在Worker Actor中，每次LaunchExecutor会创建一个CoarseGrainedExecutorBackend进程，Executor和CoarseGrainedExecutorBackend是1对1的关系。也就是说集群里启动多少Executor实例就有多少CoarseGrainedExecutorBackend进程。
那么到底是如何分配Executor的呢？怎么控制调节Executor的个数呢？
下面主要介绍一下Spark Executor分配策略：
我们仅看，当Application提交注册到Master后，Master会返回RegisteredApplication，之后便会调用schedule（）这个方法，来分配Driver的资源，和启动Executor的资源。
schedule()方法是来调度当前可用资源的调度方法，它管理还在排队等待的Apps资源的分配，这个方法是每次在集群资源发生变动的时候都会调用，根据当前集群最新的资源来进行Apps的资源分配。
// First schedule drivers, they take strict precedence over applications
val shuffledWorkers = Random.shuffle(workers) // 把当前workers这个HashSet的顺序随机打乱
for (worker <- shuffledWorkers if worker.state == WorkerState.ALIVE) { //遍历活着的workers
for (driver <- waitingDrivers) { //在等待队列中的Driver们会进行资源分配
if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) { //当前的worker内存和cpu均大于当前driver请求的mem和cpu，则启动
launchDriver(worker, driver) //启动Driver 内部实现是发送启动Driver命令给指定Worker，Worker来启动Driver。
waitingDrivers -= driver //把启动过的Driver从队列移除
}
}
}
val spreadOutApps = conf.getBoolean("spark.deploy.spreadOut", true) /**
* Can an app use the given worker? True if the worker has enough memory and we haven't already
* launched an executor for the app on it (right now the standalone backend doesn't like having
* two executors on the same worker).
*/
def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = {
worker.memoryFree >= app.desc.memoryPerSlave && !worker.hasExecutor(app)
}
// 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.
if (spreadOutApps) {
// Try to spread out each app among all the nodes, until it has all its cores
for (app <- waitingApps if app.coresLeft > 0) { //对还未被完全分配资源的apps处理
val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
.filter(canUse(app, _)).sortBy(_.coresFree).reverse //根据core Free对可用Worker进行降序排序。
val numUsable = usableWorkers.length //可用worker的个数 eg:可用5个worker
val assigned = new Array[Int](numUsable) //候选Worker，每个Worker一个下标，是一个数组，初始化默认都是0
var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)//还要分配的cores = 集群中可用Worker的可用cores总和（10）， 当前未分配core（5）中找最小的
var pos = 0
while (toAssign > 0) {
if (usableWorkers(pos).coresFree - assigned(pos) > 0) { //以round robin方式在所有可用Worker里判断当前worker空闲cpu是否大于当前数组已经分配core值
toAssign -= 1
assigned(pos) += 1 //当前下标pos的Worker分配1个core +1
}
pos = (pos + 1) % numUsable //round-robin轮询寻找有资源的Worker
}
// Now that we've decided how many cores to give on each node, let's actually give them
for (pos <- 0 until numUsable) {
if (assigned(pos) > 0) { //如果assigned数组中的值>0，将启动一个executor在，指定下标的机器上。
val exec = app.addExecutor(usableWorkers(pos), assigned(pos)) //更新app里的Executor信息
launchExecutor(usableWorkers(pos), exec) //通知可用Worker去启动Executor
app.state = ApplicationState.RUNNING
}
}
}
} else {
，跑个题，有些困了。。 } else {
// Pack each app into as few nodes as possible until we've assigned all its cores
for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
for (app <- waitingApps if app.coresLeft > 0) {
if (canUse(app, worker)) { //直接问当前worker是有空闲的core
val coresToUse = math.min(worker.coresFree, app.coresLeft) //有则取，不管多少
if (coresToUse > 0) { //有
val exec = app.addExecutor(worker, coresToUse) //直接启动
launchExecutor(worker, exec)
app.state = ApplicationState.RUNNING
}
}
}
}
}
}2、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Worker变多。
3、Executor的资源分配有2种策略：
3.1、SpreadOut ：一种轮询集群各个Worker，为Executor比较平均的分配Worker资源，来启动创建Executor的策略，好处是负载均衡，坏处是会造成启动等待。
3.2、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的core是不均匀的。好处是加快了App的Executor启动，坏处，每个Executor的并行度和负载均衡就不能够保证了。
行文仓促，如有不正之处，请指出，欢迎讨论 ：）
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原创文章，转载请注明出自：http://blog.csdn.net/oopsoom/article/details/38763985
原文地址：http://blog.csdn.net/oopsoom/article/details/38763985
