Standalone 的整体架构
在Spark集群中的3个角色Client, Master, Worker, 下面的图是Client Submit 一个任务的流程图：
完整的流程：Driver 提交任务给Master, 由Master节点根据任务的参数对进行Worker的Executor的分配，Worker节点获取到具体的分配信息启动executor 的子进程
Master分配Executor的策略
Master 接收到从Client发送的RegiterApplication 的消息后，开始进行worker资源的分配和调度
1. 寻找有效的Worker
val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE).filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB &&worker.coresFree >= coresPerExecutor.getOrElse(1)).sortBy(_.coresFree).reverse在worker列表中，寻找有效的worker
A. 剩余内存大于单个Executor需要的内存
B. 剩余的内核数大于单个Executor所需的内核数
在Worker的分配中剩余的内核最多的（最空闲）的Worker，优先分配Executor
2. 分配Executor
Executor 和核数的关系? 可以简单的理解为进程和线程的关系，所以在分配一个新Executor的时候不仅要考虑核数同时还需要考虑内存是否足够。
几个控制参数
a. 每个executor的核数
当没有设置executor的核数
- 默认认为每个executor的核数是1
- 一个Worker上只能分配一个Executor（在这种情况下，一个Executor可以启动多个cores直到Worker的最大能分配的核数）
b. 每个Executor的需要的内存数
d. Application 所需要的内核数(total-executor-cores)
每个运行的Application所设置的最大内核数，如果没有设置，取默认的内核数
如何判断能在Worker上分配Executor?
def canLaunchExecutor(pos: Int): Boolean = {val keepScheduling = coresToAssign >= minCoresPerExecutorval enoughCores = usableWorkers(pos).coresFree - assignedCores(pos) >= minCoresPerExecutor// If we allow multiple executors per worker, then we can always launch new executors.// Otherwise, if there is already an executor on this worker, just give it more cores.val launchingNewExecutor = !oneExecutorPerWorker || assignedExecutors(pos) == 0if (launchingNewExecutor) {val assignedMemory = assignedExecutors(pos) * memoryPerExecutorval enoughMemory = usableWorkers(pos).memoryFree - assignedMemory >= memoryPerExecutorval underLimit = assignedExecutors.sum + app.executors.size < app.executorLimitkeepScheduling && enoughCores && enoughMemory && underLimit} else {// We're adding cores to an existing executor, so no need// to check memory and executor limitskeepScheduling && enoughCores}}- Worker 上剩余的核数大于一个Executor的核数
- Worker 上如果允许创建新的Executor，需要检查Worker上的内存是否足够Executor，和创建的Executor的总数否超过App对Executor的大小限制
EX: 设置
executor-cores=5
如何在Worker上均衡分配Executor
在Spark上通过轮训的在所有有效的Worker列表（在前面1里已经谈过如何创建空闲的worker列表）里创建Executor，每次轮训的在每个Worker上分配一个executor的核数（一个executor），直到分配完这个应用所需要的所有核数。
Master.scala
private def scheduleExecutorsOnWorkers(app: ApplicationInfo,usableWorkers: Array[WorkerInfo],spreadOutApps: Boolean): Array[Int] = {val coresPerExecutor = app.desc.coresPerExecutorval minCoresPerExecutor = coresPerExecutor.getOrElse(1)val oneExecutorPerWorker = coresPerExecutor.isEmptyval memoryPerExecutor = app.desc.memoryPerExecutorMBval numUsable = usableWorkers.lengthval assignedCores = new Array[Int](numUsable) // Number of cores to give to each workerval assignedExecutors = new Array[Int](numUsable) // Number of new executors on each workervar coresToAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
。。。。。。。。。// Keep launching executors until no more workers can accommodate any// more executors, or if we have reached this application's limitsvar freeWorkers = (0 until numUsable).filter(canLaunchExecutor)while (freeWorkers.nonEmpty) {freeWorkers.foreach { pos =>var keepScheduling = truewhile (keepScheduling && canLaunchExecutor(pos)) {coresToAssign -= minCoresPerExecutorassignedCores(pos) += minCoresPerExecutor// If we are launching one executor per worker, then every iteration assigns 1 core// to the executor. Otherwise, every iteration assigns cores to a new executor.if (oneExecutorPerWorker) {assignedExecutors(pos) = 1} else {assignedExecutors(pos) += 1}// Spreading out an application means spreading out its executors across as// many workers as possible. If we are not spreading out, then we should keep// scheduling executors on this worker until we use all of its resources.// Otherwise, just move on to the next worker.if (spreadOutApps) {keepScheduling = false}}}freeWorkers = freeWorkers.filter(canLaunchExecutor)}assignedCores}assignedCores是每个workers的被分配的核数的列表，为何不是分配的Executor数目呢？
还记得前面的参数每个Executor的核数的配置么？如果没有配置，就是默认为每个Worker只起一个Executor, 如果此时返回的是Executor的数目列表的话，在这种情况下只能返回{1,1...}的集合，根本无法知道每个Worker的分配的核数。
但反过来却很容易知道每个Work要创建的Executor的数目，只要 cores.sum/coresPerExecutor 就可以了
3. Worker上申请资源
private def allocateWorkerResourceToExecutors(app: ApplicationInfo,assignedCores: Int,coresPerExecutor: Option[Int],worker: WorkerInfo): Unit = {// If the number of cores per executor is specified, we divide the cores assigned// to this worker evenly among the executors with no remainder.// Otherwise, we launch a single executor that grabs all the assignedCores on this worker.val numExecutors = coresPerExecutor.map { assignedCores / _ }.getOrElse(1)val coresToAssign = coresPerExecutor.getOrElse(assignedCores)for (i <- 1 to numExecutors) {val exec = app.addExecutor(worker, coresToAssign)launchExecutor(worker, exec)app.state = ApplicationState.RUNNING}} private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc): Unit = {logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)worker.addExecutor(exec)worker.endpoint.send(LaunchExecutor(masterUrl,exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory))exec.application.driver.send(ExecutorAdded(exec.id, worker.id, worker.hostPort, exec.cores, exec.memory))} case ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) =>val fullId = appId + "/" + idlogInfo("Executor added: %s on %s (%s) with %d cores".format(fullId, workerId, hostPort,cores))listener.executorAdded(fullId, workerId, hostPort, cores, memory)在listener处理的函数里，只是简单的记录了日志
override def executorAdded(fullId: String, workerId: String, hostPort: String, cores: Int,memory: Int) {logInfo("Granted executor ID %s on hostPort %s with %d cores, %s RAM".format(fullId, hostPort, cores, Utils.megabytesToString(memory)))}4. 资源申请管理
虽然我们都在谈论Executor，但实际上核心数才是关键，而Worker的资源也是由core和内存来决定是否能够在上申请成功，如果Worker上的空闲核心数不够申请一个Executor的核心数时候，这个Worker会被忽略。在Master上会有一个数组统计依然存活的Application
private val waitingApps = new ArrayBuffer[ApplicationInfo]在启动ExecutorsOnWorkers函数里
private def startExecutorsOnWorkers(): Unit = {// Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app// in the queue, then the second app, etc.for (app <- waitingApps if app.coresLeft > 0) {
.....
}
}如果没有完全分配完core的application（比如application设置了总共需要的cores，但实际上资源不够只分配了一部分Cores），都会继续再次优先被分配资源，因为在waitingApps的队列的前面，后续的Application资源分配遵循FIFO的策略，等待前面的Application分配资源结束，才能获取到分配资源的权利。
注意：
这里并不是代表没分配完Core的Application就不开始运行了，Application的最小单位是Executor, 在前面的代码里也看到在分配的时候，只要Worker能被分配出Executor，就会对Worker发送LaunchExecutor 消息，并不需要等完整的分配完下面的参数
total-executor-cores=10在Spark理念中当资源不足的时候，先分配给Application一部分的Executor，让任务运行了在说，后续如果有Worker资源被释放,继续对该Application从worker中申请Executor，直到资源申请完，或者该Application运行完，而所有的Worker的状态、资源的状况，均保存在Master里，由Master来全局调度分配。
设置过大的Total-Executor-Cores会带来很大的风险
Master会不停的持续的分配Worker资源直到最大的Core的数目为止
后续：
Worker收到LaunchExecutor的消息后，会启动Executor的子进程，Executor会发消息RegisterExecutor给Application，通知Application所分配的Executor启动了
Executor和Core？
在Worker启动Executor子进程的时候，并没有启动对应的Core数目的线程
private def fetchAndRunExecutor() {
}那么Core数对Executor进程的意义何在？
在Spark中在Application的任务中，同时还有task，task的任务数据的拆分是基于参数和Executor汇报的Core数量，而Task的数量会决定最后在Executor中启动的运行Task的线程数，关于Task后续会讲到。