BlockManager初始化和注册解密、BlockManagerMaster工作解密、BlockTransferService解密、本地数据读写解密、远程数据读写解密

BlockManager初始化和注册解密、BlockManagerMaster工作解密、BlockTransferService解密、本地数据读写解密、远程数据读写解密_第1张图片
1. BlockManager的注册和初始化
2. BlockManager里面的重要函数详细介绍
BlockManager初始化和注册解密、BlockManagerMaster工作解密、BlockTransferService解密、本地数据读写解密、远程数据读写解密_第2张图片
BlockManager初始化和注册解密、BlockManagerMaster工作解密、BlockTransferService解密、本地数据读写解密、远程数据读写解密_第3张图片
一:BlockManager初始化
1. BlockManager的实例对象调用initializes的时候才能正常工作。
启动initializes方法又两个作用:BlockTransferService(网络通信),ShuffleClient

/**
 * Initializes the BlockManager with the given appId. This is not performed in the constructor as
 * the appId may not be known at BlockManager instantiation time (in particular for the driver,
 * where it is only learned after registration with the TaskScheduler).
 *
 * This method initializes the BlockTransferService and ShuffleClient, registers with the
 * BlockManagerMaster, starts the BlockManagerWorker endpoint, and registers with a local shuffle
 * service if configured.
 */

在executor启动的时候通过BlockManager.initialize来实例化Executor的BlockManager。

if (!isLocal) {
  env.metricsSystem.registerSource(executorSource)
  env.blockManager.initialize(conf.getAppId)
}

BlockManager在启动的时候都会向BlockManagerMaster注册。

master.registerBlockManager(blockManagerId, maxMemory, slaveEndpoint)

并且创建BlockManagerSlaveEndpoint这个消息循环体来接受Driver中的BlockManagerMaster发过来的指令,例如删除Block等;

private val slaveEndpoint = rpcEnv.setupEndpoint(
  "BlockManagerEndpoint" + BlockManager.ID_GENERATOR.next,
  new BlockManagerSlaveEndpoint(rpcEnv, this, mapOutputTracker))

下面就具体看一下BlockManagerSlaveEndpoint,从注释里面可以看到, BlockManagerSlaveEndpoint接收BlockManagerMaster发过来的信息。

/**
 * An RpcEndpoint to take commands from the master to execute options. For example,
 * this is used to remove blocks from the slave's BlockManager.
 */

BlockManager注册
1. 通过RegisterBlockManager注册BlockManager

/** Register the BlockManager's id with the driver. */
def registerBlockManager(
    blockManagerId: BlockManagerId, maxMemSize: Long, slaveEndpoint: RpcEndpointRef): Unit = {
  logInfo("Trying to register BlockManager")
  tell(RegisterBlockManager(blockManagerId, maxMemSize, slaveEndpoint))
  logInfo("Registered BlockManager")
}

2.Tell就将此方法发送给Driver端。

/** Send a one-way message to the master endpoint, to which we expect it to reply with true. */
private def tell(message: Any) {
  if (!driverEndpoint.askWithRetry[Boolean](message)) {
    throw new SparkException("BlockManagerMasterEndpoint returned false, expected true.")
  }
}

3.当BlockManagerSlaveEndpoint实例化后,Executor上的BlockManager需要向Driver上的BlockManagerMasterEndpoint注册。

master.registerBlockManager(blockManagerId, maxMemory, slaveEndpoint)

4.BlockManagerMasterEndpoint接收到Executor上的注册信息并进行处理。
BlockManagerMasterEndpoint:

/**
 * BlockManagerMasterEndpoint is an [[ThreadSafeRpcEndpoint]] on the master node to track statuses
 * of all slaves' block managers.
 */

override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
  case RegisterBlockManager(blockManagerId, maxMemSize, slaveEndpoint) =>
    register(blockManagerId, maxMemSize, slaveEndpoint)
    context.reply(true)

下面具体分析register方法:

private def register(id: BlockManagerId, maxMemSize: Long, slaveEndpoint: RpcEndpointRef) {
  val time = System.currentTimeMillis()
  if (!blockManagerInfo.contains(id)) {
    blockManagerIdByExecutor.get(id.executorId) match {

5.分析里面的参数blockManagerInfo,blockManagerMaster会为每一个executor创建一个blockManagerInfo,blockManagerInfo是Driver端为了管理ExecutorBackend中的BlockManager上面的所有元数据而设立的。

// Mapping from block manager id to the block manager's information.
private val blockManagerInfo = new mutable.HashMap[BlockManagerId, BlockManagerInfo]

根据BlockManagerId来映射BlockManager的信息。

private[spark] class BlockManagerInfo(
    val blockManagerId: BlockManagerId,//获得BlockManager的Id
    timeMs: Long,       
    val maxMem: Long,   
    val slaveEndpoint: RpcEndpointRef)  

6.根据获得的BlockManagerId来判断此时的BlockManager是否被注册过,如果注册过了那么就将此BlockManager,remove掉。

private def register(id: BlockManagerId, maxMemSize: Long, slaveEndpoint: RpcEndpointRef) {
  val time = System.currentTimeMillis()
  if (!blockManagerInfo.contains(id)) {
    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")
        removeExecutor(id.executorId)//此时的executorId是从blockManagerId中获取的。
      case None =>
    }

根据blockManagerIdByExecutor将executor ID从映射block manager ID,从而就获取了executor ID。

// Mapping from executor ID to block manager ID.
private val blockManagerIdByExecutor = new mutable.HashMap[String, BlockManagerId]

7.下面我们看一下具体是怎么removeExecutor掉的,以及remove掉了什么?
调用removeExecutor来remove掉Executor。

private def removeExecutor(execId: String) {
  logInfo("Trying to remove executor " + execId + " from BlockManagerMaster.")
  blockManagerIdByExecutor.get(execId).foreach(removeBlockManager)
}

removeBlockManager具体实现blockManager的删除。

private def removeBlockManager(blockManagerId: BlockManagerId) {
  val info = blockManagerInfo(blockManagerId)//先得到BlockManagerInfo

  // Remove the block manager from blockManagerIdByExecutor.
  blockManagerIdByExecutor -= blockManagerId.executorId
//blockManager管理的对象是Block
  // Remove it from blockManagerInfo and remove all the blocks.
  blockManagerInfo.remove(blockManagerId)
  val iterator = info.blocks.keySet.iterator
  while (iterator.hasNext) {    //重复注册的话,会将所有的Block,删除掉
    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")
}

下面来详细的看一下BlockManager里面的重要的方法:
BlockManager初始化和注册解密、BlockManagerMaster工作解密、BlockTransferService解密、本地数据读写解密、远程数据读写解密_第4张图片
8.Executor上的BlockManager注册完成之后,BlockManager会不断的向Driver汇报executor上的Block的状态。

private def reportAllBlocks(): Unit = {
  logInfo(s"Reporting ${blockInfo.size} blocks to the master.")
  for ((blockId, info) <- blockInfo) {
    val status = getCurrentBlockStatus(blockId, info)
    if (!tryToReportBlockStatus(blockId, info, status)) {
      logError(s"Failed to report $blockId to master; giving up.")
      return
    }
  }
}

9.获得Block的位置,就要发消息给DriverEndpoint,向Driver端索取Block的位置信息。

/**
 * Get locations of an array of blocks.
 */
private def getLocationBlockIds(blockIds: Array[BlockId]): Array[Seq[BlockManagerId]] = {
  val startTimeMs = System.currentTimeMillis
  val locations = master.getLocations(blockIds).toArray
  logDebug("Got multiple block location in %s".format(Utils.getUsedTimeMs(startTimeMs)))
  locations
}

具体实现是在BlockManagerMaster,因为BlockManagerMaster拥有所有BlockManager的信息。

/** Get locations of multiple blockIds from the driver */
def getLocations(blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
  driverEndpoint.askWithRetry[IndexedSeq[Seq[BlockManagerId]]](
    GetLocations    MultipleBlockIds(blockIds))
}

10.通过getLocationsMultipleBlockIds来从BlockManagerMasterEndpoint中获得BlockId的位置。

private def getLocationsMultipleBlockIds(
    blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
  blockIds.map(blockId => getLocations(blockId))
}

getLocations首先会判断内存缓冲区中是否有BlockId如果有则直接返回。

private def getLocations(blockId: BlockId): Seq[BlockManagerId] = {
  if (blockLocations.containsKey(blockId)) blockLocations.get(blockId).toSeq else Seq.empty
}

blockLocations中的V为啥是一个HashSet?

// Mapping from block id to the set of block managers that have the block.
private val blockLocations = new JHashMap[BlockId, mutable.HashSet[BlockManagerId]]

因为一个Block一般会有副本,并且副本存储在不同机器上,不同机器上的BlockManager一定是不一样的,则BlockId肯定是不一样的,因此要返回HashSet.
11.通过getLocal从本地来获得Block信息。

/**
 * Get block from local block manager.
 */
def getLocal(blockId: BlockId): Option[BlockResult] = {
  logDebug(s"Getting local block $blockId")
  doGetLocal(blockId, asBlockResult = true).asInstanceOf[Option[BlockResult]]
}

具体看一下doGetLocal实现。

private def doGetLocal(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {
  val info = blockInfo.get(blockId).orNull
  if (info != null) {
    info.synchronized {

为啥里面用了synchronized?不同的线程去操作一块数据,JVM是多线程操作的数据,所以用了一个同步代码块来防止资源竞争。
如果有其他线程正在操作,所以该线程就要等待,为了保证数据的一致性。

// If another thread is writing the block, wait for it to become ready.
if (!info.waitForReady()) { //所以要等待
  // If we get here, the block write failed.
  logWarning(s"Block $blockId was marked as failure.")
  return None
}

在内存中寻找Block。

// Look for the block in memory
if (level.useMemory) {  //useMemory是Block的存储级别中的内存  
  logDebug(s"Getting block $blockId from memory")
  val result = if (asBlockResult) {//如果有则返回结果
    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")
  }
}

如果存储的数据在磁盘中,则会将磁盘中的数据存储到内存中。

// Look for block on disk, potentially storing it back in memory if required
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. */
//将数据存储到内存中
      memoryStore.putBytes(blockId, bytes.limit, () => {

12.getRemote从远程获取数据。

/**
 * Get block from remote block managers.
 */
def getRemote(blockId: BlockId): Option[BlockResult] = {
  logDebug(s"Getting remote block $blockId")
  doGetRemote(blockId, asBlockResult = true).asInstanceOf[Option[BlockResult]]
}

BlockId对于的Block一般是有多个副本,只需要读取一个副本上的数据即可。

private def doGetRemote(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {
  require(blockId != null, "BlockId is null")
//通过BlockId,master就可以获取BlockdId所对应的不同节点上的block副本,然后再对结果进行Shuffle一下。此时的Shuffle只是为了负载均衡。
  val locations = Random.shuffle(master.getLocations(blockId))

通过BlockTransforService来获取不同节点上的副本。

var numFetchFailures = 0
for (loc <- locations) {
  logDebug(s"Getting remote block $blockId from $loc")
  val data = try {
    blockTransferService.fetchBlockSync(
      loc.host, loc.port, loc.executorId, blockId.toString).nioByteBuffer()
  } catch {
    case NonFatal(e) =>
      numFetchFailures += 1
      if (numFetchFailures == locations.size) {//获取副本的时候可能会失败。
//所以下面会有失败次数的限制
        // An exception is thrown while fetching this block from all locations
        throw new BlockFetchException(s"Failed to fetch block from" +
          s" ${locations.size} locations. Most recent failure cause:",

BlockTransforService获取副本是通过具体实现的。

val data = try {
  blockTransferService.fetchBlockSync(
    loc.host, loc.port, loc.executorId, blockId.toString).nioByteBuffer()

13.Drop的block有可能放到disk上,此可能只有一种就是Memory and Disk的时候,而此时的Memory不够的时候,才会将block放到Disk中。
其次,如果你的数据并没有指定Memory and Disk的时候,数据就直接丢弃了,这时候如果你曾经进行了cache,那再次获取的时候就需要重新计算。
Drop:是指当我们的内存不够的时候,尝试释放一部分内存,给要使用内存的应用或者操作。
这个时候就会有权衡,如果直接丢弃的话,下回再次用的时候就要重新计算,如果cache的话,下次用直接调用。

/**
 * Drop a block from memory, possibly putting it on disk if applicable. Called when the memory
 * store reaches its limit and needs to free up space.
 *
 * If `data` is not put on disk, it won't be created.
 *
 * Return the block status if the given block has been updated, else None.
 */

总结: 通过源码的方式对BlockManager进行了详细的分析,但是对象持久化和消息通信方面接下来几篇将会详细剖析。

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