上篇博文中我们讲解了，AppClient和Executor是如何启动，如何为逻辑上与物理上的资源调度，以及分析了在Spark1.4之前逻辑上资源调度算法的bug。
这篇博文，我们就来讲讲Executor启动后，是如何在Executor上执行Task的，以及其后续处理。
执行Task
我们在中提到了，任务调度完成后，CoarseGrainedSchedulerBackend.DriverEndpoint会调用launchTasks向CoarseGrainedExecutorBackend发送带着serializedTask的LaunchTask信号。接下来，我们就来讲讲CoarseGrainedExecutorBackend接收到LaunchTask信号后，是如何执行Task的。
调用栈如下：
CoarseGrainedExecutorBackend.receive
Executor.launchTask
Executor.updateDependencies
Task.run
ShuffleMapTask.runTask
ResultTask.runTask
Executor.TaskRunner.run
CoarseGrainedExecutorBackend.receive
case LaunchTask(data) => if (executor == null) {
exitExecutor(1, "Received LaunchTask command but executor was null")
} else { // 反序列话task描述
val taskDesc = ser.deserialize[TaskDescription](data.value)
logInfo("Got assigned task " + taskDesc.taskId) // 调用executor.launchTask
executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
taskDesc.name, taskDesc.serializedTask)
}Executor.launchTask
def launchTask(
context: ExecutorBackend,
taskId: Long,
attemptNumber: Int,
taskName: String,
serializedTask: ByteBuffer): Unit = { // 创建TaskRunner
val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName,
serializedTask) // 把taskID 以及 对应的 TaskRunner，
// 加入到 ConcurrentHashMap[Long, TaskRunner]
runningTasks.put(taskId, tr) // 线程池 执行 TaskRunner
threadPool.execute(tr)
}Executor.TaskRunner.run
override def run(): Unit = { val threadMXBean = ManagementFactory.getThreadMXBean val taskMemoryManager = new TaskMemoryManager(env.memoryManager, taskId) // 记录开始反序列化的时间
val deserializeStartTime = System.currentTimeMillis() // 记录开始反序列化的时的Cpu时间
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L 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
var taskStartCpu: Long = 0
// 开始GC的时间
startGCTime = computeTotalGcTime() try { //反序列化任务信息
val (taskFiles, taskJars, taskProps, taskBytes) = Task.deserializeWithDependencies(serializedTask) // 根据taskProps设置executor属性
Executor.taskDeserializationProps.set(taskProps) // 根据taskFiles和taskJars，
// 下载任务所需的File 和 加载所需的Jar包
updateDependencies(taskFiles, taskJars) // 根据taskBytes生成task
task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) //设置task属性
task.localProperties = taskProps //设置task内存管理
task.setTaskMemoryManager(taskMemoryManager) // 若在反序列话之前Task就被kill了，
// 抛出异常
if (killed) { throw new TaskKilledException
}
logDebug("Task " + taskId + "'s epoch is " + task.epoch) //更新mapOutputTracker Epoch 为task epoch
env.mapOutputTracker.updateEpoch(task.epoch) // 记录任务开始时间
taskStart = System.currentTimeMillis() // 记录任务开始时的cpu时间
taskStartCpu = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L var threwException = true
val value = try { // 运行Task
val res = task.run(
taskAttemptId = taskId,
attemptNumber = attemptNumber,
metricsSystem = env.metricsSystem)
threwException = false
res
} finally { val releasedLocks = env.blockManager.releaseAllLocksForTask(taskId) val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory() if (freedMemory > 0 && !threwException) { val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId"
if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) { throw new SparkException(errMsg)
} else {
logWarning(errMsg)
}
} if (releasedLocks.nonEmpty && !threwException) { val errMsg = s"${releasedLocks.size} block locks were not released by TID = $taskId:\n" +
releasedLocks.mkString("[", ", ", "]") if (conf.getBoolean("spark.storage.exceptionOnPinLeak", false)) { throw new SparkException(errMsg)
} else {
logWarning(errMsg)
}
}
} // 记录任务结束时间
val taskFinish = System.currentTimeMillis() // 记录任务结束时的cpu时间
val taskFinishCpu = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L // 若task在运行中被kill了
// 则抛出异常
if (task.killed) { throw new TaskKilledException
}
val resultSer = env.serializer.newInstance() // 结果记录序列化开始的系统时间
val beforeSerialization = System.currentTimeMillis() // 序列化结果
val valueBytes = resultSer.serialize(value) // 结果记录序列化完成的系统时间
val afterSerialization = System.currentTimeMillis() // 反序列话发生在两个地方：
// 1. 在该函数下反序列化Task信息以及Task实例。
// 2. 在任务启动后，Task.run 反序列化 RDD 和 函数
// 计算task的反序列化费时
task.metrics.setExecutorDeserializeTime(
(taskStart - deserializeStartTime) + task.executorDeserializeTime) // 计算task的反序列化cpu费时
task.metrics.setExecutorDeserializeCpuTime(
(taskStartCpu - deserializeStartCpuTime) + task.executorDeserializeCpuTime) // 计算task运行费时
task.metrics.setExecutorRunTime((taskFinish - taskStart) - task.executorDeserializeTime) // 计算task运行cpu费时
task.metrics.setExecutorCpuTime(
(taskFinishCpu - taskStartCpu) - task.executorDeserializeCpuTime) // 计算GC时间
task.metrics.setJvmGCTime(computeTotalGcTime() - startGCTime)
//计算结果序列化时间
task.metrics.setResultSerializationTime(afterSerialization - beforeSerialization)
val accumUpdates = task.collectAccumulatorUpdates() // 这里代码存在缺陷：
// value相当于被序列化了两次
val directResult = new DirectTaskResult(valueBytes, accumUpdates) val serializedDirectResult = ser.serialize(directResult) // 得到结果的大小
val resultSize = serializedDirectResult.limit // 对于计算结果，会根据结果的大小有不同的策略：
// 1.生成结果在(正无穷,1GB)：
// 超过1GB的部分结果直接丢弃，
// 可以通过spark.driver.maxResultSize实现
// 默认为1G
// 2.生成结果大小在$[1GB,128MB - 200KB]
// 会把该结果以taskId为编号存入BlockManager中，
// 然后把该编号通过Netty发送给Driver，
// 该阈值是Netty框架传输的最大值
// spark.akka.frameSize（默认为128MB）和Netty的预留空间reservedSizeBytes（200KB）的差值
// 3.生成结果大小在（128MB - 200KB,0）：
// 直接通过Netty发送到Driver
val serializedResult: ByteBuffer = { 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 > maxDirectResultSize) { val blockId = TaskResultBlockId(taskId)
env.blockManager.putBytes(
blockId, new ChunkedByteBuffer(serializedDirectResult.duplicate()), 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
}
} // 更新execBackend 状态
execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult)
} catch { case ffe: FetchFailedException => val reason = ffe.toTaskFailedReason
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) case _: TaskKilledException =>
logInfo(s"Executor killed $taskName (TID $taskId)")
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) case _: InterruptedException if task.killed =>
logInfo(s"Executor interrupted and killed $taskName (TID $taskId)")
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) case CausedBy(cDE: CommitDeniedException) => val reason = cDE.toTaskFailedReason
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) case t: Throwable =>
logError(s"Exception in $taskName (TID $taskId)", t) val accums: Seq[AccumulatorV2[_, _]] = if (task != null) {
task.metrics.setExecutorRunTime(System.currentTimeMillis() - taskStart)
task.metrics.setJvmGCTime(computeTotalGcTime() - startGCTime)
task.collectAccumulatorUpdates(taskFailed = true)
} else { Seq.empty
} val accUpdates = accums.map(acc => acc.toInfo(Some(acc.value), None)) val serializedTaskEndReason = { try {
ser.serialize(new ExceptionFailure(t, accUpdates).withAccums(accums))
} catch { case _: NotSerializableException =>
ser.serialize(new ExceptionFailure(t, accUpdates, false).withAccums(accums))
}
}
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.FAILED, serializedTaskEndReason) if (Utils.isFatalError(t)) { SparkUncaughtExceptionHandler.uncaughtException(t)
}
} finally { // 任务结束后移除
runningTasks.remove(taskId)
}
}Executor.updateDependencies
接下来，我们来看看更新executor的依赖，即下载任务所需的File和加载所需的Jar包：
private def updateDependencies(newFiles: HashMap[String, Long], newJars: HashMap[String, Long]) { lazy val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
synchronized { // 下载任务所需的File
for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) {
logInfo("Fetching " + name + " with timestamp " + timestamp) Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf,
env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
currentFiles(name) = timestamp
} // 加载所需的Jar包
for ((name, timestamp) <- newJars) { val localName = name.split("/").last val currentTimeStamp = currentJars.get(name)
.orElse(currentJars.get(localName))
.getOrElse(-1L) if (currentTimeStamp < timestamp) {
logInfo("Fetching " + name + " with timestamp " + timestamp) Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf,
env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
currentJars(name) = timestamp // 把它加入到 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)
}
}
}
}
}Task.run
接下来，我们来看看这篇博文最核心的部分——task运行：
final def run(
taskAttemptId: Long,
attemptNumber: Int,
metricsSystem: MetricsSystem): T = { SparkEnv.get.blockManager.registerTask(taskAttemptId) //创建TaskContextImpl
context = new TaskContextImpl(
stageId,
partitionId,
taskAttemptId,
attemptNumber,
taskMemoryManager,
localProperties,
metricsSystem,
metrics) //在TaskContext中设置TaskContextImpl
TaskContext.setTaskContext(context)
taskThread = Thread.currentThread() if (_killed) {
kill(interruptThread = false)
} new CallerContext("TASK", appId, appAttemptId, jobId, Option(stageId), Option(stageAttemptId), Option(taskAttemptId), Option(attemptNumber)).setCurrentContext() try { // 调用runTask
runTask(context)
} catch { case e: Throwable => try {
context.markTaskFailed(e)
} catch { case t: Throwable =>
e.addSuppressed(t)
} throw e
} finally { // 标记Task完成
context.markTaskCompleted() try { Utils.tryLogNonFatalError { // 释放内存
SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(MemoryMode.ON_HEAP) SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(MemoryMode.OFF_HEAP) val memoryManager = SparkEnv.get.memoryManager
memoryManager.synchronized { memoryManager.notifyAll() }
}
} finally { //取消TaskContext设置
TaskContext.unset()
}
}
}Task有两个子类，一个是非最后的Stage的Task，ShuffleMapTask；一个是最后的Stage的Task，ResultTask。它们都覆盖了Task的runTask方法，接下来我们就分别来讲下它们的runTask方法。
ShuffleMapTask.runTask
根据每个Stage的partition数量来生成ShuffleMapTask，ShuffleMapTask会根据下游的Partition数量和Shuffle的策略来生成一系列文件。
override def runTask(context: TaskContext): MapStatus = { val threadMXBean = ManagementFactory.getThreadMXBean // 记录反序列化开始时间
val deserializeStartTime = System.currentTimeMillis() // 记录反序列化开始时的Cpu时间
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L val ser = SparkEnv.get.closureSerializer.newInstance() // 反序列化rdd 及其 依赖
val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])]( ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader) // 计算 反序列化费时
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime // 计算 反序列化Cpu费时
_executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
} else 0L var writer: ShuffleWriter[Any, Any] = null
try { //获取shuffleManager
val manager = SparkEnv.get.shuffleManager // writer
writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context) // 调用writer.write 开始计算RDD，
// 这部分 我们会在后续博文讲解
writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]]) // 停止计算，并返回结果
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
}
}ResultTask.runTask
override def runTask(context: TaskContext): U = { val threadMXBean = ManagementFactory.getThreadMXBean // 记录反序列化开始时间
val deserializeStartTime = System.currentTimeMillis() // 记录反序列化开始时的Cpu时间
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L val ser = SparkEnv.get.closureSerializer.newInstance() // 反序列化rdd 及其 作用于RDD的结果函数
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)]( ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader) // 计算 反序列化费时
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime // 计算 反序列化Cpu费时
_executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
} else 0L // 这部分 我们会在后续博文讲解
func(context, rdd.iterator(partition, context))
}后续处理
计量统计
对各个费时的统计，上章已经讲解。
回收内存
这在上章Task.run也已经讲解。
处理执行结果
Executor.TaskRunner.run的execBackend.statusUpdate
作者：小爷Souljoy
链接：https://www.jianshu.com/p/e4c8d4d25d87
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