源码目录
1 程序入口
var conf: SparkConf = new SparkConf().setAppName("SparkJob_Demo").setMaster("local[*]")
val sparkContext: SparkContext = new SparkContext(conf)
sparkContext.parallelize(List("aaa", "bbb", "ccc", "ddd"), 2)
.repartition(4)
.collect()
2 进入源码
- 进入
org.apache.spark.scheduler.TaskSchedulerImpl.scala
override def submitTasks(taskSet: TaskSet) {
val tasks = taskSet.tasks
logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
this.synchronized {
val manager = createTaskSetManager(taskSet, maxTaskFailures)
val stage = taskSet.stageId
val stageTaskSets =
taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
// Mark all the existing TaskSetManagers of this stage as zombie, as we are adding a new one.
// This is necessary to handle a corner case. Let's say a stage has 10 partitions and has 2
// TaskSetManagers: TSM1(zombie) and TSM2(active). TSM1 has a running task for partition 10
// and it completes. TSM2 finishes tasks for partition 1-9, and thinks he is still active
// because partition 10 is not completed yet. However, DAGScheduler gets task completion
// events for all the 10 partitions and thinks the stage is finished. If it's a shuffle stage
// and somehow it has missing map outputs, then DAGScheduler will resubmit it and create a
// TSM3 for it. As a stage can't have more than one active task set managers, we must mark
// TSM2 as zombie (it actually is).
stageTaskSets.foreach { case (_, ts) =>
ts.isZombie = true
}
stageTaskSets(taskSet.stageAttemptId) = manager
schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
if (!isLocal && !hasReceivedTask) {
starvationTimer.scheduleAtFixedRate(new TimerTask() {
override def run() {
if (!hasLaunchedTask) {
logWarning("Initial job has not accepted any resources; " +
"check your cluster UI to ensure that workers are registered " +
"and have sufficient resources")
} else {
this.cancel()
}
}
}, STARVATION_TIMEOUT_MS, STARVATION_TIMEOUT_MS)
}
hasReceivedTask = true
}
backend.reviveOffers()
}
- 为提交的各 TaskSet 分别创建 TaskSetManager；
- 将 TaskSetManager 放到 SchedulableBuilder 的队列中；
- 在非本地模式下，启一个定时器定时检测提交的任务是否被启动；
- 调用 backend.reviveOffers() 恢复 SchedulerBackend 开始运行。
2.1 TaskSet放入调度器队列
以默认的 FIFO 调度模式为例。
- 进入
org.apache.spark.scheduler.FIFOSchedulableBuilder.scala
override def addTaskSetManager(manager: Schedulable, properties: Properties) {
rootPool.addSchedulable(manager)
}
- 进入
org.apache.spark.scheduler.Pool.scala
override def addSchedulable(schedulable: Schedulable) {
require(schedulable != null)
schedulableQueue.add(schedulable)
schedulableNameToSchedulable.put(schedulable.name, schedulable)
schedulable.parent = this
}
将 TaskSetManager 加入到 Pool.schedulableQueue 队列中。
2.2 分配资源
- 进入
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.scala
override def reviveOffers() {
driverEndpoint.send(ReviveOffers)
}
发消息 ReviveOffers 给 Driver。
- 进入
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.DriverEndpoint.scala
override def receive: PartialFunction[Any, Unit] = {
case ReviveOffers =>
makeOffers()
}
// Make fake resource offers on all executors
private def makeOffers() {
// Make sure no executor is killed while some task is launching on it
val taskDescs = withLock {
// Filter out executors under killing
val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
val workOffers = activeExecutors.map {
case (id, executorData) =>
new WorkerOffer(id, executorData.executorHost, executorData.freeCores,
Some(executorData.executorAddress.hostPort))
}.toIndexedSeq
scheduler.resourceOffers(workOffers)
}
if (!taskDescs.isEmpty) {
launchTasks(taskDescs)
}
}
- 从 CoarseGrainedSchedulerBackend.executorDataMap 中选出状态为 Alive 的 Executors（Spark源码：启动Executors 时，当Executor启动完成注册到Driver时，会将Executor加入到 CoarseGrainedSchedulerBackend.executorDataMap 中）；
- 将 Executor 分别封装成 WorkerOffer；
- 调用 scheduler.resourceOffers(workOffers) 为 Tasks 分配资源；
- 启动 Tasks。
- 进入
org.apache.spark.scheduler.TaskSchedulerImpl.scala
/**
* 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: IndexedSeq[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) {
if (!hostToExecutors.contains(o.host)) {
hostToExecutors(o.host) = new HashSet[String]()
}
if (!executorIdToRunningTaskIds.contains(o.executorId)) {
hostToExecutors(o.host) += o.executorId
executorAdded(o.executorId, o.host)
executorIdToHost(o.executorId) = o.host
executorIdToRunningTaskIds(o.executorId) = HashSet[Long]()
newExecAvail = true
}
for (rack <- getRackForHost(o.host)) {
hostsByRack.getOrElseUpdate(rack, new HashSet[String]()) += o.host
}
}
// Before making any offers, remove any nodes from the blacklist whose blacklist has expired. Do
// this here to avoid a separate thread and added synchronization overhead, and also because
// updating the blacklist is only relevant when task offers are being made.
blacklistTrackerOpt.foreach(_.applyBlacklistTimeout())
val filteredOffers = blacklistTrackerOpt.map { blacklistTracker =>
offers.filter { offer =>
!blacklistTracker.isNodeBlacklisted(offer.host) &&
!blacklistTracker.isExecutorBlacklisted(offer.executorId)
}
}.getOrElse(offers)
val shuffledOffers = shuffleOffers(filteredOffers)
// Build a list of tasks to assign to each worker.
val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores / CPUS_PER_TASK))
val availableCpus = shuffledOffers.map(o => o.cores).toArray
val availableSlots = shuffledOffers.map(o => o.cores / CPUS_PER_TASK).sum
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
for (taskSet <- sortedTaskSets) {
// Skip the barrier taskSet if the available slots are less than the number of pending tasks.
if (taskSet.isBarrier && availableSlots < taskSet.numTasks) {
// Skip the launch process.
// TODO SPARK-24819 If the job requires more slots than available (both busy and free
// slots), fail the job on submit.
logInfo(s"Skip current round of resource offers for barrier stage ${taskSet.stageId} " +
s"because the barrier taskSet requires ${taskSet.numTasks} slots, while the total " +
s"number of available slots is $availableSlots.")
} else {
var launchedAnyTask = false
// Record all the executor IDs assigned barrier tasks on.
val addressesWithDescs = ArrayBuffer[(String, TaskDescription)]()
for (currentMaxLocality <- taskSet.myLocalityLevels) {
var launchedTaskAtCurrentMaxLocality = false
do {
launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(taskSet,
currentMaxLocality, shuffledOffers, availableCpus, tasks, addressesWithDescs)
launchedAnyTask |= launchedTaskAtCurrentMaxLocality
} while (launchedTaskAtCurrentMaxLocality)
}
if (!launchedAnyTask) {
taskSet.getCompletelyBlacklistedTaskIfAny(hostToExecutors).foreach { taskIndex =>
// If the taskSet is unschedulable we try to find an existing idle blacklisted
// executor. If we cannot find one, we abort immediately. Else we kill the idle
// executor and kick off an abortTimer which if it doesn't schedule a task within the
// the timeout will abort the taskSet if we were unable to schedule any task from the
// taskSet.
// Note 1: We keep track of schedulability on a per taskSet basis rather than on a per
// task basis.
// Note 2: The taskSet can still be aborted when there are more than one idle
// blacklisted executors and dynamic allocation is on. This can happen when a killed
// idle executor isn't replaced in time by ExecutorAllocationManager as it relies on
// pending tasks and doesn't kill executors on idle timeouts, resulting in the abort
// timer to expire and abort the taskSet.
executorIdToRunningTaskIds.find(x => !isExecutorBusy(x._1)) match {
case Some ((executorId, _)) =>
if (!unschedulableTaskSetToExpiryTime.contains(taskSet)) {
blacklistTrackerOpt.foreach(blt => blt.killBlacklistedIdleExecutor(executorId))
val timeout = conf.get(config.UNSCHEDULABLE_TASKSET_TIMEOUT) * 1000
unschedulableTaskSetToExpiryTime(taskSet) = clock.getTimeMillis() + timeout
logInfo(s"Waiting for $timeout ms for completely "
+ s"blacklisted task to be schedulable again before aborting $taskSet.")
abortTimer.schedule(
createUnschedulableTaskSetAbortTimer(taskSet, taskIndex), timeout)
}
case None => // Abort Immediately
logInfo("Cannot schedule any task because of complete blacklisting. No idle" +
s" executors can be found to kill. Aborting $taskSet." )
taskSet.abortSinceCompletelyBlacklisted(taskIndex)
}
}
} else {
// We want to defer killing any taskSets as long as we have a non blacklisted executor
// which can be used to schedule a task from any active taskSets. This ensures that the
// job can make progress.
// Note: It is theoretically possible that a taskSet never gets scheduled on a
// non-blacklisted executor and the abort timer doesn't kick in because of a constant
// submission of new TaskSets. See the PR for more details.
if (unschedulableTaskSetToExpiryTime.nonEmpty) {
logInfo("Clearing the expiry times for all unschedulable taskSets as a task was " +
"recently scheduled.")
unschedulableTaskSetToExpiryTime.clear()
}
}
if (launchedAnyTask && taskSet.isBarrier) {
// Check whether the barrier tasks are partially launched.
// TODO SPARK-24818 handle the assert failure case (that can happen when some locality
// requirements are not fulfilled, and we should revert the launched tasks).
require(addressesWithDescs.size == taskSet.numTasks,
s"Skip current round of resource offers for barrier stage ${taskSet.stageId} " +
s"because only ${addressesWithDescs.size} out of a total number of " +
s"${taskSet.numTasks} tasks got resource offers. The resource offers may have " +
"been blacklisted or cannot fulfill task locality requirements.")
// materialize the barrier coordinator.
maybeInitBarrierCoordinator()
// Update the taskInfos into all the barrier task properties.
val addressesStr = addressesWithDescs
// Addresses ordered by partitionId
.sortBy(_._2.partitionId)
.map(_._1)
.mkString(",")
addressesWithDescs.foreach(_._2.properties.setProperty("addresses", addressesStr))
logInfo(s"Successfully scheduled all the ${addressesWithDescs.size} tasks for barrier " +
s"stage ${taskSet.stageId}.")
}
}
}
// TODO SPARK-24823 Cancel a job that contains barrier stage(s) if the barrier tasks don't get
// launched within a configured time.
if (tasks.size > 0) {
hasLaunchedTask = true
}
return tasks
}
- 将所有可用的 WorkerOffer 随机打散；
- 遍历这些 WorkerOffers，每个 WorkerOffer 都利用
WorkerOffer.cores / CPUS_PER_TASK算出每个 WorkerOffer(Executor) 上可以处理的 Task 数，这样每个 WorkerOffer 都对应一个 Array[TaskDescription]； - 调用 rootPool.getSortedTaskSetQueue 获取排完序的 TaskSets；
- 遍历这些 TaskSets，每个 TaskSet 都计算出一个对应的本地性级别（PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY），将这些级别按顺序分别传入 resourceOfferSingleTaskSet 方法去为当前 TaskSet 分配资源；
- 当遍历完所有 TaskSets，且每个 TaskSet 又利用 resourceOfferSingleTaskSet 方法在每个本地性级别下为 Tasks 分配了资源后，所有获得资源的 Tasks 就计算出来了，返回这些 Tasks 准备提交。
注：
- PROCESS_LOCAL：本地进程，Task要计算的数据在同一个Executor中，即同一个JVM中
- NODE_LOCAL：本地节点，速度比 PROCESS_LOCAL 稍慢，因为数据需要在不同进程之间传递或从文件中读取
- NO_PREF：没有偏好
- RACK_LOCAL：本地机架：数据在同一机架的不同节点上。需要通过网络传输数据及文件 IO，比 NODE_LOCAL 慢
- ANY：跨机架，数据在非同一机架的网络上，速度最慢
- 进入
org.apache.spark.scheduler.Pool.scala
override def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] = {
val sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]
val sortedSchedulableQueue =
schedulableQueue.asScala.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator)
for (schedulable <- sortedSchedulableQueue) {
sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue
}
sortedTaskSetQueue
}
在将 TaskSet 放入调度器队列中时，将封装好的 TaskSetManager 放入到了 Pool.schedulableQueue 中。
这里将 Pool.schedulableQueue 中的 TaskSetManager 按照调度算法排序（本文中假设是默认的FIFO）后返回，就得到了排好序的 TaskSets 了。
- 进入
org.apache.spark.scheduler.TaskSchedulerImpl.scala
private def resourceOfferSingleTaskSet(
taskSet: TaskSetManager,
maxLocality: TaskLocality,
shuffledOffers: Seq[WorkerOffer],
availableCpus: Array[Int],
tasks: IndexedSeq[ArrayBuffer[TaskDescription]],
addressesWithDescs: ArrayBuffer[(String, TaskDescription)]) : Boolean = {
var launchedTask = false
// nodes and executors that are blacklisted for the entire application have already been
// filtered out by this point
for (i <- 0 until shuffledOffers.size) {
val execId = shuffledOffers(i).executorId
val host = shuffledOffers(i).host
if (availableCpus(i) >= CPUS_PER_TASK) {
try {
for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
tasks(i) += task
val tid = task.taskId
taskIdToTaskSetManager.put(tid, taskSet)
taskIdToExecutorId(tid) = execId
executorIdToRunningTaskIds(execId).add(tid)
availableCpus(i) -= CPUS_PER_TASK
assert(availableCpus(i) >= 0)
// Only update hosts for a barrier task.
if (taskSet.isBarrier) {
// The executor address is expected to be non empty.
addressesWithDescs += (shuffledOffers(i).address.get -> task)
}
launchedTask = true
}
} catch {
case e: TaskNotSerializableException =>
logError(s"Resource offer failed, task set ${taskSet.name} was not serializable")
// Do not offer resources for this task, but don't throw an error to allow other
// task sets to be submitted.
return launchedTask
}
}
}
return launchedTask
}
此方法用于为单个 TaskSet 分配资源。
遍历 WorkerOffers，针对每个 WorkerOffer，得到 executorId 和 host，如果该 WorkerOffer 的可用核数大于 CPUS_PER_TASK，则调用 resourceOffer 方法基于当前的本地性，为当前 TaskSet 分配资源。
- 进入
org.apache.spark.scheduler.TaskSetManager.scala
@throws[TaskNotSerializableException]
def resourceOffer(
execId: String,
host: String,
maxLocality: TaskLocality.TaskLocality)
: Option[TaskDescription] =
{
val offerBlacklisted = taskSetBlacklistHelperOpt.exists { blacklist =>
blacklist.isNodeBlacklistedForTaskSet(host) ||
blacklist.isExecutorBlacklistedForTaskSet(execId)
}
if (!isZombie && !offerBlacklisted) {
val curTime = clock.getTimeMillis()
var allowedLocality = maxLocality
if (maxLocality != TaskLocality.NO_PREF) {
allowedLocality = getAllowedLocalityLevel(curTime)
if (allowedLocality > maxLocality) {
// We're not allowed to search for farther-away tasks
allowedLocality = maxLocality
}
}
dequeueTask(execId, host, allowedLocality).map { case ((index, taskLocality, speculative)) =>
// Found a task; do some bookkeeping and return a task description
val task = tasks(index)
val taskId = sched.newTaskId()
// Do various bookkeeping
copiesRunning(index) += 1
val attemptNum = taskAttempts(index).size
val info = new TaskInfo(taskId, index, attemptNum, curTime,
execId, host, taskLocality, speculative)
taskInfos(taskId) = info
taskAttempts(index) = info :: taskAttempts(index)
// Update our locality level for delay scheduling
// NO_PREF will not affect the variables related to delay scheduling
if (maxLocality != TaskLocality.NO_PREF) {
currentLocalityIndex = getLocalityIndex(taskLocality)
lastLaunchTime = curTime
}
// Serialize and return the task
val serializedTask: ByteBuffer = try {
ser.serialize(task)
} catch {
// If the task cannot be serialized, then there's no point to re-attempt the task,
// as it will always fail. So just abort the whole task-set.
case NonFatal(e) =>
val msg = s"Failed to serialize task $taskId, not attempting to retry it."
logError(msg, e)
abort(s"$msg Exception during serialization: $e")
throw new TaskNotSerializableException(e)
}
if (serializedTask.limit() > TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024 &&
!emittedTaskSizeWarning) {
emittedTaskSizeWarning = true
logWarning(s"Stage ${task.stageId} contains a task of very large size " +
s"(${serializedTask.limit() / 1024} KB). The maximum recommended task size is " +
s"${TaskSetManager.TASK_SIZE_TO_WARN_KB} KB.")
}
addRunningTask(taskId)
// We used to log the time it takes to serialize the task, but task size is already
// a good proxy to task serialization time.
// val timeTaken = clock.getTime() - startTime
val taskName = s"task ${info.id} in stage ${taskSet.id}"
logInfo(s"Starting $taskName (TID $taskId, $host, executor ${info.executorId}, " +
s"partition ${task.partitionId}, $taskLocality, ${serializedTask.limit()} bytes)")
sched.dagScheduler.taskStarted(task, info)
new TaskDescription(
taskId,
attemptNum,
execId,
taskName,
index,
task.partitionId,
addedFiles,
addedJars,
task.localProperties,
serializedTask)
}
} else {
None
}
}
- 找出 TaskSet 中 Tasks 允许的本地性级别；
- 基于上述本地性级别，调用 dequeueTask 方法得到一个 Task 和 TaskLocality 的队列；
- 遍历队列，得到 TaskSet 中的各 Task，针对每个 Task，生成一个新的 TaskId；
- 调用 ser.serialize(task) 序列化 Task；
- 封装成 TaskDescription(taskId, attemptNumber, executorId, taskName, index, partitionId, addedFiles, addedJars, properties,serializedTask) 并返回。
2.3 启动Tasks
- 进入
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.DriverEndpoint.scala
// Launch tasks returned by a set of resource offers
private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
for (task <- tasks.flatten) {
val serializedTask = TaskDescription.encode(task)
if (serializedTask.limit() >= maxRpcMessageSize) {
Option(scheduler.taskIdToTaskSetManager.get(task.taskId)).foreach { taskSetMgr =>
try {
var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
"spark.rpc.message.maxSize (%d bytes). Consider increasing " +
"spark.rpc.message.maxSize or using broadcast variables for large values."
msg = msg.format(task.taskId, task.index, serializedTask.limit(), maxRpcMessageSize)
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
logDebug(s"Launching task ${task.taskId} on executor id: ${task.executorId} hostname: " +
s"${executorData.executorHost}.")
executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
}
}
}
- 遍历所有 TaskDescription，针对每个 TaskDescription，调用 encode 方法将其序列化为 ByteBuffer；
- 如果序列化后的 ByteBuffer 小于
spark.rpc.message.maxSize配置的大小，则从 CoarseGrainedSchedulerBackend.executorDataMap 中取出与此 TaskDescription.executorId 对应的 ExecutorData（在Spark源码：启动Executors 中，注册Executor到Driver时将ExecutorData 放到了CoarseGrainedSchedulerBackend.executorDataMap中）； - 调用 ExecutorEndpointRef 发送 LaunchTask 消息，即将 LaunchTask 消息发送到 Executor，用于启动 Task；
- 遍历完所有 TaskDescription，则 TaskSet 提交完毕。
- 进入
org.apache.spark.executor.CoarseGrainedExecutorBackend.scala
override def receive: PartialFunction[Any, Unit] = {
case LaunchTask(data) =>
if (executor == null) {
exitExecutor(1, "Received LaunchTask command but executor was null")
} else {
val taskDesc = TaskDescription.decode(data.value)
logInfo("Got assigned task " + taskDesc.taskId)
executor.launchTask(this, taskDesc)
}
}
调用 Executor.launchTask 方法运行任务（后面文章分析）。
3. 总结
- 将提交上来的 TaskSet 放入到调度器的队列中（默认是 FIFOSchedulableBuilder.schedulableQueue）;
- 从 CoarseGrainedSchedulerBackend.executorDataMap 中选出状态为 Alive 的 Executors（Spark源码：启动Executors 时，当Executor启动完成注册到Driver时，会将Executor加入到 CoarseGrainedSchedulerBackend.executorDataMap 中）；
- 将所有 Executor 分别封装成 WorkerOffer；
- 随机打散所有的 WorkerOffers，计算出每个 WorkerOffer 上可运行的 Task 数；
- 从调度器的队列中取出 TaskSet，利用 resourceOfferSingleTaskSet 方法依次在各本地性级别下（PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY）为 TaskSet 中的 Tasks 分配 WorkerOffer 资源，最终返回获得资源的 Tasks；
- 调用 launchTasks 方法分别发送 LaunchTask 消息给 Executors，用于启动 Tasks。