spark源码阅读笔记RDD（三）RDD的缓存原理

1、RDD的缓存（cache和persist）

spark最重要一个能力就是：在不同的操作中把数据集缓存（cache）或存留（persist）在内存（memory）

中。当持久化一个RDD后，每个节点都会把计算的分片的结果保存在内存中，之后可以对此数据集在其他action中

再次使用。这使得后续的action变得迅速（通常快10x）[1].

2、缓存的级别

源码在：package org.apache.spark.storage这个包，官方给出的各个级别的意思如下：



3、cache和persist关系

RDD缓存函数persist和cache关系如下（附录：源码1）：



从源码发现其实cache就是persist(StorageLevel.MEMORY_ONLY)，也就是说我们选择缓存的时候是把数据缓存在内存中的（memory）。這样加快了数据在再次使用的调用速度。我们再来查看最核心函数：

persist(newLevel: StorageLevel, allowOverride: Boolean)，

StorageLevel：存储的级别，

allowOverride：RDD1转换为RDD2之后RDD2是否重写它之前定义的级别

存储部分如下：

// 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

它的存储是用sparkcontext来进行缓存这个RDD的：第一次调用的时候，清理缓存，然后进行登记，每个cache的RDD只做一次。

private[spark] def cleaner: Option[ContextCleaner] = _cleaner

private var _cleaner: Option[ContextCleaner] = None

registerRDDForCleanup函数如下：

/** Register a RDD for cleanup when it is garbage collected. */
def registerRDDForCleanup(rdd: RDD[_]): Unit = {
registerForCleanup(rdd, CleanRDD(rdd.id))
}

之后用sc.persistRDD(this)进行注册：

/**
* Register an RDD to be persisted in memory and/or disk storage
*/
private[spark] def persistRDD(rdd: RDD[_]) {
persistentRdds(rdd.id) = rdd
}

// Keeps track of all persisted RDDs
private[spark] val persistentRdds = {
val map: ConcurrentMap[Int, RDD[_]] = new MapMaker().weakValues().makeMap[Int, RDD[_]]()
map.asScala
}

用persistentRdds这个函数进行跟踪所有进行缓存的函数，它就用一个hashmap进行存储的，形式是：[Int, RDD[_]]

类型为：

new MapMaker().weakValues().makeMap[Int, RDD[_]]()

我们知道我们创建一个RDD时候，系统会分配一个id给这个RDD（看http://blog.csdn.net/legotime/article/details/51223572），那么这个[Int, RDD[_]]中的Int存储的就是这个id

4、RDD的存储

我们从调用数据的角度来分析，需要数据的时候，是怎么拿数据的。我们知道真正对RDD进行动刀的是action，也就是说当一个stage的ResultTask触发，我们才进行存储，我们查看ResultTask（源码2），在runTask中发现调用了RDD的iterator,下面我们看看RDD中iterator是什么，源码如下：

/**
* Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
* This should ''not'' be called by users directly, but is available for implementors of custom
* subclasses of RDD.
*/
final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
if (storageLevel != StorageLevel.NONE) {
getOrCompute(split, context)
} else {
computeOrReadCheckpoint(split, context)
}
}

我们发现iterator会判断这个存储级别是否为NONE，NONE的值如下：

val NONE = new StorageLevel(false, false, false, false)

class StorageLevel private(
private var _useDisk: Boolean,
private var _useMemory: Boolean,
private var _useOffHeap: Boolean,
private var _deserialized: Boolean,
private var _replication: Int = 1)
extends Externalizable {/*集合体*/}

如果判断这个存储级别不是NONE，也就是说之前已经cache了，那么会调用getOrCompute函数，getOrCompute如下：首先，我们输入blockid和存储级别（storageLevel）：会向blockManager发送请求，是否可以直接拿还是需要计算

private[spark] def getOrCompute(partition: Partition, context: TaskContext): Iterator[T] = {
val blockId = RDDBlockId(id, partition.index)  //获取RDD的blockID
var readCachedBlock = true
// This method is called on executors, so we need call SparkEnv.get instead of sc.env.
SparkEnv.get.blockManager.getOrElseUpdate(blockId, storageLevel, () => {
readCachedBlock = false
computeOrReadCheckpoint(partition, context)
}) match {
case Left(blockResult) =>
if (readCachedBlock) {
val existingMetrics = context.taskMetrics().registerInputMetrics(blockResult.readMethod)
existingMetrics.incBytesReadInternal(blockResult.bytes)
new InterruptibleIterator[T](context, blockResult.data.asInstanceOf[Iterator[T]]) {
override def next(): T = {
existingMetrics.incRecordsReadInternal(1)
delegate.next()
}
}
} else {
new InterruptibleIterator(context, blockResult.data.asInstanceOf[Iterator[T]])
}
case Right(iter) =>
new InterruptibleIterator(context, iter.asInstanceOf[Iterator[T]])
}
}

如果存在，那么我们就检索我们的block，如果不存在那么我们就提供`
makeiterator `方法，这个方法就是处理：

() => {
readCachedBlock = false
computeOrReadCheckpoint(partition, context)}

它的作用就是如果我们想要的block不存在，那么就调用computeOrReadCheckpoint(partition,
context)这个函数，具体getOrElseUpdate函数如下

def getOrElseUpdate(
blockId: BlockId,
level: StorageLevel,
makeIterator: () => Iterator[Any]): Either[BlockResult, Iterator[Any]] = {
// Initially we hold no locks on this block.
doPut(blockId, IteratorValues(makeIterator), level, keepReadLock = true) match {
//我们调用的block不存在，那么需要计算
case None =>
// doPut() didn't hand work back to us, so the block already existed or was successfully
// stored. Therefore, we now hold a read lock on the block.
val blockResult = get(blockId).getOrElse {
// Since we held a read lock between the doPut() and get() calls, the block should not
// have been evicted, so get() not returning the block indicates some internal error.
releaseLock(blockId)
throw new SparkException(s"get() failed for block $blockId even though we held a lock")
}
Left(blockResult)
//我们调用的block已经存在了
case Some(failedPutResult) =>
// The put failed, likely because the data was too large to fit in memory and could not be
// dropped to disk. Therefore, we need to pass the input iterator back to the caller so
// that they can decide what to do with the values (e.g. process them without caching).
Right(failedPutResult.data.left.get)
}
}

doput是存储管理，代码量很大，所以打算放到存储那块一起分析，不过可以从它返回的信息，来补充我们需要的分

析资料。doput的返回的信息如下：

* @return `Some(PutResult)` if the block did not exist and could not be successfully cached,
*         or None if the block already existed or was successfully stored (fully consuming
*         the input data / input iterator).

函数

private def doPut(
blockId: BlockId,
data: BlockValues,
level: StorageLevel,
tellMaster: Boolean = true,
effectiveStorageLevel: Option[StorageLevel] = None,
keepReadLock: Boolean = false): Option[PutResult] = {/*集合体*/}

/**
* Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing.
*/
private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] =
{
if (isCheckpointedAndMaterialized) {
firstParent[T].iterator(split, context)
} else {
compute(split, context)   //从新计算
}
}

综上所述，分析到最后如果都没有找到我们所需，那么就会先看看有没有checkpoint，最后在重新计算我们所需。下面是寻找数据的流程图：



附录

源码1

/**
* Mark this RDD for persisting using the specified level.
*
* @param newLevel the target storage level
* @param allowOverride whether to override any existing level with the new one
*/
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
}

/**
* Set this RDD's storage level to persist its values across operations after the first time
* it is computed. This can only be used to assign a new storage level if the RDD does not
* have a storage level set yet. Local checkpointing is an exception.
*/
def persist(newLevel: StorageLevel): this.type = {
if (isLocallyCheckpointed) {
// This means the user previously called localCheckpoint(), which should have already
// marked this RDD for persisting. Here we should override the old storage level with
// one that is explicitly requested by the user (after adapting it to use disk).
persist(LocalRDDCheckpointData.transformStorageLevel(newLevel), allowOverride = true)
} else {
persist(newLevel, allowOverride = false)
}
}

/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
def persist(): this.type = persist(StorageLevel.MEMORY_ONLY)

/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
def cache(): this.type = persist()

/**
* Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
*
* @param blocking Whether to block until all blocks are deleted.
* @return This RDD.
*/
def unpersist(blocking: Boolean = true): this.type = {
logInfo("Removing RDD " + id + " from persistence list")
sc.unpersistRDD(id, blocking)
storageLevel = StorageLevel.NONE
this
}

/** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */
def getStorageLevel: StorageLevel = storageLevel

源码2

private[spark] class ResultTask[T, U](
stageId: Int,
stageAttemptId: Int,
taskBinary: Broadcast[Array[Byte]],
partition: Partition,
locs: Seq[TaskLocation],
val outputId: Int,
_initialAccums: Seq[Accumulator[_]] = InternalAccumulator.createAll())
extends Task[U](stageId, stageAttemptId, partition.index, _initialAccums)
with Serializable {

@transient private[this] val preferredLocs: Seq[TaskLocation] = {
if (locs == null) Nil else locs.toSet.toSeq
}

override def runTask(context: TaskContext): U = {
// Deserialize the RDD and the func using the broadcast variables.
val deserializeStartTime = System.currentTimeMillis()
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime

metrics = Some(context.taskMetrics)
func(context, rdd.iterator(partition, context))
}

// This is only callable on the driver side.
override def preferredLocations: Seq[TaskLocation] = preferredLocs

override def toString: String = "ResultTask(" + stageId + ", " + partitionId + ")"
}

参考文献：

http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence