Standalone模式下Spark任务资源分配

Standalone模式下Spark任务资源分配

1.背景

    在spark集群采用spark-submit命令提交任务时，我们可以通过配置资源相关参数来控制任务使用的资源，比如总核数，每个executor中核数与内存等，但是这个任务在spark集群中实际占用资源是没法配置的，需要根据实际环境和参数结合起来考虑，下面是就是对Standalone模式下Spark任务在集群中实际资源分配的分析

    需要说明的是，这里指只针对cpu、executor进行分析，不分析memory的分配，因为实际使用的总memory=executor个数*每个executor中memory，其中第二个参数是指定的，所以只需分析executor即可

2.源码分析

    在这里以spark2.0.2为例进行分析

    任务提交后，driver会向master注册，master收到消息后，会对维护的worker列表进行轮询，并在符合一定条件的worker上申请资源给executor，相关代码如下：

    Master类中，Master获取driver注册application的消息后，会在Mater注册application，然后开始为application分配资源

    

  case RegisterApplication(description, driver) =>
    // TODO Prevent repeated registrations from some driver
    if (state == RecoveryState.STANDBY) {
      // ignore, don't send response
    } else {
      logInfo("Registering app " + description.name)
      val app = createApplication(description, driver)
      registerApplication(app)
      logInfo("Registered app " + description.name + " with ID " + app.id)
      persistenceEngine.addApplication(app)
      driver.send(RegisteredApplication(app.id, self))
      schedule()
    }

   进入到schedule，在startExecutorsOnWorkers中开始在Worker上分配Executor

    

  /**
   * Schedule the currently available resources among waiting apps. This method will be called
   * every time a new app joins or resource availability changes.
   */
  private def schedule(): Unit = {
    if (state != RecoveryState.ALIVE) {
      return
    }
    // Drivers take strict precedence over executors
    val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
    val numWorkersAlive = shuffledAliveWorkers.size
    var curPos = 0
    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 (numWorkersVisited < numWorkersAlive && !launched) {
        val worker = shuffledAliveWorkers(curPos)
        numWorkersVisited += 1
        if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
          launchDriver(worker, driver)
          waitingDrivers -= driver
          launched = true
        }
        curPos = (curPos + 1) % numWorkersAlive
      }
    }
    startExecutorsOnWorkers()
  }

  private def launchExecutor(worke