spark3.0Application调度算法解析
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.x 到 现在最新的spark 3.x ，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 canlunch 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，
// 并且当前 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！！！
以上是 spark3.0Application调度算法解析 的全部内容， 来源链接： utcz.com/z/531665.html
