本篇结构：
- CoarseGrainedExecutorBackend 接收 LaunchTask 消息
- Executor 执行 launchTask
- 执行 Task 的 run 方法
一、CoarseGrainedExecutorBackend 接收 LaunchTask 消息
CoarseGrainedSchedulerBackend 中向 Executor 发送 LaunchTask 消息，CoarseGrainedExecutorBackend 接收该消息：
case LaunchTask(data) =>
if (executor == null) {
exitExecutor(1, "Received LaunchTask command but executor was null")
} else {
val taskDesc = ser.deserialize[TaskDescription](data.value)
logInfo("Got assigned task " + taskDesc.taskId)
executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
taskDesc.name, taskDesc.serializedTask)
}
二、Executor 执行 launchTask
CoarseGrainedExecutorBackend 接收到消息后，先对 TaskDescription 进行反序列化，然后 调用 executor.launchTask：
def launchTask(
context: ExecutorBackend,
taskId: Long,
attemptNumber: Int,
taskName: String,
serializedTask: ByteBuffer): Unit = {
val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName,
serializedTask)
runningTasks.put(taskId, tr)
threadPool.execute(tr)
}
在 Executor 的 launchTask 方法中，会构建一个 TaskRunner 来封装任务，再把 TaskRunner 放到线程池去执行。
在 TaskRunner 的 run 方法中，要对发送过来的 task 及其依赖的 jar 等文件进行反序列化，然后对反序列化后的 Task 调用 run 方法：
override def run(): Unit = {
val threadMXBean = ManagementFactory.getThreadMXBean
// 生成内存管理 TaskMemoryManager 实例，用于管理运行期间内存
val taskMemoryManager = new TaskMemoryManager(env.memoryManager, taskId)
val deserializeStartTime = System.currentTimeMillis()
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
Thread.currentThread.setContextClassLoader(replClassLoader)
val ser = env.closureSerializer.newInstance()
logInfo(s"Running $taskName (TID $taskId)")
// 向 Driver 终端发送任务开始的消息
execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
var taskStart: Long = 0
var taskStartCpu: Long = 0
startGCTime = computeTotalGcTime()
try {
// 对任务开始需要的 文件、jar包、代码进行反序列化
val (taskFiles, taskJars, taskProps, taskBytes) =
Task.deserializeWithDependencies(serializedTask)
// Must be set before updateDependencies() is called, in case fetching dependencies
// requires access to properties contained within (e.g. for access control).
Executor.taskDeserializationProps.set(taskProps)
updateDependencies(taskFiles, taskJars)
task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader)
task.localProperties = taskProps
task.setTaskMemoryManager(taskMemoryManager)
// 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
}
logDebug("Task " + taskId + "'s epoch is " + task.epoch)
env.mapOutputTracker.updateEpoch(task.epoch)
// Run the actual task and measure its runtime.
taskStart = System.currentTimeMillis()
taskStartCpu = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
var threwException = true
// 调用 task 的 run 方法，task 是个抽象类，在其 run 模板方法中会调用子类的 runTask 方法
val value = try {
val res = task.run(
taskAttemptId = taskId,
attemptNumber = attemptNumber,
metricsSystem = env.metricsSystem)
threwException = false
res
} finally {
...
}
}
三、执行 Task 的 run 方法
Task 的模板方法 run：
/**
* Called by [[org.apache.spark.executor.Executor]] to run this task.
*
* @param taskAttemptId an identifier for this task attempt that is unique within a SparkContext.
* @param attemptNumber how many times this task has been attempted (0 for the first attempt)
* @return the result of the task along with updates of Accumulators.
*/
final def run(
taskAttemptId: Long,
attemptNumber: Int,
metricsSystem: MetricsSystem): T = {
SparkEnv.get.blockManager.registerTask(taskAttemptId)
context = new TaskContextImpl(
stageId,
partitionId,
taskAttemptId,
attemptNumber,
taskMemoryManager,
localProperties,
metricsSystem,
metrics)
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 {
runTask(context)
} catch {
case e: Throwable =>
// Catch all errors; run task failure callbacks, and rethrow the exception.
try {
context.markTaskFailed(e)
} catch {
case t: Throwable =>
e.addSuppressed(t)
}
throw e
} finally {
// Call the task completion callbacks.
context.markTaskCompleted()
try {
Utils.tryLogNonFatalError {
// Release memory used by this thread for unrolling blocks
SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(MemoryMode.ON_HEAP)
SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask(MemoryMode.OFF_HEAP)
// Notify any tasks waiting for execution memory to be freed to wake up and try to
// acquire memory again. This makes impossible the scenario where a task sleeps forever
// because there are no other tasks left to notify it. Since this is safe to do but may
// not be strictly necessary, we should revisit whether we can remove this in the future.
val memoryManager = SparkEnv.get.memoryManager
memoryManager.synchronized { memoryManager.notifyAll() }
}
} finally {
TaskContext.unset()
}
}
}
runTask 的实现有两种，一种是 ShuffleMapTask 的实现，一种是 ResultTask。
对于 ShuffleMapTask ，它的计算结果会写到 BlockManage，最终返回给 DAGScheduler 的是一个 MapStatus 对象，该对象中管理了 ShuffleMapTask 的运算结果存储到 BlockManager 里的相关存储信息，而不是计算结果本身，这些信息会成为下一阶段的任务需要获得的输入数据时的依据:
override def runTask(context: TaskContext): MapStatus = {
// Deserialize the RDD using the broadcast variable.
val threadMXBean = ManagementFactory.getThreadMXBean
val deserializeStartTime = System.currentTimeMillis()
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
val ser = SparkEnv.get.closureSerializer.newInstance()
// 反序列化获取 RDD 和 RDD 的依赖
val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime
_executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
} else 0L
var writer: ShuffleWriter[Any, Any] = null
try {
val manager = SparkEnv.get.shuffleManager
writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
// 首先调用 rdd.iterator，如果该 RDD 已经 Cache 或者 Checkpoint，那么直接读取结果，
// 否则计算，计算结果会保存在本地系统的 BlockManager 中
writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
// 关闭 writer, 返回计算结果，返回包含了数据的 location 和 size 等元数据信息的 MapStatus
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
}
}
看看计算的过程：
/**
* Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
* This should ''not'' be called by users directly, but is available for implementors of custom
* subclasses of RDD.
*/
final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
if (storageLevel != StorageLevel.NONE) {
getOrCompute(split, context)
} else {
computeOrReadCheckpoint(split, context)
}
}
->
/**
* Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing.
*/
private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] =
{
if (isCheckpointedAndMaterialized) {
firstParent[T].iterator(split, context)
} else {
compute(split, context)
}
}
compute 的实现有很多，这里以 MapPartitionsRDD 为例：
override def compute(split: Partition, context: TaskContext): Iterator[U] =
f(context, split.index, firstParent[T].iterator(split, context))
再看 ResultTask 的 runTask：
override def runTask(context: TaskContext): U = {
// Deserialize the RDD and the func using the broadcast variables.
val threadMXBean = ManagementFactory.getThreadMXBean
val deserializeStartTime = System.currentTimeMillis()
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime
_executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
} else 0L
func(context, rdd.iterator(partition, context))
}
也是先反序列化 RDD，然后直接返回 func 函数的计算结果。
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