spark源码阅读 RDDs

RDDs弹性分布式数据集

spark就是实现了RDDs编程模型的集群计算平台。有很多RDDs的介绍，这里就不仔细说了，这儿主要看源码。

abstract class RDD[T: ClassTag](
    @transient private var _sc: SparkContext,
    @transient private var deps: Seq[Dependency[_]]
  ) extends Serializable with Logging {

 

SparkEnv几个重要组件

BlockManager

主要成员

  private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo]
  // Actual storage of where blocks are kept
  private var externalBlockStoreInitialized = false
  private[spark] val memoryStore = new MemoryStore(this, memoryManager)
  private[spark] val diskStore = new DiskStore(this, diskBlockManager)
  private[spark] lazy val externalBlockStore: ExternalBlockStore = {
    externalBlockStoreInitialized = true
    new ExternalBlockStore(this, executorId)
  }

 

主要方法

get(blockId: BlockId) 通过BlockId找Block

  /**
   * Get a block from the block manager (either local or remote).
   */
  def get(blockId: BlockId): Option[BlockResult] = {
    val local = getLocal(blockId)
    if (local.isDefined) {
      logInfo(s"Found block $blockId locally")
      return local
    }
    val remote = getRemote(blockId)
    if (remote.isDefined) {
      logInfo(s"Found block $blockId remotely")
      return remote
    }
    None
  }

getLocal(blockId: BlockId): Option[BlockResult] 通过本地Block Manager找Block

  /**
   * 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]]
  }

doGetLocal(blockId: BlockId, asBlockResult: Boolean): Option[Any] 在本地获取Block代码实现

  private def doGetLocal(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {
    val info = blockInfo.get(blockId).orNull
    if (info != null) {
      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.
        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
        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 external block store
        if (level.useOffHeap) {
          logDebug(s"Getting block $blockId from ExternalBlockStore")
          if (externalBlockStore.contains(blockId)) {
            val result = if (asBlockResult) {
              externalBlockStore.getValues(blockId)
                .map(new BlockResult(_, DataReadMethod.Memory, info.size))
            } else {
              externalBlockStore.getBytes(blockId)
            }
            result match {
              case Some(values) =>
                return result
              case None =>
                logDebug(s"Block $blockId not found in ExternalBlockStore")
            }
          }
        }

        // 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. */
              memoryStore.putBytes(blockId, bytes.limit, () => {
                // https://issues.apache.org/jira/browse/SPARK-6076
                // If the file size is bigger than the free memory, OOM will happen. So if we cannot
                // put it into MemoryStore, copyForMemory should not be created. That's why this
                // action is put into a `() => ByteBuffer` and created lazily.
                val copyForMemory = ByteBuffer.allocate(bytes.limit)
                copyForMemory.put(bytes)
              })
              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
  }

 

 

相关类

Dependency

宽依赖和窄依赖两种。Denpendency类中主要保存父RDD，根据partition id获得所依赖的父RDD partitions列表。ShuffleDependency还保存了shuffle需要的必要组件，shuffle output partitioner、serializer、keyOrdering、aggregator等

abstract class Dependency[T] extends Serializable {
  def rdd: RDD[T]
}


/**
 * :: DeveloperApi ::
 * Base class for dependencies where each partition of the child RDD depends on a small number
 * of partitions of the parent RDD. Narrow dependencies allow for pipelined execution.
 */
@DeveloperApi
abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
  /**
   * Get the parent partitions for a child partition.
   * @param partitionId a partition of the child RDD
   * @return the partitions of the parent RDD that the child partition depends upon
   */
  def getParents(partitionId: Int): Seq[Int]

  override def rdd: RDD[T] = _rdd
}


/**
 * :: DeveloperApi ::
 * Represents a dependency on the output of a shuffle stage. Note that in the case of shuffle,
 * the RDD is transient since we don't need it on the executor side.
 *
 * @param _rdd the parent RDD
 * @param partitioner partitioner used to partition the shuffle output
 * @param serializer [[org.apache.spark.serializer.Serializer Serializer]] to use. If set to None,
 *                   the default serializer, as specified by `spark.serializer` config option, will
 *                   be used.
 * @param keyOrdering key ordering for RDD's shuffles
 * @param aggregator map/reduce-side aggregator for RDD's shuffle
 * @param mapSideCombine whether to perform partial aggregation (also known as map-side combine)
 */
@DeveloperApi
class ShuffleDependency[K: ClassTag, V: ClassTag, C: ClassTag](
    @transient private val _rdd: RDD[_ <: Product2[K, V]],
    val partitioner: Partitioner,
    val serializer: Option[Serializer] = None,
    val keyOrdering: Option[Ordering[K]] = None,
    val aggregator: Option[Aggregator[K, V, C]] = None,
    val mapSideCombine: Boolean = false)
  extends Dependency[Product2[K, V]] {

  override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]]

  private[spark] val keyClassName: String = reflect.classTag[K].runtimeClass.getName
  private[spark] val valueClassName: String = reflect.classTag[V].runtimeClass.getName
  // Note: It's possible that the combiner class tag is null, if the combineByKey
  // methods in PairRDDFunctions are used instead of combineByKeyWithClassTag.
  private[spark] val combinerClassName: Option[String] =
    Option(reflect.classTag[C]).map(_.runtimeClass.getName)

  val shuffleId: Int = _rdd.context.newShuffleId()

  val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(
    shuffleId, _rdd.partitions.size, this)

  _rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
}


/**
 * :: DeveloperApi ::
 * Represents a one-to-one dependency between partitions of the parent and child RDDs.
 */
@DeveloperApi
class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {
  override def getParents(partitionId: Int): List[Int] = List(partitionId)
}


/**
 * :: DeveloperApi ::
 * Represents a one-to-one dependency between ranges of partitions in the parent and child RDDs.
 * @param rdd the parent RDD
 * @param inStart the start of the range in the parent RDD
 * @param outStart the start of the range in the child RDD
 * @param length the length of the range
 */
@DeveloperApi
class RangeDependency[T](rdd: RDD[T], inStart: Int, outStart: Int, length: Int)
  extends NarrowDependency[T](rdd) {

  override def getParents(partitionId: Int): List[Int] = {
    if (partitionId >= outStart && partitionId < outStart + length) {
      List(partitionId - outStart + inStart)
    } else {
      Nil
    }
  }
}

RDDBlockId

一个RDD partition用一个RDDBlock存储。

/**
 * :: DeveloperApi ::
 * Identifies a particular Block of data, usually associated with a single file.
 * A Block can be uniquely identified by its filename, but each type of Block has a different
 * set of keys which produce its unique name.
 *
 * If your BlockId should be serializable, be sure to add it to the BlockId.apply() method.
 */
@DeveloperApi
sealed abstract class BlockId {
  /** A globally unique identifier for this Block. Can be used for ser/de. */
  def name: String

  // convenience methods
  def asRDDId: Option[RDDBlockId] = if (isRDD) Some(asInstanceOf[RDDBlockId]) else None
  def isRDD: Boolean = isInstanceOf[RDDBlockId]
  def isShuffle: Boolean = isInstanceOf[ShuffleBlockId]
  def isBroadcast: Boolean = isInstanceOf[BroadcastBlockId]

  override def toString: String = name
  override def hashCode: Int = name.hashCode
  override def equals(other: Any): Boolean = other match {
    case o: BlockId => getClass == o.getClass && name.equals(o.name)
    case _ => false
  }
}

@DeveloperApi
case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockId {
  override def name: String = "rdd_" + rddId + "_" + splitIndex
}

BlockInfo

private[storage] class BlockInfo(val level: StorageLevel, val tellMaster: Boolean) {
  // To save space, 'pending' and 'failed' are encoded as special sizes:
  @volatile var size: Long = BlockInfo.BLOCK_PENDING
  private def pending: Boolean = size == BlockInfo.BLOCK_PENDING
  private def failed: Boolean = size == BlockInfo.BLOCK_FAILED
  private def initThread: Thread = BlockInfo.blockInfoInitThreads.get(this)

  setInitThread()

  private def setInitThread() {
    /* Set current thread as init thread - waitForReady will not block this thread
     * (in case there is non trivial initialization which ends up calling waitForReady
     * as part of initialization itself) */
    BlockInfo.blockInfoInitThreads.put(this, Thread.currentThread())
  }

  /**
   * Wait for this BlockInfo to be marked as ready (i.e. block is finished writing).
   * Return true if the block is available, false otherwise.
   */
  def waitForReady(): Boolean = {
    if (pending && initThread != Thread.currentThread()) {
      synchronized {
        while (pending) {
          this.wait()
        }
      }
    }
    !failed
  }

  /** Mark this BlockInfo as ready (i.e. block is finished writing) */
  def markReady(sizeInBytes: Long) {
    require(sizeInBytes >= 0, s"sizeInBytes was negative: $sizeInBytes")
    assert(pending)
    size = sizeInBytes
    BlockInfo.blockInfoInitThreads.remove(this)
    synchronized {
      this.notifyAll()
    }
  }

  /** Mark this BlockInfo as ready but failed */
  def markFailure() {
    assert(pending)
    size = BlockInfo.BLOCK_FAILED
    BlockInfo.blockInfoInitThreads.remove(this)
    synchronized {
      this.notifyAll()
    }
  }
}

private object BlockInfo {
  /* initThread is logically a BlockInfo field, but we store it here because
   * it's only needed while this block is in the 'pending' state and we want
   * to minimize BlockInfo's memory footprint. */
  private val blockInfoInitThreads = new ConcurrentHashMap[BlockInfo, Thread]

  private val BLOCK_PENDING: Long = -1L
  private val BLOCK_FAILED: Long = -2L
}

 

 

 

主要成员

/** sparkContext */
def sparkContext: SparkContext = sc
/** 唯一标识的RDD id */
val id: Int = sc.newRddId()
/** name */
@transient var name: String = null
/** 依赖列表 */
protected def getDependencies: Seq[Dependency[_]] = deps
/** 分区列表 */
protected def getPartitions: Array[Partition]
/** 子类可选重写，优先存储的位置 */
protected def getPreferredLocations(split: Partition): Seq[String] = Nil
/** 分区器 */
@transient val partitioner: Option[Partitioner] = None
/** 指定怎么计算的到RDD的分区 */
def compute(split: Partition, context: TaskContext): Iterator[T]

 

主要方法

persist实现

  private def persist(newLevel: StorageLevel, allowOverride: Boolean): this.type = {
    // TODO: Handle changes of StorageLevel
    if (storageLevel != StorageLevel.NONE && newLevel != storageLevel && !allowOverride) {
      throw new UnsupportedOperationException(
        "Cannot change storage level of an RDD after it was already assigned a level")
    }
    // If this is the first time this RDD is marked for persisting, register it
    // with the SparkContext for cleanups and accounting. Do this only once.
    if (storageLevel == StorageLevel.NONE) {
      sc.cleaner.foreach(_.registerRDDForCleanup(this))
      sc.persistRDD(this)
    }
    storageLevel = newLevel
    this
  }

  private[spark] def persistRDD(rdd: RDD[_]) {
    persistentRdds(rdd.id) = rdd
  }

获取数据iterator方法

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

getOrCompute

  /** 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
        val existingMetrics = context.taskMetrics
          .getInputMetricsForReadMethod(blockResult.readMethod)
        existingMetrics.incBytesRead(blockResult.bytes)

        val iter = blockResult.data.asInstanceOf[Iterator[T]]
        new InterruptibleIterator[T](context, iter) {
          override def next(): T = {
            existingMetrics.incRecordsRead(1)
            delegate.next()
          }
        }
      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.isRunningLocally) {
            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()
          }
        }
    }
  }