Spark内核源码解析十三：cacheManager原理解析和源码解析

CacheManager原理图

入口RDD的iterator方法

 final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
    // 如果持久化级别不是none，之前持久化过RDD，那么就不要直接去执行父RDD的算子，
    // 计算新的RDD的Partition优先采用cacheManager去获取持久化的数据
    if (storageLevel != StorageLevel.NONE) {
      SparkEnv.get.cacheManager.getOrCompute(this, split, context, storageLevel)
    } else {
      computeOrReadCheckpoint(split, context)
    }
  }

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")
    // 从blockManager获取数据，获取到直接返回，
    blockManager.get(key) match {
      case Some(blockResult) =>
        // Partition is already materialized, so just return its values
        val inputMetrics = blockResult.inputMetrics
        val existingMetrics = context.taskMetrics
          .getInputMetricsForReadMethod(inputMetrics.readMethod)
        existingMetrics.incBytesRead(inputMetrics.bytesRead)

        val iter = blockResult.data.asInstanceOf[Iterator[T]]
        new InterruptibleIterator[T](context, iter) {
          override def next(): T = {
            existingMetrics.incRecordsRead(1)
            delegate.next()
          }
        }
        // 如果没有获取到数据，虽然之前持久化过，就只能再次获取数据，如果没有获取到
      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")
          // 如果rdd之前checkpoint过，那就尝试直接读取checkpoint，如果没有就只能采用父rdd重新再计算一次
          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)]
          // 重新计算了或者读取了数据要重新存放如BlockManager中
          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()
          }
        }
    }
  }

  private def putInBlockManager[T](
      key: BlockId,
      values: Iterator[T],
      level: StorageLevel,
      updatedBlocks: ArrayBuffer[(BlockId, BlockStatus)],
      effectiveStorageLevel: Option[StorageLevel] = None): Iterator[T] = {

    val putLevel = effectiveStorageLevel.getOrElse(level)
    if (!putLevel.useMemory) {
      /*
       * This RDD is not to be cached in memory, so we can just pass the computed values as an
       * iterator directly to the BlockManager rather than first fully unrolling it in memory.
       */
      updatedBlocks ++=
        blockManager.putIterator(key, values, level, tellMaster = true, effectiveStorageLevel)
      blockManager.get(key) match {
        case Some(v) => v.data.asInstanceOf[Iterator[T]]
        case None =>
          logInfo(s"Failure to store $key")
          throw new BlockException(key, s"Block manager failed to return cached value for $key!")
      }
    } else {
      /*
       * This RDD is to be cached in memory. In this case we cannot pass the computed values
       * to the BlockManager as an iterator and expect to read it back later. This is because
       * we may end up dropping a partition from memory store before getting it back.
       *
       * In addition, we must be careful to not unroll the entire partition in memory at once.
       * Otherwise, we may cause an OOM exception if the JVM does not have enough space for this
       * single partition. Instead, we unroll the values cautiously, potentially aborting and
       * dropping the partition to disk if applicable.
       */
      // 如果持久化级别是内存，将数据写入内存，如果unrollSafely判断可以写入内存，如果不行就写入磁盘文件
      //
      blockManager.memoryStore.unrollSafely(key, values, updatedBlocks) match {
        case Left(arr) =>
          // We have successfully unrolled the entire partition, so cache it in memory
          updatedBlocks ++=
            blockManager.putArray(key, arr, level, tellMaster = true, effectiveStorageLevel)
          arr.iterator.asInstanceOf[Iterator[T]]
        case Right(it) =>
          // There is not enough space to cache this partition in memory
          val returnValues = it.asInstanceOf[Iterator[T]]
          // 有些数据如果不能写入内存，判断是否有磁盘的持久化级别，尝试写入磁盘
          if (putLevel.useDisk) {
            logWarning(s"Persisting partition $key to disk instead.")
            val diskOnlyLevel = StorageLevel(useDisk = true, useMemory = false,
              useOffHeap = false, deserialized = false, putLevel.replication)
            putInBlockManager[T](key, returnValues, level, updatedBlocks, Some(diskOnlyLevel))
          } else {
            returnValues
          }
      }
    }
  }