Spark Streaming资源动态申请和动态控制消费速率原理剖析

为什么需要动态？
a) Spark默认情况下粗粒度的，先分配好资源再计算。对于Spark Streaming而言有高峰值和低峰值，但是他们需要的资源是不一样的，如果按照高峰值的角度的话，就会有大量的资源浪费。
b) Spark Streaming不断的运行，对资源消耗和管理也是我们要考虑的因素。
Spark Streaming资源动态调整的时候会面临挑战：
Spark Streaming是按照Batch Duration运行的，Batch Duration需要很多资源，下一次Batch Duration就不需要那么多资源了，调整资源的时候还没调整完Batch Duration运行就已经过期了。这个时候调整时间间隔。

Spark Streaming资源动态申请
1. 在SparkContext中默认是不开启动态资源分配的，但是可以通过手动在SparkConf中配置。

// Optionally scale number of executors dynamically based on workload. Exposed for testing.val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)if (!dynamicAllocationEnabled && //参数配置是否开启资源动态分配_conf.getBoolean("spark.dynamicAllocation.enabled", false)) {  logWarning("Dynamic Allocation and num executors both set, thus dynamic allocation disabled.")}_executorAllocationManager =  if (dynamicAllocationEnabled) {    Some(new ExecutorAllocationManager(this, listenerBus, _conf))  } else {    None  }_executorAllocationManager.foreach(_.start())

2.  ExecutorAllocationManager: 有定时器会不断的去扫描Executor的情况，正在运行的Stage，要运行在不同的Executor中，要么增加Executor或者减少。3.  ExecutorAllocationManager中schedule方法会被周期性触发进行资源动态调整。

/** * This is called at a fixed interval to regulate the number of pending executor requests * and number of executors running. * * First, adjust our requested executors based on the add time and our current needs. * Then, if the remove time for an existing executor has expired, kill the executor. * * This is factored out into its own method for testing. */private def schedule(): Unit = synchronized {  val now = clock.getTimeMillis  updateAndSyncNumExecutorsTarget(now)  removeTimes.retain { case (executorId, expireTime) =>    val expired = now >= expireTime    if (expired) {      initializing = false      removeExecutor(executorId)    }    !expired  }}

4.  在ExecutorAllocationManager中会在线程池中定时器会不断的运行schedule.

/** * Register for scheduler callbacks to decide when to add and remove executors, and start * the scheduling task. */def start(): Unit = {  listenerBus.addListener(listener)  val scheduleTask = new Runnable() {    override def run(): Unit = {      try {        schedule()      } catch {        case ct: ControlThrowable =>          throw ct        case t: Throwable =>          logWarning(s"Uncaught exception in thread ${Thread.currentThread().getName}", t)      }    }  }// intervalMillis定时器触发时间  executor.scheduleAtFixedRate(scheduleTask, 0, intervalMillis, TimeUnit.MILLISECONDS)}

动态控制消费速率：
Spark Streaming提供了一种弹性机制，流进来的速度和处理速度的关系，是否来得及处理数据。如果不能来得及的话，他会自动动态控制数据流进来的速度，spark.streaming.backpressure.enabled参数设置。