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Spark RDD缓存代码分析

  我们知道,Spark相比Hadoop最大的一个优势就是可以将数据cache到内存,以供后面的计算使用。本文将对这部分的代码进行分析。

  我们可以通过rdd.persist()或rdd.cache()来缓存RDD中的数据,cache()其实就是调用persist()实现的。persist()支持下面的几种存储级别:

val NONE = new StorageLevel(false, false, false, false)
val DISK_ONLY = new StorageLevel(true, false, false, false)
val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2)
val MEMORY_ONLY = new StorageLevel(false, true, false, true)
val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2)
val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false)
val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2)
val MEMORY_AND_DISK = new StorageLevel(true, true, false, true)
val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2)
val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false)
val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2)
val OFF_HEAP = new StorageLevel(false, false, true, false)

  而cache()最终调用的是persist(StorageLevel.MEMORY_ONLY),也就是默认的缓存级别。我们可以根据自己的需要去设置不同的缓存级别,这里各种缓存级别的含义我就不介绍了,可以参见官方文档说明。

通过调用rdd.persist()来缓存RDD中的数据,其最终调用的都是下面的代码:

/////////////////////////////////////////////////////////////////////
 User: 过往记忆
 Date: 2015-11-17
 Time: 22:59
 bolg: https://www.iteblog.com
 本文地址:https://www.iteblog.com/archives/1532 
 过往记忆博客,专注于hadoop、hive、spark、shark、flume的技术博客,大量的干货
 过往记忆博客微信公共帐号:iteblog_hadoop
/////////////////////////////////////////////////////////////////////

private def persist(newLevel: StorageLevel, allowOverride: Boolean): this.type = {
  // TODO: Handle changes of StorageLevel
  if (storageLevel != StorageLevel.NONE && newLevel != storageLevel && !allowOverride) {
    throw new UnsupportedOperationException(
      "Cannot change storage level of an RDD after it was already assigned a level")
  }
  // If this is the first time this RDD is marked for persisting, register it
  // with the SparkContext for cleanups and accounting. Do this only once.
  if (storageLevel == StorageLevel.NONE) {
    sc.cleaner.foreach(_.registerRDDForCleanup(this))
    sc.persistRDD(this)
  }
  storageLevel = newLevel
  this
}

  这段代码的最主要作用其实就是将storageLevel设置为persist()函数传进来的存储级别,而且一旦设置好RDD的存储级别之后就不能再对相同RDD设置别的存储级别,否则将会出现异常。设置好存储级别在之后除非触发了action操作,否则不会真正地执行缓存操作。当我们触发了action,它会调用sc.runJob方法来真正的计算,而这个方法最终会调用org.apache.spark.scheduler.Task#run,而这个方法最后会调用ResultTask或者ShuffleMapTask的runTask方法,runTask方法最后会调用org.apache.spark.rdd.RDD#iterator方法,iterator的代码如下:

final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
  if (storageLevel != StorageLevel.NONE) {
    SparkEnv.get.cacheManager.getOrCompute(this, split, context, storageLevel)
  } else {
    computeOrReadCheckpoint(split, context)
  }
}

如果当前RDD设置了存储级别(也就是通过上面的rdd.persist()设置的),那么会从cacheManager中判断是否有缓存数据。如果有,则直接获取,如果没有则计算。getOrCompute的代码如下:

def getOrCompute[T](
    rdd: RDD[T],
    partition: Partition,
    context: TaskContext,
    storageLevel: StorageLevel): Iterator[T] = {

  val key = RDDBlockId(rdd.id, partition.index)
  logDebug(s"Looking for partition $key")
  blockManager.get(key) match {
    case Some(blockResult) =>
      // Partition is already materialized, so just return its values
      val existingMetrics = context.taskMetrics
        .getInputMetricsForReadMethod(blockResult.readMethod)
      existingMetrics.incBytesRead(blockResult.bytes)

      val iter = blockResult.data.asInstanceOf[Iterator[T]]
      new InterruptibleIterator[T](context, iter) {
        override def next(): T = {
          existingMetrics.incRecordsRead(1)
          delegate.next()
        }
      }
    case None =>
      // Acquire a lock for loading this partition
      // If another thread already holds the lock, wait for it to finish return its results
      val storedValues = acquireLockForPartition[T](key)
      if (storedValues.isDefined) {
        return new InterruptibleIterator[T](context, storedValues.get)
      }

      // Otherwise, we have to load the partition ourselves
      try {
        logInfo(s"Partition $key not found, computing it")
        val computedValues = rdd.computeOrReadCheckpoint(partition, context)

        // If the task is running locally, do not persist the result
        if (context.isRunningLocally) {
          return computedValues
        }

        // Otherwise, cache the values and keep track of any updates in block statuses
        val updatedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
        val cachedValues = putInBlockManager(key, computedValues, storageLevel, updatedBlocks)
        val metrics = context.taskMetrics
        val lastUpdatedBlocks = metrics.updatedBlocks.getOrElse(Seq[(BlockId, BlockStatus)]())
        metrics.updatedBlocks = Some(lastUpdatedBlocks ++ updatedBlocks.toSeq)
        new InterruptibleIterator(context, cachedValues)

      } finally {
        loading.synchronized {
          loading.remove(key)
          loading.notifyAll()
        }
      }
  }
}

  首先通过RDD的ID和当前计算的分区ID构成一个key,并向blockManager中查找是否存在相关的block信息。如果能够获取得到,说明当前分区已经被缓存了;否者需要重新计算。如果重新计算,我们需要获取到相关的锁,因为可能有多个线程对请求同一分区的数据。如果获取到相关的锁,则会调用rdd.computeOrReadCheckpoint(partition, context)计算当前分区的数据,并放计算完的数据放到BlockManager中,如果有相关的线程等待该分区的计算,那么在计算完数据之后还得通知它们(loading.notifyAll())。

如果获取锁失败,则说明已经有其他线程在计算该分区中的数据了,那么我们就得等(loading.wait()),获取锁的代码如下:

/////////////////////////////////////////////////////////////////////
 User: 过往记忆
 Date: 2015-11-17
 Time: 22:59
 bolg: https://www.iteblog.com
 本文地址:https://www.iteblog.com/archives/1532 
 过往记忆博客,专注于hadoop、hive、spark、shark、flume的技术博客,大量的干货
 过往记忆博客微信公共帐号:iteblog_hadoop
/////////////////////////////////////////////////////////////////////

private def acquireLockForPartition[T](id: RDDBlockId): Option[Iterator[T]] = {
  loading.synchronized {
    if (!loading.contains(id)) {
      // If the partition is free, acquire its lock to compute its value
      loading.add(id)
      None
    } else {
      // Otherwise, wait for another thread to finish and return its result
      logInfo(s"Another thread is loading $id, waiting for it to finish...")
      while (loading.contains(id)) {
        try {
          loading.wait()
        } catch {
          case e: Exception =>
            logWarning(s"Exception while waiting for another thread to load $id", e)
        }
      }
      logInfo(s"Finished waiting for $id")
      val values = blockManager.get(id)
      if (!values.isDefined) {
        /* The block is not guaranteed to exist even after the other thread has finished.
         * For instance, the block could be evicted after it was put, but before our get.
         * In this case, we still need to load the partition ourselves. */
        logInfo(s"Whoever was loading $id failed; we'll try it ourselves")
        loading.add(id)
      }
      values.map(_.data.asInstanceOf[Iterator[T]])
    }
  }
}

  等待的线程(也就是没有获取到锁的线程)是通过获取到锁的线程调用loading.notifyAll()唤醒的,唤醒之后之后调用new InterruptibleIterator[T](context, storedValues.get)获取已经缓存的数据。以后后续RDD需要这个RDD的数据我们就可以直接在缓存中获取了,而不需要再计算了。后面我会对checkpoint相关代码进行分析。

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