一、原理

1、图解

Driver上，有BlockManagerMaster，它的功能，就是负责对各个节点上的BlockManager内部管理的数据的元数据进行维护，比如Block的增删改等操作，都会在这里维护元数据的变更;每个节点上，都有BlockManager，BlockManager上有几个关键组件：DiskStore，负责对磁盘上的数据进行读写;MemoryStore，负责对内存中的数据进行读写;ConnectionManager，负责建立BlockManager到远程其他节点的BlockManager的网络连接;BlockTransferService，负责远程其他节点的BlockManager的数据的读写;每个BlockManager创建之后，做的第一件事就是向BlockManagerMaster去进行注册，此时BlockManagerMaster会为其创建对应的BlockManagerInfo;使用BlockManager进行写操作时，比如说，RDD运行过程中的一些中间数据，或者手动指定了persist(),优先将数据写入内存中，内存大小不够用，会使用自己的算法，将内存中的部分数据写入磁盘;此外，如果persist()指定了要replica，那么，会使用BlockTransferService将数据replicate一份到其他节点的BlockManager上去;BlockTransferService会通过ConnectionManager连接其他BlockManager，BlockTransferService进行replicate操作;从BlockManager读数据时，比如Shuffle Read操作，如果能从本地读取数据，那么利用DiskStore或者MemoryStore从本地读取数据，如果本地没有数据的话，会用ConnectionManager与有数据的BlockManager建立连接，然后用BlockTransferService从远程BlockManager读取数据;只要使用了BlockManager执行了数据增删改查的操作，那么必须将block的BlockStatus上报到BlockManagerMaster上去，在BlockManagerMaster上，会对指定BlockManager的BlockManagerInfo内部的BlockStatus，进行增删改操作，从而达到元数据的维护功能;

二、源码分析

1、BlockManager注册

首先看BlockManagerMasterActor，BlockManagerMasterActor就是负责维护各个executor的BlockManager的元数据，BlockManagerInfo，BlockStatus首先看看BlockManagerMasterActor里面两个重要的map###org.apache.spark.storage/BlockManagerMasterActor.scalal// Mapping from block manager id to the block manager's information.  // 这个map，映射了block manager id 到 block manager的info  // BlockManagerMaster要负责维护每个BlockManager的BlockManagerInfo  private val blockManagerInfo = new mutable.HashMap[BlockManagerId, BlockManagerInfo]   // Mapping from executor ID to block manager ID.  // 映射了每个ExecutorId到BlockManagerId，也就是说，每个executor是与一个BlockManager相关联的  private val blockManagerIdByExecutor = new mutable.HashMap[String, BlockManagerId]###org.apache.spark.storage/BlockManagerMasterActor.scalal/**    * 注册BlockManager    */  private def register(id: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) {    val time = System.currentTimeMillis()    // 首先判断本地HashMap中没有指定的BlockManagerId，说明从来没有注册过，才会往下走，去注册这个BlockManager    if (!blockManagerInfo.contains(id)) {      // 根据BlockManager对应的executorId找到对应的BlockManagerInfo      // 这里其实是做一个安全判断，因为如果blockManagerInfo map里面没有BlockManagerId      // 那么同步的blockManagerIdByExecutor map里，也必须没有BlockManager对应的executor对应的BlockManagerId      // 所以这里要判断一下，如果blockManagerIdByExecutor map里有BlockManageId，那么做一下清理      blockManagerIdByExecutor.get(id.executorId) match {        case Some(oldId) =>          // A block manager of the same executor already exists, so remove it (assumed dead)          logError("Got two different block manager registrations on same executor - "               + s" will replace old one $oldId with new one $id")          // 从内存中，移除该executorId相关的BlockManagerInfo          removeExecutor(id.executorId)          case None =>      }      logInfo("Registering block manager %s with %s RAM, %s".format(        id.hostPort, Utils.bytesToString(maxMemSize), id))       // 往blockManagerIdByExecutor map中保存一份executorId到BlockManagerId的映射      blockManagerIdByExecutor(id.executorId) = id       // 为BlockManagerId创建一根BlockManagerInfo，并往blockManagerInfo map中，保存一份BlockManagerId到BlockManagerInfo的映射      blockManagerInfo(id) = new BlockManagerInfo(        id, System.currentTimeMillis(), maxMemSize, slaveActor)    }    listenerBus.post(SparkListenerBlockManagerAdded(time, id, maxMemSize))  }###org.apache.spark.storage/BlockManagerMasterActor.scalalprivate def removeExecutor(execId: String) {    logInfo("Trying to remove executor " + execId + " from BlockManagerMaster.")    // 获取executorId对应的BlockManagerInfo，对其调用removeBlockManager方法    blockManagerIdByExecutor.get(execId).foreach(removeBlockManager)  }###org.apache.spark.storage/BlockManagerMasterActor.scalalprivate def removeBlockManager(blockManagerId: BlockManagerId) {    // 尝试根据blockManagerId获取到它对应的BlockManagerInfo    val info = blockManagerInfo(blockManagerId)     // Remove the block manager from blockManagerIdByExecutor.    // 从blockManagerIdByExecutor map中移除executorId对应的BlockManagerInfo    blockManagerIdByExecutor -= blockManagerId.executorId     // Remove it from blockManagerInfo and remove all the blocks.    // 从blockManagerInfo也移除对应的BlockManagerInfo    blockManagerInfo.remove(blockManagerId)    // 遍历BlockManagerInfo内部所有的block的blockId    val iterator = info.blocks.keySet.iterator    while (iterator.hasNext) {      // 清空BlockManagerInfo内部的block的BlockStatus信息      val blockId = iterator.next      val locations = blockLocations.get(blockId)      locations -= blockManagerId      if (locations.size == 0) {        blockLocations.remove(blockId)      }    }    listenerBus.post(SparkListenerBlockManagerRemoved(System.currentTimeMillis(), blockManagerId))    logInfo(s"Removing block manager $blockManagerId")  }

2、更新BlockInfo

更新BlockInfo，也就是说，每个BlockManager上，如果block发生了变化，那么都要发送updateBlockInfo请求来BlockManagerMaster这里。来进行BlockInfo的更新

/**    * 更新BlockInfo，也就是说，每个BlockManager上，如果block发生了变化，那么都要发送updateBlockInfo请求来BlockManagerMaster这里。来进行BlockInfo的更新    */  private def updateBlockInfo(      blockManagerId: BlockManagerId,      blockId: BlockId,      storageLevel: StorageLevel,      memSize: Long,      diskSize: Long,      tachyonSize: Long): Boolean = {     if (!blockManagerInfo.contains(blockManagerId)) {      if (blockManagerId.isDriver && !isLocal) {        // We intentionally do not register the master (except in local mode),        // so we should not indicate failure.        return true      } else {        return false      }    }     if (blockId == null) {      blockManagerInfo(blockManagerId).updateLastSeenMs()      return true    }     // 调用BlockManager的blockManagerInfo的updateBlockInfo()方法，更新block信息    blockManagerInfo(blockManagerId).updateBlockInfo(      blockId, storageLevel, memSize, diskSize, tachyonSize)     // 每一个block可能会在多个BlockManager上面，因为如果将StorageLevel设置成带着_2的这种，那么就需要将block replicate一份，放到其他    // BlockManager上，blockLocations map其实保存了blockId对应的BlockManagerId的set集合，所以，这里会更新blockLocations中的信息，    // 因为是用set存储BlockManagerId，因此自动就去重了    var locations: mutable.HashSet[BlockManagerId] = null    if (blockLocations.containsKey(blockId)) {      locations = blockLocations.get(blockId)    } else {      locations = new mutable.HashSet[BlockManagerId]      blockLocations.put(blockId, locations)    }     if (storageLevel.isValid) {      locations.add(blockManagerId)    } else {      locations.remove(blockManagerId)    }     // Remove the block from master tracking if it has been removed on all slaves.    if (locations.size == 0) {      blockLocations.remove(blockId)    }    true  }

3、BlockManager初始化

BlockManager运行在每个节点上，包括Driver和Executor，都会有一份，主要提供了在本地或者远程存取数据的功能，支持内存、磁盘、堆外存储(Tychyon)###org.apache.spark.storage/BlockManager.scala// 每个BlockManager，都会自己维护一个map，这里维护的blockInfo map，可以代表一个block，blockInfo最大的作用，就是用于  // 多线程并发访问同一个block的同步监视器  private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo]###org.apache.spark.storage/BlockManager.scaladef initialize(appId: String): Unit = {    // 首先初始化，用于进行远程block数据传输的blockTransferService    blockTransferService.init(this)    shuffleClient.init(appId)     // 为当前这个BlockManager创建一个唯一的BlockManagerId    // 使用executorId(每个BlockManager都关联一个Executor)，blockTransferService的hostname，blockTransferService的port    // 所以，从BlockManagerId的初始化即可看出，一个BlockManager是通过一个节点上的Executor来唯一标识的    blockManagerId = BlockManagerId(      executorId, blockTransferService.hostName, blockTransferService.port)     shuffleServerId = if (externalShuffleServiceEnabled) {      BlockManagerId(executorId, blockTransferService.hostName, externalShuffleServicePort)    } else {      blockManagerId    }     // 使用BlockManagerMasterActor的引用，进行BlockManager的注册，发送消息到BlockManagerMasterActor    master.registerBlockManager(blockManagerId, maxMemory, slaveActor)     // Register Executors' configuration with the local shuffle service, if one should exist.    if (externalShuffleServiceEnabled && !blockManagerId.isDriver) {      registerWithExternalShuffleServer()    }  }

4、BlockManager写数据

###org.apache.spark.storage/BlockManager.scalaprivate 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. */    // 为要写入的block，创建一个blockInfo，并将其放入blockinfo map中缓存起来    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    }     // 尝试对BlockInfo加锁，进行多线程并发访问同步    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        // 首先根据持久化级别，选择一种BlockStore        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        // 根据选择的BlockStore，然后根据数据的类型，将数据放入store中        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 += _ }        }         // 获取到一个Block对应的BlockStatus        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) {            // 调用reportBlockStatus()方法，将新写入的block数据，发送到BlockManagerMaster，以便于进行block元数据的同步和维护            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:    // 如果持久化是定义了_2这种后缀，说明需要对block进行replica，然后传输到其他节点上    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)          }          // 调用replicate()方法进行复制操作          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  }###org.apache.spark.storage/DiskStore.scalaoverride def putBytes(blockId: BlockId, _bytes: ByteBuffer, level: StorageLevel): PutResult = {    // So that we do not modify the input offsets !    // duplicate does not copy buffer, so inexpensive    val bytes = _bytes.duplicate()    logDebug(s"Attempting to put block $blockId")    val startTime = System.currentTimeMillis    val file = diskManager.getFile(blockId)    // 使用Java NIO将数据写入磁盘文件    val channel = new FileOutputStream(file).getChannel    while (bytes.remaining > 0) {      channel.write(bytes)    }    channel.close()    val finishTime = System.currentTimeMillis    logDebug("Block %s stored as %s file on disk in %d ms".format(      file.getName, Utils.bytesToString(bytes.limit), finishTime - startTime))    PutResult(bytes.limit(), Right(bytes.duplicate()))  }###org.apache.spark.storage/MemoryStore.scala// MemoryStore中维护的entries map 其实就是真正存放每个block的数据  // 每个Block在内存中的数据，用MemoryEntry代表  private val entries = new LinkedHashMap[BlockId, MemoryEntry](32, 0.75f, true)###org.apache.spark.storage/MemoryStore.scalaoverride def putBytes(blockId: BlockId, _bytes: ByteBuffer, level: StorageLevel): PutResult = {    // Work on a duplicate - since the original input might be used elsewhere.    val bytes = _bytes.duplicate()    bytes.rewind()    if (level.deserialized) {      val values = blockManager.dataDeserialize(blockId, bytes)      putIterator(blockId, values, level, returnValues = true)    } else {      val putAttempt = tryToPut(blockId, bytes, bytes.limit, deserialized = false)      PutResult(bytes.limit(), Right(bytes.duplicate()), putAttempt.droppedBlocks)    }  }###org.apache.spark.storage/MemoryStore.scala  override def putIterator(      blockId: BlockId,      values: Iterator[Any],      level: StorageLevel,      returnValues: Boolean): PutResult = {    putIterator(blockId, values, level, returnValues, allowPersistToDisk = true)  }###org.apache.spark.storage/MemoryStore.scalaprivate[storage] def putIterator(      blockId: BlockId,      values: Iterator[Any],      level: StorageLevel,      returnValues: Boolean,      allowPersistToDisk: Boolean): PutResult = {    val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]    val unrolledValues = unrollSafely(blockId, values, droppedBlocks)    unrolledValues match {      case Left(arrayValues) =>        // Values are fully unrolled in memory, so store them as an array        val res = putArray(blockId, arrayValues, level, returnValues)        droppedBlocks ++= res.droppedBlocks        PutResult(res.size, res.data, droppedBlocks)      case Right(iteratorValues) =>        // Not enough space to unroll this block; drop to disk if applicable        if (level.useDisk && allowPersistToDisk) {          logWarning(s"Persisting block $blockId to disk instead.")          val res = blockManager.diskStore.putIterator(blockId, iteratorValues, level, returnValues)          PutResult(res.size, res.data, droppedBlocks)        } else {          PutResult(0, Left(iteratorValues), droppedBlocks)        }    }  }###org.apache.spark.storage/MemoryStore.scalaoverride def putArray(      blockId: BlockId,      values: Array[Any],      level: StorageLevel,      returnValues: Boolean): PutResult = {    if (level.deserialized) {      val sizeEstimate = SizeEstimator.estimate(values.asInstanceOf[AnyRef])      val putAttempt = tryToPut(blockId, values, sizeEstimate, deserialized = true)      PutResult(sizeEstimate, Left(values.iterator), putAttempt.droppedBlocks)    } else {      val bytes = blockManager.dataSerialize(blockId, values.iterator)      val putAttempt = tryToPut(blockId, bytes, bytes.limit, deserialized = false)      PutResult(bytes.limit(), Right(bytes.duplicate()), putAttempt.droppedBlocks)    }  }###org.apache.spark.storage/MemoryStore.scalatryToPut()方法，优先放入内存，不行的话，尝试移除部分旧数据，再将block存入，真正存数据的方法；private def tryToPut(      blockId: BlockId,      value: Any,      size: Long,      deserialized: Boolean): ResultWithDroppedBlocks = {     /* TODO: Its possible to optimize the locking by locking entries only when selecting blocks     * to be dropped. Once the to-be-dropped blocks have been selected, and lock on entries has     * been released, it must be ensured that those to-be-dropped blocks are not double counted     * for freeing up more space for another block that needs to be put. Only then the actually     * dropping of blocks (and writing to disk if necessary) can proceed in parallel. */     var putSuccess = false    val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]     // 进行多线程并发同步，这里必须进行多线程并发同步，因为可能你刚判断内存足够，但是其他线程就放入了数据，然后你往内存中放数据，直接OOM内存溢出    accountingLock.synchronized {      // 调用ensureFreeSpace()方法，判断内存是否够用，如果不够用，此时会将部分数据用dropFromMemory()方法尝试写入磁盘，但是如果持久化级别不支持磁盘，那么数据丢失      val freeSpaceResult = ensureFreeSpace(blockId, size)      val enoughFreeSpace = freeSpaceResult.success      droppedBlocks ++= freeSpaceResult.droppedBlocks       // 将数据写入内存的时候，首先调用enoughFreeSpace()方法，判断内存是否足够放入数据      if (enoughFreeSpace) {        // 给数据创建一份MemoryEntry        val entry = new MemoryEntry(value, size, deserialized)        entries.synchronized {          // 将数据放入内存的entries中          entries.put(blockId, entry)          currentMemory += size        }        val valuesOrBytes = if (deserialized) "values" else "bytes"        logInfo("Block %s stored as %s in memory (estimated size %s, free %s)".format(          blockId, valuesOrBytes, Utils.bytesToString(size), Utils.bytesToString(freeMemory)))        putSuccess = true      } else {        // Tell the block manager that we couldn't put it in memory so that it can drop it to        // disk if the block allows disk storage.        val data = if (deserialized) {          Left(value.asInstanceOf[Array[Any]])        } else {          Right(value.asInstanceOf[ByteBuffer].duplicate())        }        val droppedBlockStatus = blockManager.dropFromMemory(blockId, data)        droppedBlockStatus.foreach { status => droppedBlocks += ((blockId, status)) }      }    }    ResultWithDroppedBlocks(putSuccess, droppedBlocks)  }###org.apache.spark.storage/MemoryStore.scalaprivate def ensureFreeSpace(      blockIdToAdd: BlockId,      space: Long): ResultWithDroppedBlocks = {    logInfo(s"ensureFreeSpace($space) called with curMem=$currentMemory, maxMem=$maxMemory")     val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]     if (space > maxMemory) {      logInfo(s"Will not store $blockIdToAdd as it is larger than our memory limit")      return ResultWithDroppedBlocks(success = false, droppedBlocks)    }     // Take into account the amount of memory currently occupied by unrolling blocks    val actualFreeMemory = freeMemory - currentUnrollMemory     // 如果当前内存不足够将这个block放入的话    if (actualFreeMemory < space) {      val rddToAdd = getRddId(blockIdToAdd)      val selectedBlocks = new ArrayBuffer[BlockId]      var selectedMemory = 0L       // This is synchronized to ensure that the set of entries is not changed      // (because of getValue or getBytes) while traversing the iterator, as that      // can lead to exceptions.      // 同步entries      entries.synchronized {        val iterator = entries.entrySet().iterator()        // 尝试从entries中，移除一部分数据        while (actualFreeMemory + selectedMemory < space && iterator.hasNext) {          val pair = iterator.next()          val blockId = pair.getKey          if (rddToAdd.isEmpty || rddToAdd != getRddId(blockId)) {            selectedBlocks += blockId            selectedMemory += pair.getValue.size          }        }      }       // 判断，如果移除一部分数据，就可以存放新的block了      if (actualFreeMemory + selectedMemory >= space) {        logInfo(s"${selectedBlocks.size} blocks selected for dropping")        // 将之前选择要移除的block数据，遍历        for (blockId <- selectedBlocks) {          val entry = entries.synchronized { entries.get(blockId) }          // This should never be null as only one thread should be dropping          // blocks and removing entries. However the check is still here for          // future safety.          if (entry != null) {            val data = if (entry.deserialized) {              Left(entry.value.asInstanceOf[Array[Any]])            } else {              Right(entry.value.asInstanceOf[ByteBuffer].duplicate())            }            // 调用dropFromMemory()方法，尝试将数据写入磁盘，但是如果block的持久化级别没有写入磁盘，那么这个数据就彻底丢了            val droppedBlockStatus = blockManager.dropFromMemory(blockId, data)            droppedBlockStatus.foreach { status => droppedBlocks += ((blockId, status)) }          }        }        return ResultWithDroppedBlocks(success = true, droppedBlocks)      } else {        logInfo(s"Will not store $blockIdToAdd as it would require dropping another block " +          "from the same RDD")        return ResultWithDroppedBlocks(success = false, droppedBlocks)      }    }    ResultWithDroppedBlocks(success = true, droppedBlocks)  }

6、BlockManager读数据

###org.apache.spark.storage/MemoryStore.scala/**   * 从本地获取数据   */  private def doGetLocal(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {    // 尝试获取block对应的blockInfo的锁    val info = blockInfo.get(blockId).orNull    if (info != null) {      // 对所有的blockInfo，都会进行多线程并发访问的同步操作，所以BlockInfo，相当于是对一个Block，用于作为多线程并发访问的同步监视器      info.synchronized {        // Double check to make sure the block is still there. There is a small chance that the        // block has been removed by removeBlock (which also synchronizes on the blockInfo object).        // Note that this only checks metadata tracking. If user intentionally deleted the block        // on disk or from off heap storage without using removeBlock, this conditional check will        // still pass but eventually we will get an exception because we can't find the block.        if (blockInfo.get(blockId).isEmpty) {          logWarning(s"Block $blockId had been removed")          return None        }         // If another thread is writing the block, wait for it to become ready.        // 如果其他线程在操作这个block，那么其实会卡住，等待，去获取BlockInfo的排他锁，如果始终没有获取到，返回false，就直接返回        if (!info.waitForReady()) {          // If we get here, the block write failed.          logWarning(s"Block $blockId was marked as failure.")          return None        }         val level = info.level        logDebug(s"Level for block $blockId is $level")         // Look for the block in memory        // 判断，如果持久化级别使用了内存，比如MEMORY_ONLY，MEMORY_AND_DISK，MEMORY_ONLY_SER，MEMORY_AND_DSK_SER等        // 尝试从MemoryStore中获取数据        if (level.useMemory) {          logDebug(s"Getting block $blockId from memory")          val result = if (asBlockResult) {            memoryStore.getValues(blockId).map(new BlockResult(_, DataReadMethod.Memory, info.size))          } else {            memoryStore.getBytes(blockId)          }          result match {            case Some(values) =>              return result            case None =>              logDebug(s"Block $blockId not found in memory")          }        }         // Look for the block in Tachyon        if (level.useOffHeap) {          logDebug(s"Getting block $blockId from tachyon")          if (tachyonStore.contains(blockId)) {            tachyonStore.getBytes(blockId) match {              case Some(bytes) =>                if (!asBlockResult) {                  return Some(bytes)                } else {                  return Some(new BlockResult(                    dataDeserialize(blockId, bytes), DataReadMethod.Memory, info.size))                }              case None =>                logDebug(s"Block $blockId not found in tachyon")            }          }        }         // Look for block on disk, potentially storing it back in memory if required        // 判断，如果持久化级别使用了磁盘        if (level.useDisk) {          logDebug(s"Getting block $blockId from disk")          val bytes: ByteBuffer = diskStore.getBytes(blockId) match {            case Some(b) => b            case None =>              throw new BlockException(                blockId, s"Block $blockId not found on disk, though it should be")          }          assert(0 == bytes.position())           if (!level.useMemory) {            // If the block shouldn't be stored in memory, we can just return it            if (asBlockResult) {              return Some(new BlockResult(dataDeserialize(blockId, bytes), DataReadMethod.Disk,                info.size))            } else {              return Some(bytes)            }          } else {            // Otherwise, we also have to store something in the memory store            if (!level.deserialized || !asBlockResult) {              /* We'll store the bytes in memory if the block's storage level includes               * "memory serialized", or if it should be cached as objects in memory               * but we only requested its serialized bytes. */              val copyForMemory = ByteBuffer.allocate(bytes.limit)              copyForMemory.put(bytes)              // 如果使用了Disk级别，也使用了Memory级别，那么从disk读取出来之后，其实会尝试将其放入MemoryStore中，也就是缓存到内存中              memoryStore.putBytes(blockId, copyForMemory, level)              bytes.rewind()            }            if (!asBlockResult) {              return Some(bytes)            } else {              val values = dataDeserialize(blockId, bytes)              if (level.deserialized) {                // Cache the values before returning them                val putResult = memoryStore.putIterator(                  blockId, values, level, returnValues = true, allowPersistToDisk = false)                // The put may or may not have succeeded, depending on whether there was enough                // space to unroll the block. Either way, the put here should return an iterator.                putResult.data match {                  case Left(it) =>                    return Some(new BlockResult(it, DataReadMethod.Disk, info.size))                  case _ =>                    // This only happens if we dropped the values back to disk (which is never)                    throw new SparkException("Memory store did not return an iterator!")                }              } else {                return Some(new BlockResult(values, DataReadMethod.Disk, info.size))              }            }          }        }      }    } else {      logDebug(s"Block $blockId not registered locally")    }    None  }###org.apache.spark.storage/MemoryStore.scalaprivate def doGetRemote(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {    require(blockId != null, "BlockId is null")    // 首先从BlockManagerMaster上，获取每个blockId对应的BlockManager的信息，然后会随机打乱    val locations = Random.shuffle(master.getLocations(blockId))    // 遍历每个BlockManager    for (loc <- locations) {      logDebug(s"Getting remote block $blockId from $loc")      // 使用blockTransferService进行，异步的远程网络获取，将block数据传输过来      // 连接的时候，使用BlockManager的唯一标识，就是host，port，executorId      val data = blockTransferService.fetchBlockSync(        loc.host, loc.port, loc.executorId, blockId.toString).nioByteBuffer()       if (data != null) {        if (asBlockResult) {          return Some(new BlockResult(            dataDeserialize(blockId, data),            DataReadMethod.Network,            data.limit()))        } else {          return Some(data)        }      }      logDebug(s"The value of block $blockId is null")    }    logDebug(s"Block $blockId not found")    None  }###org.apache.spark.storage/DiskStore.scalaprivate def getBytes(file: File, offset: Long, length: Long): Option[ByteBuffer] = {    // 底层使用的是java的nio进行文件的读写操作    val channel = new RandomAccessFile(file, "r").getChannel     try {      // For small files, directly read rather than memory map      if (length < minMemoryMapBytes) {        val buf = ByteBuffer.allocate(length.toInt)        channel.position(offset)        while (buf.remaining() != 0) {          if (channel.read(buf) == -1) {            throw new IOException("Reached EOF before filling buffer\n" +              s"offset=$offset\nfile=${file.getAbsolutePath}\nbuf.remaining=${buf.remaining}")          }        }        buf.flip()        Some(buf)      } else {        Some(channel.map(MapMode.READ_ONLY, offset, length))      }    } finally {      channel.close()    }###org.apache.spark.storage/MemoryStore.scalaMemoryStore的getBytes()和getValues()方法override def getBytes(blockId: BlockId): Option[ByteBuffer] = {    // entries也是多线程并发访问同步的    val entry = entries.synchronized {      // 尝试从内存中获取block数据      entries.get(blockId)    }    if (entry == null) {    // 如果没有获取到 就返回None      None    } else if (entry.deserialized) {      // 如果读取到了非序列化的数据，调用BlockManager序列化方法，将数据序列化后返回      Some(blockManager.dataSerialize(blockId, entry.value.asInstanceOf[Array[Any]].iterator))    } else {      // 否则，直接返回数据      Some(entry.value.asInstanceOf[ByteBuffer].duplicate()) // Doesn't actually copy the data    }  }   override def getValues(blockId: BlockId): Option[Iterator[Any]] = {    val entry = entries.synchronized {      entries.get(blockId)    }    if (entry == null) {      None    } else if (entry.deserialized) {      // 如果非序列化，直接返回      Some(entry.value.asInstanceOf[Array[Any]].iterator)    } else {      // 如果序列化了，那么用blockManager进行反序列化返回      val buffer = entry.value.asInstanceOf[ByteBuffer].duplicate() // Doesn't actually copy data      Some(blockManager.dataDeserialize(blockId, buffer))    }  }