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：

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 Context 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) {    Env.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：

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。

本文链接: 【Spark RDD缓存代码分析】（https://www.iteblog.com/archives/1532）