Spark Streaming资源动态申请和动态控制消费速率原理剖析
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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。
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