toDouble / math.max(minShare1, 1.0).toDouble
val minShareRatio2 = runningTasks2.toDouble / math.max(minShare2, 1.0).toDouble
val taskToWeightRatio1 = runningTasks1.toDouble / s1.weight.toDouble
val taskToWeightRatio2 = runningTasks2.toDouble / s2.weight.toDouble
var compare: Int = 0
if (s1Needy && !s2Needy) {
return true
} else if (!s1Needy && s2Needy) {
return false
} else if (s1Needy && s2Needy) {
compare = minShareRatio1.compareTo(minShareRatio2)
} else {
compare = taskToWeightRatio1.compareTo(taskToWeightRatio2)
}
if (compare < 0) {
true
} else if (compare > 0) {
false
} else {
s1.name < s2.name
}
}
}
由原码可知，未满足minShare规定份额的资源的队列或任务集先执行；如果所有均不满足minShare的话，则选择缺失比率小的先调度；如果均不满足，则按执行权重比进行选择，先调度执行权重比小的。如果执行权重也相同的话则会选择StageId小的进行调度（name=“TaskSet_”+ taskSet.stageId.toString）。
以此为标准将所有TaskSet进行排序， 然后选出优先级最高的进行调度。
Spark 任务调度之任务本地性
当选出TaskSet后，将按本地性从中挑选适合Executor的任务，在Executor上执行。
