深入理解spark－两种调度模式FIFO，FAIR模式

Posted 2021-01-12 yankang

 

 

前面我们应知道了一个任务提交会由DAG拆分为job，stage，task，最后提交给TaskScheduler，在提交taskscheduler中会根据master初始化taskscheduler和schedulerbackend两个类，并且初始化一个调度池；

1.调度池比较

 根据mode初始化调度池pool

def initialize(backend: SchedulerBackend) {
    this.backend = backend
    // temporarily set rootPool name to empty 这里可以看到调度池初始化最小设置为0
    rootPool = new Pool("", schedulingMode, 0, 0)
    schedulableBuilder = {
      schedulingMode match {
        case SchedulingMode.FIFO =>
          new FIFOSchedulableBuilder(rootPool)
        case SchedulingMode.FAIR =>
          new FairSchedulableBuilder(rootPool, conf)
      }
    }
    schedulableBuilder.buildPools()
  }

 

FIFO模式

这个会根据spark.scheduler.mode 来设置FIFO or FAIR，默认的是FIFO模式；

FIFO模式什么都不做，实现默认的schedulerableBUilder方法，建立的调度池也为空，addTasksetmaneger也是调用默认的；

可以简单的理解为，默认模式FIFO什么也不做。。

 

FAIR模式

fair模式则重写了buildpools的方法，读取默认路径 $SPARK_HOME/conf/fairscheduler.xml文件，也可以通过参数spark.scheduler.allocation.file设置用户自定义配置文件。

文件中配置的是

poolname 线程池名

schedulermode 调度模式（FIFO，FAIR仅有两种）

minshare 初始大小的线程核数

wight 调度池的权重

 

override def buildPools() {
    var is: Option[InputStream] = None
    try {
      is = Option {
        schedulerAllocFile.map { f =>
          new FileInputStream(f)
        }.getOrElse {
          Utils.getSparkClassLoader.getResourceAsStream(DEFAULT_SCHEDULER_FILE)
        }
      }

      is.foreach { i => buildFairSchedulerPool(i) }
    } finally {
      is.foreach(_.close())
    }

    // finally create "default" pool
    buildDefaultPool()
  }

 

同时也重写了addtaskmanager方法

override def addTaskSetManager(manager: Schedulable, properties: Properties) {
    var poolName = DEFAULT_POOL_NAME
    var parentPool = rootPool.getSchedulableByName(poolName)
    if (properties != null) {
      poolName = properties.getProperty(FAIR_SCHEDULER_PROPERTIES, DEFAULT_POOL_NAME)
      parentPool = rootPool.getSchedulableByName(poolName)
      if (parentPool == null) {
        // we will create a new pool that user has configured in app
        // instead of being defined in xml file
        parentPool = new Pool(poolName, DEFAULT_SCHEDULING_MODE,
          DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT)
        rootPool.addSchedulable(parentPool)
        logInfo("Created pool %s, schedulingMode: %s, minShare: %d, weight: %d".format(
          poolName, DEFAULT_SCHEDULING_MODE, DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT))
      }
    }
    parentPool.addSchedulable(manager)
    logInfo("Added task set " + manager.name + " tasks to pool " + poolName)
  }

这一段逻辑中是把配置文件中的pool，或者default pool放入rootPool中，然后把TaskSetManager存入rootPool对应的子pool；

 

2.调度算法比较

除了初始化的调度池不一致外，其实现的调度算法也不一致

实现的调度池Pool，在内部实现方法中也会根据mode不一致来实现调度的不同

var taskSetSchedulingAlgorithm: SchedulingAlgorithm = {
    schedulingMode match {
      case SchedulingMode.FAIR =>
        new FairSchedulingAlgorithm()
      case SchedulingMode.FIFO =>
        new FIFOSchedulingAlgorithm()
    }
  }

 

FIFO模式

FIFO模式的调度方式很容易理解，比较stageID，谁小谁先执行；

这也很好理解，stageID小的任务一般来说是递归的最底层，是最先提交给调度池的；

private[spark] class FIFOSchedulingAlgorithm extends SchedulingAlgorithm {
  override def comparator(s1: Schedulable, s2: Schedulable): Boolean = {
    val priority1 = s1.priority
    val priority2 = s2.priority
    var res = math.signum(priority1 - priority2)
    if (res == 0) {
      val stageId1 = s1.stageId
      val stageId2 = s2.stageId
      res = math.signum(stageId1 - stageId2)
    }
    if (res < 0) {
      true
    } else {
      false
    }
  }
}

 

FAIR模式

fair模式来说的话，稍微复杂一点；

但是还是比较容易看懂，

1.先比较两个stage的 runningtask使用的核数，其实也可以理解为task的数量，谁小谁的优先级高；

2.比较两个stage的 runningtask 权重，谁的权重大谁先执行；

3.如果前面都一直，则比较名字了（字符串比较），谁大谁先执行；

private[spark] class FairSchedulingAlgorithm extends SchedulingAlgorithm {
  override def comparator(s1: Schedulable, s2: Schedulable): Boolean = {
    val minShare1 = s1.minShare
    val minShare2 = s2.minShare
    val runningTasks1 = s1.runningTasks
    val runningTasks2 = s2.runningTasks
    val s1Needy = runningTasks1 < minShare1
    val s2Needy = runningTasks2 < minShare2
    val minShareRatio1 = runningTasks1.toDouble / math.max(minShare1, 1.0).toDouble
    val minShareRatio2 = runningTasks2.toDouble / math.max(minShare2, 1.0).toDouble
    val taskToWeightRatio1 = runningTasks1.toDouble / s1.weight.toDouble
    val taskToWeightRatio2 = runningTasks2.toDouble / s2.weight.toDouble
    var compare: Int = 0

    if (s1Needy && !s2Needy) {
      return true
    } else if (!s1Needy && s2Needy) {
      return false
    } else if (s1Needy && s2Needy) {
      compare = minShareRatio1.compareTo(minShareRatio2)
    } else {
      compare = taskToWeightRatio1.compareTo(taskToWeightRatio2)
    }

    if (compare < 0) {
      true
    } else if (compare > 0) {
      false
    } else {
      s1.name < s2.name
    }
  }

 

 

总结：虽然了解一下spark的调度模式，以前在执行中基本都没啥用到，没想到spark还有这样的隐藏功能。。。