缓存管理器CacheManager

如果说spark是一把在大数据处理领域的快刀，那么spark的存储系统设计及管理等相关模块就算不是刀尖，也算得上十分锋利的刀锋了，由于工作需要，我会伴着源码来深入学习一下，这里做一个记录备忘。

RDD的cache和persist

谈到spark存储，第一反应先想到了RDD里的cache和persist。如果从RDD中的cache方法作为入口来看，cache与persist殊途同归，无非是persist支持可配的storage level作为入参，而cache直接就是默认了MEMORY_ONLY。核心是三个步骤，一个是把RDD内的storageLevel字段设置一下，另一个是在sparkcontext中标记一下，最后注册一下清理任务。最后一步先不看，仔细跟了一下在sparkcontext中标记这个动作，可以看到这个记录除了调试测试时候观察一下以外在核心流程中实际上不会用到这个标记，那么“可疑分子”就是RDD内的storageLevel这个属性了。另外，通过代码可以清楚看到，这个storageLevel只能被设置一次，换句话说就是一旦从NONE改为了一个值后就不能被修改了，否则会报错。

然后哪里会用到这个可疑分子呢，跟一把代码，看一下RDD任务计算的核心函数之一：RDD里藏着的迭代器函数：

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)    }}

很明显，如果storageLevel不是NONE的话就通过CacheManager的getOrCompute来搞起啦。

CacheManager

这货是藏在SparkEnv里的诸多大神之一，这里我们来好好看看。先简单介绍一下，CacheManager负责将RDD的分区数据传递到BlockManager，并且保证一个节点一次不会保存两个副本。

CacheManager里的核心方法就是刚才看到过的getOrCompute方法，这里粗粗看一下：

/** Gets or computes an RDD partition. Used by RDD.iterator() when an RDD is cached. */  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        context.taskMetrics.inputMetrics = Some(blockResult.inputMetrics)        new InterruptibleIterator(context, blockResult.data.asInstanceOf[Iterator[T]])        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.runningLocally) {            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()          }        }    }  }  

先根据传入的RDD ID和对应的partition index来获得在blockmanager上对应的Key，然后一个match来进行匹配：如果是已缓存的则直接返回用于获取数据的迭代器，具体的hasNext和next方法可以自行查阅；如果blockmanager上没有对应的数据，那么这里先会调用computeOrReadCheckpoint来进行计算，然后通过putInBlockManager方法把计算结果放到BlockManager，我们最开始说的storageLevel一路被人传来传去，在这里马上就要迎来人生的巅峰了。

再深入一把，看看putInBlockManager在干嘛：

/**    * Cache the values of a partition, keeping track of any updates in the storage statuses of    * other blocks along the way.    *    * The effective storage level refers to the level that actually specifies BlockManager put    * behavior, not the level originally specified by the user. This is mainly for forcing a    * MEMORY_AND_DISK partition to disk if there is not enough room to unroll the partition,    * while preserving the the original semantics of the RDD as specified by the application.    */    private def putInBlockManager[T](        key: BlockId,        values: Iterator[T],        level: StorageLevel,        updatedBlocks: ArrayBuffer[(BlockId, BlockStatus)],        effectiveStorageLevel: Option[StorageLevel] = None): Iterator[T] = {        val putLevel = effectiveStorageLevel.getOrElse(level)      if (!putLevel.useMemory) {        /*        * This RDD is not to be cached in memory, so we can just pass the computed values as an        * iterator directly to the BlockManager rather than first fully unrolling it in memory.        */        updatedBlocks ++=          blockManager.putIterator(key, values, level, tellMaster = true, effectiveStorageLevel)        blockManager.get(key) match {          case Some(v) => v.data.asInstanceOf[Iterator[T]]          case None =>            logInfo(s"Failure to store $key")            throw new BlockException(key, s"Block manager failed to return cached value for $key!")        }      } else {        /*        * This RDD is to be cached in memory. In this case we cannot pass the computed values        * to the BlockManager as an iterator and expect to read it back later. This is because        * we may end up dropping a partition from memory store before getting it back.        *        * In addition, we must be careful to not unroll the entire partition in memory at once.        * Otherwise, we may cause an OOM exception if the JVM does not have enough space for this        * single partition. Instead, we unroll the values cautiously, potentially aborting and        * dropping the partition to disk if applicable.        */        blockManager.memoryStore.unrollSafely(key, values, updatedBlocks) match {          case Left(arr) =>            // We have successfully unrolled the entire partition, so cache it in memory            updatedBlocks ++=              blockManager.putArray(key, arr, level, tellMaster = true, effectiveStorageLevel)            arr.iterator.asInstanceOf[Iterator[T]]          case Right(it) =>            // There is not enough space to cache this partition in memory            logWarning(s"Not enough space to cache partition $key in memory! " +              s"Free memory is ${blockManager.memoryStore.freeMemory} bytes.")            val returnValues = it.asInstanceOf[Iterator[T]]            if (putLevel.useDisk) {              logWarning(s"Persisting partition $key to disk instead.")              val diskOnlyLevel = StorageLevel(useDisk = true, useMemory = false,                useOffHeap = false, deserialized = false, putLevel.replication)              putInBlockManager[T](key, returnValues, level, updatedBlocks, Some(diskOnlyLevel))            } else {              returnValues            }        }      }    }  

这里又把情况分为是否使用useMemory两种情况，先看一下非useMemory的情况。这种情况时在这里的处理十分简单，只是通过blockManager的putIterator方法把数据注册到blockManager端，但在blockManager端会做不少事情。进到blockmanager的doPut方法里：

private def doPut(        blockId: BlockId,        data: BlockValues,        level: StorageLevel,        tellMaster: Boolean = true,        effectiveStorageLevel: Option[StorageLevel] = None)      : Seq[(BlockId, BlockStatus)] = {        require(blockId != null, "BlockId is null")      require(level != null && level.isValid, "StorageLevel is null or invalid")      effectiveStorageLevel.foreach { level =>        require(level != null && level.isValid, "Effective StorageLevel is null or invalid")      }        // Return value      val updatedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]        /* Remember the block's storage level so that we can correctly drop it to disk if it needs      * to be dropped right after it got put into memory. Note, however, that other threads will      * not be able to get() this block until we call markReady on its BlockInfo. */      val putBlockInfo = {        val tinfo = new BlockInfo(level, tellMaster)        // Do atomically !        val oldBlockOpt = blockInfo.putIfAbsent(blockId, tinfo)        if (oldBlockOpt.isDefined) {          if (oldBlockOpt.get.waitForReady()) {            logWarning(s"Block $blockId already exists on this machine; not re-adding it")            return updatedBlocks          }          // TODO: So the block info exists - but previous attempt to load it (?) failed.          // What do we do now ? Retry on it ?          oldBlockOpt.get        } else {          tinfo        }      }        val startTimeMs = System.currentTimeMillis        /* If we're storing values and we need to replicate the data, we'll want access to the values,      * but because our put will read the whole iterator, there will be no values left. For the      * case where the put serializes data, we'll remember the bytes, above; but for the case where      * it doesn't, such as deserialized storage, let's rely on the put returning an Iterator. */      var valuesAfterPut: Iterator[Any] = null        // Ditto for the bytes after the put      var bytesAfterPut: ByteBuffer = null        // Size of the block in bytes      var size = 0L        // The level we actually use to put the block      val putLevel = effectiveStorageLevel.getOrElse(level)        // If we're storing bytes, then initiate the replication before storing them locally.      // This is faster as data is already serialized and ready to send.      val replicationFuture = data match {        case b: ByteBufferValues if putLevel.replication > 1 =>          // Duplicate doesn't copy the bytes, but just creates a wrapper          val bufferView = b.buffer.duplicate()          Future { replicate(blockId, bufferView, putLevel) }        case _ => null      }        putBlockInfo.synchronized {        logTrace("Put for block %s took %s to get into synchronized block"          .format(blockId, Utils.getUsedTimeMs(startTimeMs)))          var marked = false        try {          // returnValues - Whether to return the values put          // blockStore - The type of storage to put these values into          val (returnValues, blockStore: BlockStore) = {            if (putLevel.useMemory) {              // Put it in memory first, even if it also has useDisk set to true;              // We will drop it to disk later if the memory store can't hold it.              (true, memoryStore)            } else if (putLevel.useOffHeap) {              // Use tachyon for off-heap storage              (false, tachyonStore)            } else if (putLevel.useDisk) {              // Don't get back the bytes from put unless we replicate them              (putLevel.replication > 1, diskStore)            } else {              assert(putLevel == StorageLevel.NONE)              throw new BlockException(                blockId, s"Attempted to put block $blockId without specifying storage level!")            }          }            // Actually put the values          val result = data match {            case IteratorValues(iterator) =>              blockStore.putIterator(blockId, iterator, putLevel, returnValues)            case ArrayValues(array) =>              blockStore.putArray(blockId, array, putLevel, returnValues)            case ByteBufferValues(bytes) =>              bytes.rewind()              blockStore.putBytes(blockId, bytes, putLevel)          }          size = result.size          result.data match {            case Left (newIterator) if putLevel.useMemory => valuesAfterPut = newIterator            case Right (newBytes) => bytesAfterPut = newBytes            case _ =>          }            // Keep track of which blocks are dropped from memory          if (putLevel.useMemory) {            result.droppedBlocks.foreach { updatedBlocks += _ }          }            val putBlockStatus = getCurrentBlockStatus(blockId, putBlockInfo)          if (putBlockStatus.storageLevel != StorageLevel.NONE) {            // Now that the block is in either the memory, tachyon, or disk store,            // let other threads read it, and tell the master about it.            marked = true            putBlockInfo.markReady(size)            if (tellMaster) {              reportBlockStatus(blockId, putBlockInfo, putBlockStatus)            }            updatedBlocks += ((blockId, putBlockStatus))          }        } finally {          // If we failed in putting the block to memory/disk, notify other possible readers          // that it has failed, and then remove it from the block info map.          if (!marked) {            // Note that the remove must happen before markFailure otherwise another thread            // could've inserted a new BlockInfo before we remove it.            blockInfo.remove(blockId)            putBlockInfo.markFailure()            logWarning(s"Putting block $blockId failed")          }        }      }      logDebug("Put block %s locally took %s".format(blockId, Utils.getUsedTimeMs(startTimeMs)))        // Either we're storing bytes and we asynchronously started replication, or we're storing      // values and need to serialize and replicate them now:      if (putLevel.replication > 1) {        data match {          case ByteBufferValues(bytes) =>            if (replicationFuture != null) {              Await.ready(replicationFuture, Duration.Inf)            }          case _ =>            val remoteStartTime = System.currentTimeMillis            // Serialize the block if not already done            if (bytesAfterPut == null) {              if (valuesAfterPut == null) {                throw new SparkException(                  "Underlying put returned neither an Iterator nor bytes! This shouldn't happen.")              }              bytesAfterPut = dataSerialize(blockId, valuesAfterPut)            }            replicate(blockId, bytesAfterPut, putLevel)            logDebug("Put block %s remotely took %s"              .format(blockId, Utils.getUsedTimeMs(remoteStartTime)))        }      }        BlockManager.dispose(bytesAfterPut)        if (putLevel.replication > 1) {        logDebug("Putting block %s with replication took %s"          .format(blockId, Utils.getUsedTimeMs(startTimeMs)))      } else {        logDebug("Putting block %s without replication took %s"          .format(blockId, Utils.getUsedTimeMs(startTimeMs)))      }        updatedBlocks    }  

很长的一个方法，我这里只说说几个比较重要的地方。这个方法顾名思义是用来缓存数据的，那么在执行缓存之前需要做一些准备工作。首先通过一个match确定是否要返回数据以及选择一个什么类型的BlockStore。对于BlockStore这里分了三种：memoryStore、tachyonStore、diskStore。第一种和第三种每什么疑问，至于tachyon是一个内存分布式文件系统，建立在ramdisk之上，和spark同宗同源，是spark兼容的存储系统之一，可以用来不同application之间共享rdd甚至与其他例如storm这样的框架共享内存数据，不过目前貌似还是在实验阶段，官网上有标注的。

确定了是否返回数据和BlockStore后，然后立马根据输入数据的组织结构把数据塞到BlockStore里面去，然后得到一个PutResult对象，如果配置的是会返回数据则在这里面还会带数据。随便挑一个看看，比如DiskStore的putBytes方法，可以看到这里是真的已经写进去了。附带一提，这里用的是nio哟。

最后，跳过一些状态更新暂时先不看，对于需要cache副本大于1的情况，就调用replicate方法传递保存副本。这个replicate方法内容很简单，从BlockMaster上获取节点信息后，然后连接异步地丢上去就行了。

=============================华丽地分割线==================================

回到CacheManager的putInBlockManager，讲完非useMemory情况后来把剩下的那部分useMemory看了。

注释里说了，在这种情况下我们就不能像之前那样，直接丢个找数据的迭代器给blockmanager然后在要用的时候再从上面拉回来。因为很有可能我们要用时这片内存已经被干掉了。此外，如果我们肆无忌惮地一次性把数据都丢到内存里也很有可能导致OOM，所以这里必须小心翼翼地搞起。所以我们就来看看是怎么个小心翼翼法。

这里用到了BlockManager里的MemoryStore(前面也有提到过)的unrollSafely方法。在这个方法之后如果能够成功把所有数据cache起来就直接put上去，如果不可以就先看看设的这个Level支部支持放DISK的，如果支持就把level改成disk，然后数据就直接丢到disk上去啦；如果不支持，比如默认的MEMORY_ONLY，那就不缓存了。所以结论就是：也不要太肆无忌惮地cache一把就高枕无忧，世界还是很奇妙的...