Spark源代码阅读（一）
强烈推荐
https://blog.csdn.net/weixin_41705780/article/details/79273666
总体架构
Spark工程下的模块
- spark core, spark 内核
- spark streaming, spark流计算(基于batch方式)
- spark sql
- MLlib, 机器学习lib库
- GraphX, 图计算
- R, 与R语言结合
- Python，与Python语言结合，PySpark。基于PY4J
- external 与Kakfa，Flume等模块的整合
- resource-managers 对Yarn mesos等的支持
- 其他（文档，实例，实例数据，工具，启动脚本，打包，授权协议）
本系列文章将只涉及spark core的2.3.2版本。
Spark的单机运行
如果不能单机运行，我们对其原理理解将只能从阅读源代码获取。没有办法进行DEBUG。这对我们深入的理解和重构源代码都是非常不利的。
SparkContext
runJob和
class SparkContext(config: SparkConf) extends Logging {
/**
* Run a function on a given set of partitions in an RDD and pass the results to the given
* handler function. This is the main entry point for all actions in Spark.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like `first()`
* @param resultHandler callback to pass each result to
*/
def runJob[T, U: ClassTag](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
resultHandler: (Int, U) => Unit): Unit = {
if (stopped.get()) {
throw new IllegalStateException("SparkContext has been shutdown")
}
val callSite = getCallSite
val cleanedFunc = clean(func)
logInfo("Starting job: " + callSite.shortForm)
if (conf.getBoolean("spark.logLineage", false)) {
logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
}
dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
progressBar.foreach(_.finishAll())
rdd.doCheckpoint()
}
/**
* Submit a job for execution and return a FutureJob holding the result.
*
* @param rdd target RDD to run tasks on
* @param processPartition a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like `first()`
* @param resultHandler callback to pass each result to
* @param resultFunc function to be executed when the result is ready
*/
def submitJob[T, U, R](
rdd: RDD[T],
processPartition: Iterator[T] => U,
partitions: Seq[Int],
resultHandler: (Int, U) => Unit,
resultFunc: => R): SimpleFutureAction[R] =
{
assertNotStopped()
val cleanF = clean(processPartition)
val callSite = getCallSite
val waiter = dagScheduler.submitJob(
rdd,
(context: TaskContext, iter: Iterator[T]) => cleanF(iter),
partitions,
callSite,
resultHandler,
localProperties.get)
new SimpleFutureAction(waiter, resultFunc)
}
DAGScheduler
在这些RDD.Action操作中（如count,collect）会自动触发runJob提交作业，不需要用户显式的提交作业。
作业调度的两个主要入口是submitJob和runJob，两者的区别在于前者返回一个Jobwaiter对象，可以用在异步调用中，用来判断作业完成或者取消作业，runJob在内部调用submitJob，等待waiter对象完成，保持阻塞直到作业完成（或失败）。
/**
* Run an action job on the given RDD and pass all the results to the resultHandler function as
* they arrive.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like first()
* @param callSite where in the user program this job was called
* @param resultHandler callback to pass each result to
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*
* @note Throws `Exception` when the job fails
*/
def runJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): Unit = {
val start = System.nanoTime
val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
ThreadUtils.awaitReady(waiter.completionFuture, Duration.Inf)
waiter.completionFuture.value.get match {
case scala.util.Success(_) =>
logInfo("Job %d finished: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
case scala.util.Failure(exception) =>
logInfo("Job %d failed: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
// SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
val callerStackTrace = Thread.currentThread().getStackTrace.tail
exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
throw exception
}
}
/**
* Submit an action job to the scheduler.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like first()
* @param callSite where in the user program this job was called
* @param resultHandler callback to pass each result to
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*
* @return a JobWaiter object that can be used to block until the job finishes executing
* or can be used to cancel the job.
*
* @throws IllegalArgumentException when partitions ids are illegal
*/
def submitJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): JobWaiter[U] = {
// Check to make sure we are not launching a task on a partition that does not exist.
val maxPartitions = rdd.partitions.length
partitions.find(p => p >= maxPartitions || p < 0).foreach { p =>
throw new IllegalArgumentException(
"Attempting to access a non-existent partition: " + p + ". " +
"Total number of partitions: " + maxPartitions)
}
val jobId = nextJobId.getAndIncrement()
if (partitions.size == 0) {
// Return immediately if the job is running 0 tasks
return new JobWaiter[U](this, jobId, 0, resultHandler)
}
assert(partitions.size > 0)
val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
//实际上是将JOB put进一个LinkedBlockingDeque。另一个线程负责将JOB取出。另一个线程将执行下面的方法
eventProcessLoop.post(JobSubmitted(
jobId, rdd, func2, partitions.toArray, callSite, waiter,
SerializationUtils.clone(properties)))
waiter
}
//接收的上面JOB的事件
private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
case StageCancelled(stageId, reason) =>
dagScheduler.handleStageCancellation(stageId, reason)
case JobCancelled(jobId, reason) =>
dagScheduler.handleJobCancellation(jobId, reason)
case JobGroupCancelled(groupId) =>
dagScheduler.handleJobGroupCancelled(groupId)
case AllJobsCancelled =>
dagScheduler.doCancelAllJobs()
case ExecutorAdded(execId, host) =>
dagScheduler.handleExecutorAdded(execId, host)
case ExecutorLost(execId, reason) =>
val workerLost = reason match {
case SlaveLost(_, true) => true
case _ => false
}
dagScheduler.handleExecutorLost(execId, workerLost)
case WorkerRemoved(workerId, host, message) =>
dagScheduler.handleWorkerRemoved(workerId, host, message)
case BeginEvent(task, taskInfo) =>
dagScheduler.handleBeginEvent(task, taskInfo)
case SpeculativeTaskSubmitted(task) =>
dagScheduler.handleSpeculativeTaskSubmitted(task)
case GettingResultEvent(taskInfo) =>
dagScheduler.handleGetTaskResult(taskInfo)
case completion: CompletionEvent =>
dagScheduler.handleTaskCompletion(completion)
case TaskSetFailed(taskSet, reason, exception) =>
dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
case ResubmitFailedStages =>
dagScheduler.resubmitFailedStages()
}
LiveListenerBus
在SparkContext中, 首先会创建LiveListenerBus实例,这个类主要功能如下:
- 保存有消息队列,负责消息的缓存
- 保存有注册过的listener,负责消息的分发
这是一个简单的监听器模型。
TaskScheduler
TaskScheduler是抽象类，目前Spark仅提供了TaskSchedulerImpl一种实现；其初始化是在SparkContext中
private[spark] class TaskSchedulerImpl(
val sc: SparkContext,
val maxTaskFailures: Int,
isLocal: Boolean = false)
extends TaskScheduler with Logging
TaskScheduler实际是SchedulerBackend(比如一种实现为YarnClientSchedulerBackend)的代理，本身处理一些通用逻辑，如不同Job间的调度顺序，将运行缓慢的task在空闲节点上重新提交(speculation)等
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])
stageTaskSets(taskSet.stageAttemptId) = manager
val conflictingTaskSet = stageTaskSets.exists { case (_, ts) =>
ts.taskSet != taskSet && !ts.isZombie
}
if (conflictingTaskSet) {
throw new IllegalStateException(s"more than one active taskSet for stage $stage:" +
s" ${stageTaskSets.toSeq.map{_._2.taskSet.id}.mkString(",")}")
}
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()
}
RDD
abstract class RDD[T: ClassTag](
@transient private var _sc: SparkContext,
@transient private var deps: Seq[Dependency[_]]
) extends Serializable with Logging {
def map[U: ClassTag](f: T => U): RDD[U] = withScope {
val cleanF = sc.clean(f)
new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF))
}
def reduce(f: (T, T) => T): T = withScope {
val cleanF = sc.clean(f)
val reducePartition: Iterator[T] => Option[T] = iter => {
if (iter.hasNext) {
Some(iter.reduceLeft(cleanF))
} else {
None
}
}
var jobResult: Option[T] = None
val mergeResult = (index: Int, taskResult: Option[T]) => {
if (taskResult.isDefined) {
jobResult = jobResult match {
case Some(value) => Some(f(value, taskResult.get))
case None => taskResult
}
}
}
sc.runJob(this, reducePartition, mergeResult)
// Get the final result out of our Option, or throw an exception if the RDD was empty
jobResult.getOrElse(throw new UnsupportedOperationException("empty collection"))
}
RDD的checkpoint机制
/**
* Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint
* directory set with `SparkContext#setCheckpointDir` and all references to its parent
* RDDs will be removed. This function must be called before any job has been
* executed on this RDD. It is strongly recommended that this RDD is persisted in
* memory, otherwise saving it on a file will require recomputation.
*/
def checkpoint(): Unit = RDDCheckpointData.synchronized {
// NOTE: we use a global lock here due to complexities downstream with ensuring
// children RDD partitions point to the correct parent partitions. In the future
// we should revisit this consideration.
if (context.checkpointDir.isEmpty) {
throw new SparkException("Checkpoint directory has not been set in the SparkContext")
} else if (checkpointData.isEmpty) {
checkpointData = Some(new ReliableRDDCheckpointData(this))
}
}
checkpoint 的使用方式如下
val data = sc.textFile("/tmp/spark/1.data").cache() // 注意要cache
sc.setCheckpointDir("/tmp/spark/checkpoint")
data.checkpoint
data.count
使用很简单， 就是设置一下 checkpoint 目录，然后再rdd上调用 checkpoint 方法， action 的时候就对数据进行了 checkpoint当checkpoint为当前RDD设置检查点的时候，该函数将会创建一个二进制的文件，并存储到checkpoint目录中，该目录是用SparkContext.setCheckpointDir()设置的。在checkpoint的过程中，该RDD的所有依赖于父RDD中的信息将全部被移出。对RDD进行checkpoint操作并不会马上被执行，必须执行Action操作才能触发。当需要checkpoint的数据的时候，通过ReliableCheckpointRDD的readCheckpointFile方法来从file路径里面读出已经Checkpoint的数据。
Executor
- 在Worker Actor中，每次LaunchExecutor会创建一个CoarseGrainedExecutorBackend进程，一个Executor对应一个CoarseGrainedExecutorBackend
- 针对同一个App，每个Worker里只能有一个针对该App的Executor存在，切记。如果想让整个App的Executor变多，设置SPARK_WORKER_INSTANCES，让每个节点（机器上）的Worker变多。APP之间不公用Executor。
- Executor的资源分配有2种策略：
- SpreadOut ：一种以round-robin方式遍历集群所有可用Worker，分配Worker资源，来启动创建Executor的策略，好处是尽可能的将cores分配到各个节点，最大化负载均衡和高并行。
- 非SpreadOut：会尽可能的根据每个Worker的剩余资源来启动Executor，这样启动的Executor可能只在集群的一小部分机器的Worker上。这样做对node较少的集群还可以，集群规模大了，Executor的并行度和机器负载均衡就不能够保证了。
Streaming
https://blog.csdn.net/define_us/article/details/83109337
JavaStreamingContext
http://spark.apache.org/docs/0.8.1/api/streaming/org/apache/spark/streaming/api/java/JavaStreamingContext.html
定义上下文后，您必须执行以下操作：
- 通过创建输入DStreams定义输入源
- 通过对DStreams应用转换操作（transformation）和输出操作（output）来定义流计算
- 可以使用streamingContext.start()方法接收和处理数据
- 可以使用streamingContext.awaitTermination()方法等待流计算完成（手动或由于任何错误），来防止应用退出
- 可以使用streamingContext.stop（）手动停止处理。
class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
/**
* Start the execution of the streams.
*/
def start(): Unit = {
ssc.start()
}