Spark源码分析--资源调度机制

 

 

/*
*schedule()解决了spark资源调度的问题
*/
rivate def schedule() {
 //首先判断，master状态不是ALIVE的话，直接返回
 //也就是说，stanby master是不会进行application等资源调度的
 if (state != RecoveryState.ALIVE) { return }

 // First schedule drivers, they take strict precedence over applications
 // Randomization helps balance drivers

 //Random.shuffle的原理，大家要清楚，就是对传入的集合的元素进行随机的打乱
 //取出了workers中的所有之前注册上来的worker,进行过滤，必须是状态为ALIVE的worker
 //对状态为ALIVE的worker，调用Random的shuffle方法进行随机的打乱
 val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
 val numWorkersAlive = shuffledAliveWorkers.size
 var curPos = 0

 //首先，调度driver
 //为什么要调度driver,大家想一下，什么情况下，会注册driver,并且会导致driver被调度
 //其实 ，只有用yarn-cluster模式提交的时候，才会注册driver;因为standalone和yarn-client模式，都会在本地直接
 //启动driver，而不会来注册driver，就更不可能让master调度driver了

 //driver调度机制
 //遍历waittingDrivers ArrayBuffer
 for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers
   // We assign workers to each waiting driver in a round-robin fashion. For each driver, we
   // start from the last worker that was assigned a driver, and continue onwards until we have
   // explored all alive workers.
   var launched = false
   var numWorkersVisited = 0

   //while的条件，numWorkersVisited小于numWorkersAlive
   //什么意思？就是说，只要还有活着的worker没有遍历到，那么就继续进行遍历
   //而且，当前这个driver还没有被启动，也就是launched为false
   while (numWorkersVisited < numWorkersAlive && !launched) {
     val worker = shuffledAliveWorkers(curPos)
     numWorkersVisited += 1

     //如果当前这个worker的空闲内存量大于等于，driver需要的内存
     //并且worker的空闲cpu数量，大于等于driver需要的cpu数量
     if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
       //启动driver
       launchDriver(worker, driver)
       //并且将driver从waitingDrivers队列中移除
       waitingDrivers -= driver
       launched = true
     }

     //将指针指向下一个worker
     curPos = (curPos + 1) % numWorkersAlive
   }
 }

 // 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.
 // Application的调度机制（核心之核心，重中之重）
 // 首先， application的调度算法有两种，一种是spreadOutApps，另一种是非spreadOutApps
 if (spreadOutApps) {
   // Try to spread out each app among all the nodes, until it has all its cores

   //首先，遍历waitingApps中的ApplicationInfo,并且过滤出application还需要高度的cores的application
   for (app <- waitingApps if app.coresLeft > 0) {
     //从workers中，过滤状态为ALIVE的，再次过滤可以被Application使用的Worker，然后按照剩余cpu数量倒序排序
     val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
       .filter(canUse(app, _)).sortBy(_.coresFree).reverse
     val numUsable = usableWorkers.length
     //创建一个空数组，存储了要分配给每个worker的cpu数量
     val assigned = new Array[Int](numUsable) // Number of cores to give on each node
     //获取到底要分配多少cpu，取app剩余要分配的cpu的数量和worker总共可用cpu数量的最小值
     var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)

     //通过这种算法，其实会将每个application，要启动的executor,都平均分布到各个worker上去
     //比如有20个cpu core要分配，那么实际会循环两遍worker，每次循环，给每个worker分配1个core
     //最后每个worker分配了2个core

     //while条件，只要要分配的cpu，还没有分配完，就继续循环
     var pos = 0
     while (toAssign > 0) {
       //每一个worker，如果空闲的cpu数量大于，已经分配出去的cpu数量
       //也就是说，worker还有可分配的cpu
       if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
         //将总共要分配的cpu数量-1，因为这里已经决定在这个worker上分配一个cpu了
         toAssign -= 1
         //给这个worker分配的cpu数量，加1
         assigned(pos) += 1
       }
       //指针移动到下一下worker
       pos = (pos + 1) % numUsable
     }

     // Now that we've decided how many cores to give on each node, let's actually give them
     // 给每个worker分配完application要求的cpu core之后
     // 遍历worker
     for (pos <- 0 until numUsable) {
       //只要判断之前给这个worker分配到了core
       if (assigned(pos) > 0) {
         //首先，在application内部缓存结构中，添加executor
         //并且创建ExecutorDesc对象，其中封装了，给这个executor分配多少个cpu core
         //在spark-submit脚本中，可以指定要多少executor，每个execuor多少个cpu,多少内存
         //那么基于源码机制，实际上，executor的实际数量，以及每个executor的cpu，可能与配置是不一样的
         //因为，我人帝里基于总的cpu来分配的，就是比如，要求3个executor,每个要3个cpu,那么比如，有9个workers,每个有1个cpu
         //那么其实总其知道，要分配9个core，其实根据这种算法，会给每个worker分配一个core,然后给每个worker启动一个executor
         //最后会启动，9个executor,每个executor有1个cpu core
         val exec = app.addExecutor(usableWorkers(pos), assigned(pos))
         //那么就在worker上启动executor
         launchExecutor(usableWorkers(pos), exec)
         //将application状态设置为running
         app.state = ApplicationState.RUNNING
       }
     }
   }
 } else {
   // Pack each app into as few nodes as possible until we've assigned all its cores

   //非spreadOutApps调度算法

   //这种算法与spreadOutApps算法正好相反,1、spreadOutApp尽量平均分配到每个executor上；2、非spreadOutApp尽量在使用单个executor的资源。
   //每个application，都尽可能分配到尽量少的worker上去，比如总其有10个worker,每个有10个core
   //app总共要分配 20个core,那么其实，只会分配到两个worker上，每个worker都占满10个core
   //那么，其余的app,就只能 分配到下一个worker了
   //比如，spark-submit里，配置的是要10个executor,每个要2个core,那么总共是20个croe
   //只会启动2个executor,每个有10个cores

   //将每个Application，尽可能少的分配到worker上去
   //首先，遍历worker，并且是状态为ALIVE，还有空闲cpu的worker
   for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
     //遍历application,并且是还有城朵分配的core的application
     for (app <- waitingApps if app.coresLeft > 0) {
       //判断，如果当前这个worker可以被 application使用
       if (canUse(app, worker)) {
         //取worker剩余cpu数量，与app要分配的cpu数量的最小值
         val coresToUse = math.min(worker.coresFree, app.coresLeft)
         //如果Worker剩余cpu为0了，就不分配了
         if (coresToUse > 0) {
           // 给app添加一个executor
           val exec = app.addExecutor(worker, coresToUse)
           //在worker上启动executor
           launchExecutor(worker, exec)
           //将application状态设置为running
           app.state = ApplicationState.RUNNING
         }
       }
     }
   }
 }