一、Task原理
1、图解
二、源码分析
1、
###org.apache.spark.executor/Executor.scala /**
* 从TaskRunner开始，来看Task的运行的工作原理
*/
class TaskRunner(
execBackend: ExecutorBackend,
val taskId: Long,
val attemptNumber: Int,
taskName: String,
serializedTask: ByteBuffer)
extends Runnable { @volatile private var killed = false
@volatile var task: Task[Any] = _
@volatile var attemptedTask: Option[Task[Any]] = None
@volatile var startGCTime: Long = _ def kill(interruptThread: Boolean) {
logInfo(s"Executor is trying to kill $taskName (TID $taskId)")
killed = true
if (task != null) {
task.kill(interruptThread)
}
} override def run() {
val deserializeStartTime = System.currentTimeMillis()
Thread.currentThread.setContextClassLoader(replClassLoader)
val ser = env.closureSerializer.newInstance()
logInfo(s"Running $taskName (TID $taskId)")
execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
var taskStart: Long = 0
startGCTime = gcTime try {
// 对序列化的task数据，进行反序列化
val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask)
// 然后，通过网络通信，将需要的文件、资源、jar拷贝过来
updateDependencies(taskFiles, taskJars)
// 最后，通过正式的反序列化操作，将整个task的数据集反序列化回来
// 这里用到了java的ClassLoader，因为java的ClassLoader可以干很多事情，比如，用反射的方式来动态加载一个类，创建这个类的对象，
// 还有比如，可以用于对指定上下文的相关资源，进行加载和读取
task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) // If this task has been killed before we deserialized it, let's quit now. Otherwise,
// continue executing the task.
if (killed) {
// Throw an exception rather than returning, because returning within a try{} block
// causes a NonLocalReturnControl exception to be thrown. The NonLocalReturnControl
// exception will be caught by the catch block, leading to an incorrect ExceptionFailure
// for the task.
throw new TaskKilledException
} attemptedTask = Some(task)
logDebug("Task " + taskId + "'s epoch is " + task.epoch)
env.mapOutputTracker.updateEpoch(task.epoch) // Run the actual task and measure its runtime.
// 计算出task开始的时间
taskStart = System.currentTimeMillis()
// 执行task，用的是task的run()方法
// 这里的value，对于ShuffleMapTask来说，其实就是MapStatus,封装了ShuffleMapTask计算的数据，输出的位置
// 后面还是一个ShuffleMapTask，那么就会去联系MapOutputTracker，来获取上一个ShuffleMapTasks的输出位置，然后通过网络拉取数据
// ResultTask，也是一样的
val value = task.run(taskAttemptId = taskId, attemptNumber = attemptNumber)
// 计算出task结束的时间
val taskFinish = System.currentTimeMillis() // If the task has been killed, let's fail it.
if (task.killed) {
throw new TaskKilledException
}
// 这个，其实就是针对MapStatus进行了各种序列化和封装，因为后面要发送给Driver(通过网络)
//
val resultSer = env.serializer.newInstance()
val beforeSerialization = System.currentTimeMillis()
val valueBytes = resultSer.serialize(value)
val afterSerialization = System.currentTimeMillis() // 计算出task相关的一些metrics，就是统计信息，包括运行了多长时间、反序列化消耗了多长时间、java虚拟机gc耗费了多长时间
// 结果的序列化耗费了多长时间，这些东西，其实会在我们的SparkUI上显示
for (m <- task.metrics) {
m.setExecutorDeserializeTime(taskStart - deserializeStartTime)
m.setExecutorRunTime(taskFinish - taskStart)
m.setJvmGCTime(gcTime - startGCTime)
m.setResultSerializationTime(afterSerialization - beforeSerialization)
} val accumUpdates = Accumulators.values val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.orNull)
val serializedDirectResult = ser.serialize(directResult)
val resultSize = serializedDirectResult.limit // directSend = sending directly back to the driver
val serializedResult = {
if (maxResultSize > 0 && resultSize > maxResultSize) {
logWarning(s"Finished $taskName (TID $taskId). Result is larger than maxResultSize " +
s"(${Utils.bytesToString(resultSize)} > ${Utils.bytesToString(maxResultSize)}), " +
s"dropping it.")
ser.serialize(new IndirectTaskResult[Any](TaskResultBlockId(taskId), resultSize))
} else if (resultSize >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
val blockId = TaskResultBlockId(taskId)
env.blockManager.putBytes(
blockId, serializedDirectResult, StorageLevel.MEMORY_AND_DISK_SER)
logInfo(
s"Finished $taskName (TID $taskId). $resultSize bytes result sent via BlockManager)")
ser.serialize(new IndirectTaskResult[Any](blockId, resultSize))
} else {
logInfo(s"Finished $taskName (TID $taskId). $resultSize bytes result sent to driver")
serializedDirectResult
}
} // 其实就是调用了Executor所在的CoarseGrainedExecutorBackend的statusUpdate()方法
execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult) }
} ###org.apache.spark.executor/Executor.scala private def updateDependencies(newFiles: HashMap[String, Long], newJars: HashMap[String, Long]) {
// 获取hadoop配置文件
lazy val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
// 这里，使用java的synchronized进行了多线程并发访问的同步
// 因为task实际上是以java线程的方式，在一个CoarseGrainedExecutorBackend进程内并发运行的
// 如果在执行业务逻辑的时候，要访问一些共享的资源，那么就可能会出现多线程并发访问安全问题
// 所以，spark在这里选择进行了多线程并发访问的同步(synchronized)，因为在这里面访问了诸如currentFiles等等这些共享资源 synchronized {
// Fetch missing dependencies
// 遍历要拉取的文件
// 通过Utils的fetchFile()方法，通过网络通信，从远程拉取文件
for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) {
logInfo("Fetching " + name + " with timestamp " + timestamp)
// Fetch file with useCache mode, close cache for local mode.
Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf,
env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
currentFiles(name) = timestamp
}
// 遍历要拉取的jar
for ((name, timestamp) <- newJars) {
val localName = name.split("/").last
// 判断一下时间戳，要求jar当前时间戳必须小于目标时间戳
// 通过Utils的fetchFile()，拉取jar文件
val currentTimeStamp = currentJars.get(name)
.orElse(currentJars.get(localName))
.getOrElse(-1L)
if (currentTimeStamp < timestamp) {
logInfo("Fetching " + name + " with timestamp " + timestamp)
// Fetch file with useCache mode, close cache for local mode.
Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf,
env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
currentJars(name) = timestamp
// Add it to our class loader
val url = new File(SparkFiles.getRootDirectory, localName).toURI.toURL
if (!urlClassLoader.getURLs.contains(url)) {
logInfo("Adding " + url + " to class loader")
urlClassLoader.addURL(url)
}
}
}
}
} ###org.apache.spark.scheduler/Task.scala final def run(taskAttemptId: Long, attemptNumber: Int): T = {
// 创建一个TaskContext，就是task的执行上下文，里面记录了task执行的一些全局性的数据，比如task重试了几次
// 比如task属于哪个stage，task要处理的是rdd的哪个partition等等
context = new TaskContextImpl(stageId = stageId, partitionId = partitionId,
taskAttemptId = taskAttemptId, attemptNumber = attemptNumber, runningLocally = false)
TaskContextHelper.setTaskContext(context)
context.taskMetrics.setHostname(Utils.localHostName())
taskThread = Thread.currentThread()
if (_killed) {
kill(interruptThread = false)
}
try {
// 调用抽象方法，runTask()
runTask(context)
} finally {
context.markTaskCompleted()
TaskContextHelper.unset()
}
} ###org.apache.spark.scheduler/Task.scala // 调用到了抽象方法，那就意味着这个类，只是一个模板类，或者抽象父类，仅仅封装了一些子类通用的数据和操作
// 而关键的操作，全部都要依赖于子类的实现，task的子类，有ShuffleMapTask、ResultTask
// 要运行子类的runTask()方法，才能执行我们自己定义的算子和逻辑
def runTask(context: TaskContext): T
2、
接下来分别看下ShuffleMapTask和ResultTask的runTask()方法：
一个ShuffleMapTask会将一个RDD的元素，切分为多个bucket，基于一个在ShuffleDependency中指定的partitioner，默认就是HashPartition； ###org.apache.spark.scheduler/ShuffleMapTask.scala /**
* ShuffleMapTask的runTask()方法有MapStatus返回值
*/
override def runTask(context: TaskContext): MapStatus = {
// Deserialize the RDD using the broadcast variable.
// 对task要处理的rdd相关的数据，做一些反序列化操作
// 这里有一个问题，如何拿到这个要处理的RDD
// 多个task运行在多个Executor中，都是并行运行，或者并发运行的，可能都不在一个地方，但是一个stage的task，其实要处理的rdd是一样，所以task如何拿到自己要处理的rdd数据？
// 这里会通过broadcast variable 直接拿到
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader) metrics = Some(context.taskMetrics)
var writer: ShuffleWriter[Any, Any] = null
try {
// 获取ShuffleManager
val manager = SparkEnv.get.shuffleManager
// 从ShuffleManager中获取ShuffleWriter
writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
// 首先调用了，rdd的iterator()方法，并且传入了，当前task要处理哪个partition
// 所以核心的逻辑，就在rdd的iterator()方法中，在这里，实现了针对rdd的某个partition，执行我们自己定义的算子，或者是函数
// 执行完了我们自己定义的算子、或者函数，就相当于是，针对rdd的partition执行了处理，会有返回的数据
// 返回的数据，都是通过ShuffleWriter，经过HashPartitioner进行分区之后，写入自己对应的分区bucket
writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
// 最后，返回结果MapStatus，MapStatus里面封装了ShuffleMapTask计算后的数据，数据存储在哪里，其实就是BlockManager的相关信息
// BlockManager是Spark底层的内存，数据，磁盘数据管理的组件
return writer.stop(success = true).get
} catch {
case e: Exception =>
try {
if (writer != null) {
writer.stop(success = false)
}
} catch {
case e: Exception =>
log.debug("Could not stop writer", e)
}
throw e
}
} ###org.apache.spark.rdd/RDD.scalal final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
if (storageLevel != StorageLevel.NONE) {
// cacheManager相关东西
SparkEnv.get.cacheManager.getOrCompute(this, split, context, storageLevel)
} else {
// 进行rdd partition的计算
computeOrReadCheckpoint(split, context)
}
} ###org.apache.spark.rdd/MapPartitionsRDD.scala // 这里，就是针对rdd中的某个partition执行我们给这个rdd定义的算子和函数
// 这里的f，可以理解为我们自己定义的算子和函数，但是是Spark内部进行了封装的，还实现了一些其他的逻辑
// 执行到了这里，就是在针对RDD的partition，执行自定义的计算操作，并返回新的rdd的partition数据
override def compute(split: Partition, context: TaskContext) =
f(context, split.index, firstParent[T].iterator(split, context)) ###org.apache.spark.scheduler/ResultTask.scala override def runTask(context: TaskContext): U = {
// Deserialize the RDD and the func using the broadcast variables.
// 进行了基本的反序列化
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
metrics = Some(context.taskMetrics)
// 执行通过rdd的iterator，执行我们定义的算子和函数
func(context, rdd.iterator(partition, context))
} ###org.apache.spark.executor/Executor.scala // 其实就是调用了Executor所在的CoarseGrainedExecutorBackend的statusUpdate()方法 execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult) ###org.apache.spark.executor/CoarseGrainedExecutorBackend.scalal override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
// 向CoarseGrainedSchedulerBackend发送一个StatusUpdate消息
driver ! StatusUpdate(executorId, taskId, state, data)
} ###org.apache.spark.scheduler.cluster/CoarseGrainedSchedulerBackend.scalla // 处理task执行结束的事件
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) " +
"from unknown executor $sender with ID $executorId")
}
} ###org.apache.spark.scheduler/TaskSchedulerlmpl.scala def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) {
var failedExecutor: Option[String] = None
synchronized {
try {
// 判断如果task是lost了，实际上，可能会经常发现task lost了，这就是因为各种各样的原因，执行失败了
if (state == TaskState.LOST && taskIdToExecutorId.contains(tid)) {
// We lost this entire executor, so remember that it's gone
// 移除Executor，将它加入失败队列
val execId = taskIdToExecutorId(tid)
if (activeExecutorIds.contains(execId)) {
removeExecutor(execId)
failedExecutor = Some(execId)
}
}
// 获取对应的taskSet
taskIdToTaskSetId.get(tid) match {
case Some(taskSetId) =>
// 如果task结束了，从内存缓存中移除
if (TaskState.isFinished(state)) {
taskIdToTaskSetId.remove(tid)
taskIdToExecutorId.remove(tid)
}
// 如果正常结束，也做相应的处理
activeTaskSets.get(taskSetId).foreach { taskSet =>
if (state == TaskState.FINISHED) {
taskSet.removeRunningTask(tid)
taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData)
} else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) {
taskSet.removeRunningTask(tid)
taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData)
}
}
case None =>
logError(
("Ignoring update with state %s for TID %s because its task set is gone (this is " +
"likely the result of receiving duplicate task finished status updates)")
.format(state, tid))
}
} catch {
case e: Exception => logError("Exception in statusUpdate", e)
}
}
// Update the DAGScheduler without holding a lock on this, since that can deadlock
if (failedExecutor.isDefined) {
dagScheduler.executorLost(failedExecutor.get)
backend.reviveOffers()
}
}