22、BlockManager原理剖析与源码分析

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.scalal

private 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.scalal

private 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.scala

def 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.scala

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. */
    // 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.scala

override 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.scala

override 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.scala

private[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.scala

override 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.scala
tryToPut()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.scala

private 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.scala

private 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.scala

private 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
" +
              s"offset=$offset
file=${file.getAbsolutePath}
buf.remaining=${buf.remaining}")
          }
        }
        buf.flip()
        Some(buf)
      } else {
        Some(channel.map(MapMode.READ_ONLY, offset, length))
      }
    } finally {
      channel.close()
    }






###org.apache.spark.storage/MemoryStore.scala
MemoryStore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))
    }
  }