spark 各个版本的application 调度算法还是有这明显的不同之处的。从spark1.3.0 到 spark 1.6.1、spark2.0 到 现在最新的spark 3.0 ，调度算法有了一定的修改。下面大家一起学习一下，最新的spark 版本spark-3.0的Application 调度机制。

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) { //如果在 spark-submmit 脚本中，指定了每个executor 多少个 CPU core， // 则每个Executor 分配该个数的 core， // 否则 默认每个executor 只分配 1 个 CPU core val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(1) // If the cores left is less than the coresPerExecutor,the cores left will not be allocated // 当前 APP 还需要分配的 core 数 不能 小于 单个 executor 启动 的 CPU core 数 if (app.coresLeft >= coresPerExecutor) {
// Filter out workers that don't have enough resources to launch an executo/*ku*/r
// 过滤出 状态 为 ALIVE，并且还能 发布 Executor 的 worker
// 按照剩余的 CPU core 数 倒序
val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
.filter(canLaunchExecutor(_, app.desc))
.sortBy(_.coresFree).reverse
if (waitingApps.length == 1 && usableWorkers.isEmpty) {
logWarning(s"App ${app.id} requires more resource than any of Workers could have.")
}

// TODO:  默认采用 spreadOutApps  调度算法， 将 application需要的 executor资源 分派到  多个 worker 上去

  val assignedCores = scheduleExecutorsOnWorkers(app, usableWorkers, spreadOutApps)

  // Now that we've decided how many cores to allocate on each worker, let's allocate them  
  for (pos <- 0 until usableWorkers.length if assignedCores(pos) > 0) {  
    allocateWorkerResourceToExecutors(  
      app, assignedCores(pos), app.desc.coresPerExecutor, usableWorkers(pos))  
  }  
}  

}
}
判断一个 worker 是否可以发布 executor

private def canLaunchExecutor(worker: WorkerInfo, desc: ApplicationDescription): Boolean = {
canLaunch(
worker,
desc.memoryPerExecutorMB,
desc.coresPerExecutor.getOrElse(1),
desc.resourceReqsPerExecutor)
}
让我们看一看里面的 canlaunch 方法

private def canLaunch(
worker: WorkerInfo,
memoryReq: Int,
coresReq: Int,
resourceRequirements: Seq[ResourceRequirement])
: Boolean = {
// worker 上 空闲的 内存值 要 大于等于 请求的 内存值
val enoughMem = worker.memoryFree >= memoryReq
// worker 上 空闲的 core 数 要 大于等于 请求的 core数
val enoughCores = worker.coresFree >= coresReq
// worker 是否满足 executor 请求的资源
val enoughResources = ResourceUtils.resourcesMeetRequirements(
worker.resourcesAmountFree, resourceRequirements)
enoughMem && enoughCores && enoughResources
}

回到上面的 scheduleExecutorsOnWorkers

private def scheduleExecutorsOnWorkers(
app: ApplicationInfo,
usableWorkers: Array[WorkerInfo],
spreadOutApps: Boolean): Array[Int] = {
val coresPerExecutor = app.desc.coresPerExecutor
val minCoresPerExecutor = coresPerExecutor.getOrElse(1)
// 默认情况下 是 开启 oneExecutorPerWorker 机制的，也就是默认是在 一个 worker 上 只启动 一个 executor的
// 如果在spark -submit 脚本中设置了coresPerExecutor ， 在worker资源充足的时候，则 会在每个worker 上，启动多个executor
val oneExecutorPerWorker = coresPerExecutor.isEmpty
val memoryPerExecutor = app.desc.memoryPerExecutorMB
val resourceReqsPerExecutor = app.desc.resourceReqsPerExecutor
val numUsable = usableWorkers.length
val assignedCores = new Array[Int](numUsable) // Number of cores to give to each worker
val assignedExecutors = new Array[Int](numUsable) // Number of new executors on each worker
var coresToAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)

// 判断 Worker节点是否能够启动Executor
def canLaunchExecutorForApp(pos: Int): Boolean = {

val keepScheduling = coresToAssign >= minCoresPerExecutor  
val enoughCores = usableWorkers(pos).coresFree - assignedCores(pos) >= minCoresPerExecutor  
val assignedExecutorNum = assignedExecutors(pos)

// 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.

// 如果spark -submit 脚本中设置了coresPerExecutor值，  
// 或者当前 这个worker 还没有为这个 application 分配 过  executor ,  
val launchingNewExecutor = !oneExecutorPerWorker || assignedExecutorNum == 0  
  // TODO:  可以启动新的 Executor  
if (launchingNewExecutor) {  
  val assignedMemory = assignedExecutorNum \* memoryPerExecutor  
  val enoughMemory = usableWorkers(pos).memoryFree - assignedMemory >= memoryPerExecutor  
  val assignedResources = resourceReqsPerExecutor.map {  
    req => req.resourceName -> req.amount \* assignedExecutorNum  
  }.toMap  
  val resourcesFree = usableWorkers(pos).resourcesAmountFree.map {  
    case (rName, free) => rName -> (free - assignedResources.getOrElse(rName, 0))  
  }  
  val enoughResources = ResourceUtils.resourcesMeetRequirements(  
    resourcesFree, resourceReqsPerExecutor)  
  val underLimit = assignedExecutors.sum + app.executors.size < app.executorLimit  
  keepScheduling && enoughCores && enoughMemory && enoughResources && underLimit  
} else {  
  // We're adding cores to an existing executor, so no need  
  // to check memory and executor limits  
  // TODO:  不满足启动新的 Executor条件，则 在 老的 Executor 上 追加  core 数  
  keepScheduling && enoughCores  
}  

}

// Keep launching executors until no more workers can accommodate any
// more executors, or if we have reached this application's limits

var freeWorkers = (0 until numUsable).filter(canLaunchExecutorForApp)
while (freeWorkers.nonEmpty) {
freeWorkers.foreach { pos =>
var keepScheduling = true
while (keepScheduling && canLaunchExecutorForApp(pos)) {
coresToAssign -= minCoresPerExecutor
assignedCores(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) {  
      //TODO: 如果该Worker节点不能启动新的 Executor，则每次在老的executor 上 分配 minCoresPerExecutor 个 CPU core(此时该值默认 为 1 )  
      assignedExecutors(pos) = 1  
    } else {  
      //TODO: 如果该Worker节点可以启动新的 Executor，则每次在新的executor 上 分配 minCoresPerExecutor 个 CPU core（此时该值为 spark-submit脚本配置的 coresPerExecutor 值）  
      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) {  
      // TODO： 这里传入 keepScheduling = false , 就是每次 worker上只分配 一次 core ,然后 到 下一个 worker 上  再去 分配 core，直到 worker  
      // TODO:  完成一次遍历  
      keepScheduling = false  
    }  
  }  
}  
freeWorkers = freeWorkers.filter(canLaunchExecutorForApp)  

}
// 返回每个Worker节点分配的CPU核数
assignedCores
}

再来分析 allocateWorkerResourceToExecutors

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 allocated = worker.acquireResources(app.desc.resourceReqsPerExecutor)
// TODO : 当前 这个 application 追加 一次 Executor
val exec = app.addExecutor(worker, coresToAssign, allocated)
//TODO： 给worker 线程 发送 launchExecutor 命令
launchExecutor(worker, exec)
app.state = ApplicationState.RUNNING
}
}
ok，至此，spark最新版本 spark-3.0的Application 调度算法分析完毕！！！