[spark] 内存管理 MemoryManager 解析

概述

spark的内存管理有两套方案,新旧方案分别对应的类是UnifiedMemoryManager和StaticMemoryManager。

旧方案是静态的,storageMemory(存储内存)和executionMemory(执行内存)拥有的内存是独享的不可相互借用,故在其中一方内存充足,另一方内存不足但又不能借用的情况下会造成资源的浪费。新方案是统一管理的,初始状态是内存各占一半,但其中一方内存不足时可以向对方借用,对内存资源进行合理有效的利用,提高了整体资源的利用率。

总的来说内存分为三大块,包括storageMemory、executionMemory、系统预留,其中storageMemory用来缓存rdd,unroll partition,存放direct task result、广播变量,在 Spark Streaming receiver 模式中存放每个 batch 的 blocks。executionMemory用于shuffle、join、sort、aggregation 中的缓存。除了这两者以外的内存都是预留给系统的。

旧方案 StaticMemoryManager

在SparkEnv中会创建memoryManager:

val useLegacyMemoryManager = conf.getBoolean("spark.memory.useLegacyMode", false)
    val memoryManager: MemoryManager =
      if (useLegacyMemoryManager) {
        new StaticMemoryManager(conf, numUsableCores)
      } else {
        UnifiedMemoryManager(conf, numUsableCores)
      }

默认使用的是统一管理方案UnifiedMemoryManager,这里我们简要的看看旧方案StaticMemoryManager。

storageMemory能分到的内存是:

systemMaxMemory * memoryFraction * safetyFraction

其中:

  • systemMaxMemory :Runtime.getRuntime.maxMemory,即JVM能获得的最大内存空间。
  • memoryFraction:由参数spark.storage.memoryFraction控制,默认0.6。
  • safetyFraction:由参数spark.storage.safetyFraction控制,默认是0.9,因为cache block都是估算的,所以需要一个安全系数来保证安全。

executionMemory能分到的内存是:

systemMaxMemory * memoryFraction * safetyFraction

其中:

  • systemMaxMemory :Runtime.getRuntime.maxMemory,即JVM能获得的最大内存空间。
  • memoryFraction:由参数spark.shuffle.memoryFraction控制,默认0.2。
  • safetyFraction:由参数spark.shuffle.safetyFraction控制,默认是0.8。

memoryFraction系数之外和安全系数之外的内存就是给系统预留的了。

executionMemory能分到的内存直接影响了shuffle中spill的频率,增加executionMemory可减少spill的次数,但storageMemory能cache的容量也相应减少。

execution 和 storage 被分配到内存后大小就一直不变了,每次申请内存都只能申请自己独有的不能相互借用,会造成资源的浪费。另外,只有 execution 内存支持 off heap,storage 内存不支持 off heap。

新方案 UnifiedMemoryManager

由于新方案中storageMemory和executionMemory是统一管理的,我们看看两者一共能拿到多少内存。

private def getMaxMemory(conf: SparkConf): Long = {
    val systemMemory = conf.getLong("spark.testing.memory", Runtime.getRuntime.maxMemory)
    val reservedMemory = conf.getLong("spark.testing.reservedMemory",
      if (conf.contains("spark.testing")) 0 else RESERVED_SYSTEM_MEMORY_BYTES)
    val minSystemMemory = (reservedMemory * 1.5).ceil.toLong
    if (systemMemory < minSystemMemory) {
      throw new IllegalArgumentException(s"System memory $systemMemory must " +
        s"be at least $minSystemMemory. Please increase heap size using the --driver-memory " +
        s"option or spark.driver.memory in Spark configuration.")
    }
    // SPARK-12759 Check executor memory to fail fast if memory is insufficient
    if (conf.contains("spark.executor.memory")) {
      val executorMemory = conf.getSizeAsBytes("spark.executor.memory")
      if (executorMemory < minSystemMemory) {
        throw new IllegalArgumentException(s"Executor memory $executorMemory must be at least " +
          s"$minSystemMemory. Please increase executor memory using the " +
          s"--executor-memory option or spark.executor.memory in Spark configuration.")
      }
    }
    val usableMemory = systemMemory - reservedMemory
    val memoryFraction = conf.getDouble("spark.memory.fraction", 0.6)
    (usableMemory * memoryFraction).toLong
  }

首先给系统内存reservedMemory预留了300M,若jvm能拿到的最大内存和配置的executor内存分别不足以reservedMemory的1.5倍即450M都会抛出异常,最后storage和execution能拿到的内存为:

 (heap space - 300) * spark.memory.fraction (默认为0.6)

storage和execution各占所获内存的50%。

申请storage内存

为某个blockId申请numBytes大小的内存:

override def acquireStorageMemory(
      blockId: BlockId,
      numBytes: Long,
      memoryMode: MemoryMode): Boolean = synchronized {
    assertInvariants()
    assert(numBytes >= 0)
    val (executionPool, storagePool, maxMemory) = memoryMode match {
      case MemoryMode.ON_HEAP => (
        onHeapExecutionMemoryPool,
        onHeapStorageMemoryPool,
        maxOnHeapStorageMemory)
      case MemoryMode.OFF_HEAP => (
        offHeapExecutionMemoryPool,
        offHeapStorageMemoryPool,
        maxOffHeapMemory)
    }
    // 申请的内存大于storage和execution内存之和
    if (numBytes > maxMemory) {
      // Fail fast if the block simply won't fit
      logInfo(s"Will not store $blockId as the required space ($numBytes bytes) exceeds our " +
        s"memory limit ($maxMemory bytes)")
      return false
    }
    // 大于storage空闲内存
    if (numBytes > storagePool.memoryFree) {
      // There is not enough free memory in the storage pool, so try to borrow free memory from
      // the execution pool.
      val memoryBorrowedFromExecution = Math.min(executionPool.memoryFree, numBytes)
      executionPool.decrementPoolSize(memoryBorrowedFromExecution)
      storagePool.incrementPoolSize(memoryBorrowedFromExecution)
    }
    storagePool.acquireMemory(blockId, numBytes)
  }
  • 若申请的numBytes比两者总共的内存还大,直接返回false,说明申请失败。
  • 若numBytes比storage空闲的内存大,则需要向executionPool借用
    • 借用的大小为此时execution的空闲内存和numBytes的较小值(个人观点应该是和<numBytes-storage空闲内存>的较小值)
    • 减小execution的poolSize
    • 增加storage的poolSize

即使向executionPool借用了内存,但不一定就够numBytes,因为不可能把execution正在使用的内存都接过来,接着调用了storagePool的acquireMemory方法在不够numBytes的情况下去释放storage中共cache的rdd,以增加storagePool.memoryFree的值:

def acquireMemory(blockId: BlockId, numBytes: Long): Boolean = lock.synchronized {
    val numBytesToFree = math.max(0, numBytes - memoryFree)
    acquireMemory(blockId, numBytes, numBytesToFree)
  }

计算出向execution借了内存后还差多少内存才能满足numBytes,即需要释放的内存numBytesToFree 。接着调用了acquireMemory方法:

def acquireMemory(
      blockId: BlockId,
      numBytesToAcquire: Long,
      numBytesToFree: Long): Boolean = lock.synchronized {
    assert(numBytesToAcquire >= 0)
    assert(numBytesToFree >= 0)
    assert(memoryUsed <= poolSize)
    if (numBytesToFree > 0) {
      memoryStore.evictBlocksToFreeSpace(Some(blockId), numBytesToFree, memoryMode)
    }
    // NOTE: If the memory store evicts blocks, then those evictions will synchronously call
    // back into this StorageMemoryPool in order to free memory. Therefore, these variables
    // should have been updated.
    val enoughMemory = numBytesToAcquire <= memoryFree
    if (enoughMemory) {
      _memoryUsed += numBytesToAcquire
    }
    enoughMemory
  }

当numBytesToFree 大于0的情况下,就真的要去释放缓存在memory中的block,释放完后再看空闲内存是否能满足numBytes,若满足则将numBytes加到已使用的变量里。

看看当需要从storay中释放block的时候是怎么释放的:

private[spark] def evictBlocksToFreeSpace(
      blockId: Option[BlockId],
      space: Long,
      memoryMode: MemoryMode): Long = {
    assert(space > 0)
    memoryManager.synchronized {
      var freedMemory = 0L
      val rddToAdd = blockId.flatMap(getRddId)
      val selectedBlocks = new ArrayBuffer[BlockId]
      def blockIsEvictable(blockId: BlockId, entry: MemoryEntry[_]): Boolean = {
        entry.memoryMode == memoryMode && (rddToAdd.isEmpty || rddToAdd != getRddId(blockId))
      }
      // 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.
      entries.synchronized {
        val iterator = entries.entrySet().iterator()
        while (freedMemory < space && iterator.hasNext) {
          val pair = iterator.next()
          val blockId = pair.getKey
          val entry = pair.getValue
          if (blockIsEvictable(blockId, entry)) {
            // We don't want to evict blocks which are currently being read, so we need to obtain
            // an exclusive write lock on blocks which are candidates for eviction. We perform a
            // non-blocking "tryLock" here in order to ignore blocks which are locked for reading:
            if (blockInfoManager.lockForWriting(blockId, blocking = false).isDefined) {
              selectedBlocks += blockId
              freedMemory += pair.getValue.size
            }
          }
        }
      }

      def dropBlock[T](blockId: BlockId, entry: MemoryEntry[T]): Unit = {
        val data = entry match {
          case DeserializedMemoryEntry(values, _, _) => Left(values)
          case SerializedMemoryEntry(buffer, _, _) => Right(buffer)
        }
        val newEffectiveStorageLevel =
          blockEvictionHandler.dropFromMemory(blockId, () => data)(entry.classTag)
        if (newEffectiveStorageLevel.isValid) {
          // The block is still present in at least one store, so release the lock
          // but don't delete the block info
          blockInfoManager.unlock(blockId)
        } else {
          // The block isn't present in any store, so delete the block info so that the
          // block can be stored again
          blockInfoManager.removeBlock(blockId)
        }
      }

      if (freedMemory >= space) {
        logInfo(s"${selectedBlocks.size} blocks selected for dropping " +
          s"(${Utils.bytesToString(freedMemory)} bytes)")
        for (blockId <- selectedBlocks) {
          val entry = entries.synchronized { entries.get(blockId) }
          // This should never be null as only one task should be dropping
          // blocks and removing entries. However the check is still here for
          // future safety.
          if (entry != null) {
            dropBlock(blockId, entry)
          }
        }
        logInfo(s"After dropping ${selectedBlocks.size} blocks, " +
          s"free memory is ${Utils.bytesToString(maxMemory - blocksMemoryUsed)}")
        freedMemory
      } else {
        blockId.foreach { id =>
          logInfo(s"Will not store $id")
        }
        selectedBlocks.foreach { id =>
          blockInfoManager.unlock(id)
        }
        0L
      }
    }
  }

spark中内存中的block都是通过memoryStore来存储的,用

private val entries = new LinkedHashMap[BlockId, MemoryEntry[_]](32, 0.75f, true)

来维护了blockId和MemoryEntry(对应value的包装)的关联,另外方法中还定义了两个方法,blockIsEvictable方法是判断遍历到的blockId和当前blockId是否属于同一个rdd,因为不能提出同一个rdd的另外一个block。dropBlock方法就是真正执行从内存中移除block的,若StorageLevel包括了使用disk,则会写到磁盘文件。

整段代码的逻辑简单概述就是:遍历当前memoryStore中存的每个block(不是和当前请求的block属于同于同一rdd),直到block对应的内存之和大于所需释放的内存才停止遍历,也有可能遍历完了都还不能满足所需的内存。若能释放的内存满足所需的内存,则真正执行移除,否则不移除,因为不可能一个block在内存中一部分,在磁盘一部分,最后返回真正剔除block释放的内存。

总结一下向StorageMemory申请内存的过程(在MemoryMode.ON_HEAP模式下):

  • 若numBytes大于storage和execution内存之和,抛异常。
  • 若numBytes大于storage空闲内存,向execution借用min(executionFree,numBytes)大的内存,并更新各自的poolSize。
  • 若申请完后还不够,则释放storage中的block来补足。
    • memoryStore缓存的block大小满足需要补足的大小,则真正执行剔除(遍历block直到内存满足需求对应的block),否则不剔除。
  • 最终若空闲内存满足numBytes则返回true,否则返回false。

申请execution内存

在execution内存不足向storage借用时,还是不满足所需内存的情况下能借多少借多少。看看在需要向execution申请内存时是怎么处理的(MemoryMode.ON_HEAP模式下):

override private[memory] def acquireExecutionMemory(
      numBytes: Long,
      taskAttemptId: Long,
      memoryMode: MemoryMode): Long = synchronized {
    assertInvariants()
    assert(numBytes >= 0)
    val (executionPool, storagePool, storageRegionSize, maxMemory) = memoryMode match {
      case MemoryMode.ON_HEAP => (
        onHeapExecutionMemoryPool,
        onHeapStorageMemoryPool,
        onHeapStorageRegionSize,
        maxHeapMemory)
      case MemoryMode.OFF_HEAP => (
        offHeapExecutionMemoryPool,
        offHeapStorageMemoryPool,
        offHeapStorageMemory,
        maxOffHeapMemory)
    }

    /**
     * Grow the execution pool by evicting cached blocks, thereby shrinking the storage pool.
     *
     * When acquiring memory for a task, the execution pool may need to make multiple
     * attempts. Each attempt must be able to evict storage in case another task jumps in
     * and caches a large block between the attempts. This is called once per attempt.
     */
    def maybeGrowExecutionPool(extraMemoryNeeded: Long): Unit = {
      if (extraMemoryNeeded > 0) {
        // There is not enough free memory in the execution pool, so try to reclaim memory from
        // storage. We can reclaim any free memory from the storage pool. If the storage pool
        // has grown to become larger than `storageRegionSize`, we can evict blocks and reclaim
        // the memory that storage has borrowed from execution.
        val memoryReclaimableFromStorage = math.max(
          storagePool.memoryFree,
          storagePool.poolSize - storageRegionSize)
        if (memoryReclaimableFromStorage > 0) {
          // Only reclaim as much space as is necessary and available:
          val spaceToReclaim = storagePool.freeSpaceToShrinkPool(
            math.min(extraMemoryNeeded, memoryReclaimableFromStorage))
          storagePool.decrementPoolSize(spaceToReclaim)
          executionPool.incrementPoolSize(spaceToReclaim)
        }
      }
    }

    /**
     * The size the execution pool would have after evicting storage memory.
     *
     * The execution memory pool divides this quantity among the active tasks evenly to cap
     * the execution memory allocation for each task. It is important to keep this greater
     * than the execution pool size, which doesn't take into account potential memory that
     * could be freed by evicting storage. Otherwise we may hit SPARK-12155.
     *
     * Additionally, this quantity should be kept below `maxMemory` to arbitrate fairness
     * in execution memory allocation across tasks, Otherwise, a task may occupy more than
     * its fair share of execution memory, mistakenly thinking that other tasks can acquire
     * the portion of storage memory that cannot be evicted.
     */
    def computeMaxExecutionPoolSize(): Long = {
      maxMemory - math.min(storagePool.memoryUsed, storageRegionSize)
    }

    executionPool.acquireMemory(
      numBytes, taskAttemptId, maybeGrowExecutionPool, computeMaxExecutionPoolSize)
  }

这里先讲解这里面的两个方法:

maybeGrowExecutionPool就是需要向storage借内存的方法,能借用的最大内存memoryReclaimableFromStorage 为storage的空闲内存和storage向execution借用的内存(即已经使用也要释放来归还)的较大值,若memoryReclaimableFromStorage为0,则说明storage之前没有向execution借用内存,并且此时storage没有空闲的内存可借。
最终申请借用的是所需内存和memoryReclaimableFromStorage的较小值(缺多少借多少),跟进storagePool.freeSpaceToShrinkPool方法看看其实现:

def freeSpaceToShrinkPool(spaceToFree: Long): Long = lock.synchronized {
    val spaceFreedByReleasingUnusedMemory = math.min(spaceToFree, memoryFree)
    val remainingSpaceToFree = spaceToFree - spaceFreedByReleasingUnusedMemory
    if (remainingSpaceToFree > 0) {
      // If reclaiming free memory did not adequately shrink the pool, begin evicting blocks:
      val spaceFreedByEviction =
        memoryStore.evictBlocksToFreeSpace(None, remainingSpaceToFree, memoryMode)
      // When a block is released, BlockManager.dropFromMemory() calls releaseMemory(), so we do
      // not need to decrement _memoryUsed here. However, we do need to decrement the pool size.
      spaceFreedByReleasingUnusedMemory + spaceFreedByEviction
    } else {
      spaceFreedByReleasingUnusedMemory
    }
  }

若storage空闲内存不足以所申请的内存,则需要通过释放storage中缓存的block来补充。

方法computeMaxExecutionPoolSize即计算的是execution拥有的最大可用内存。

接着通过这两个函数作为参数调用了方法executionPool.acquireMemory:

private[memory] def acquireMemory(
      numBytes: Long,
      taskAttemptId: Long,
      maybeGrowPool: Long => Unit = (additionalSpaceNeeded: Long) => Unit,
      computeMaxPoolSize: () => Long = () => poolSize): Long = lock.synchronized {
    assert(numBytes > 0, s"invalid number of bytes requested: $numBytes")

    // TODO: clean up this clunky method signature

    // Add this task to the taskMemory map just so we can keep an accurate count of the number
    // of active tasks, to let other tasks ramp down their memory in calls to `acquireMemory`
    if (!memoryForTask.contains(taskAttemptId)) {
      memoryForTask(taskAttemptId) = 0L
      // This will later cause waiting tasks to wake up and check numTasks again
      lock.notifyAll()
    }

    // Keep looping until we're either sure that we don't want to grant this request (because this
    // task would have more than 1 / numActiveTasks of the memory) or we have enough free
    // memory to give it (we always let each task get at least 1 / (2 * numActiveTasks)).
    // TODO: simplify this to limit each task to its own slot
    while (true) {
      val numActiveTasks = memoryForTask.keys.size
      val curMem = memoryForTask(taskAttemptId)

      // In every iteration of this loop, we should first try to reclaim any borrowed execution
      // space from storage. This is necessary because of the potential race condition where new
      // storage blocks may steal the free execution memory that this task was waiting for.
      maybeGrowPool(numBytes - memoryFree)

      // Maximum size the pool would have after potentially growing the pool.
      // This is used to compute the upper bound of how much memory each task can occupy. This
      // must take into account potential free memory as well as the amount this pool currently
      // occupies. Otherwise, we may run into SPARK-12155 where, in unified memory management,
      // we did not take into account space that could have been freed by evicting cached blocks.
      val maxPoolSize = computeMaxPoolSize()
      val maxMemoryPerTask = maxPoolSize / numActiveTasks
      val minMemoryPerTask = poolSize / (2 * numActiveTasks)

      // How much we can grant this task; keep its share within 0 <= X <= 1 / numActiveTasks
      val maxToGrant = math.min(numBytes, math.max(0, maxMemoryPerTask - curMem))
      // Only give it as much memory as is free, which might be none if it reached 1 / numTasks
      val toGrant = math.min(maxToGrant, memoryFree)

      // We want to let each task get at least 1 / (2 * numActiveTasks) before blocking;
      // if we can't give it this much now, wait for other tasks to free up memory
      // (this happens if older tasks allocated lots of memory before N grew)
      if (toGrant < numBytes && curMem + toGrant < minMemoryPerTask) {
        logInfo(s"TID $taskAttemptId waiting for at least 1/2N of $poolName pool to be free")
        lock.wait()
      } else {
        memoryForTask(taskAttemptId) += toGrant
        return toGrant
      }
    }
    0L  // Never reached
  }

里面定义了一个Task能使用的execution内存:

val maxPoolSize = computeMaxPoolSize()
      val maxMemoryPerTask = maxPoolSize / numActiveTasks
      val minMemoryPerTask = poolSize / (2 * numActiveTasks)

其中maxPoolSize 为从 storage 借用了内存后,executionMemoryPool 的最大可用内存,保证一个Task可用的内存在 1/2*numActiveTasks ~ 1/numActiveTasks 范围内,整体保证各个Task资源占用平衡。

向execution申请内存代码流程:

  1. 先获取Task目前已经分配到的内存。
  2. 当numBytes大于execution空闲内存,则会通过maybeGrowPool方法向storage借内存。
  3. 能获取的最大内存maxToGrant为numBytes和(maxMemoryPerTask - curMem)的较小值。
  4. 本次循环能获取真正的内存toGrant为maxToGrant和(execution向memory借用后可用的内存)的较小值。
  5. 若最终能申请的内存小于numBytes且申请的内存加上原来有的内存还不足以一个Task最小的使用内存minMemoryPerTask,则会阻塞,直到有足够的内存或者有新的Task进来减小了minMemoryPerTask的值。
    否则直接返回本次分配到的内存。

对于向storage和execution申请内存以及相互借用内存的方式至此讲解完成。用到storage和execution内存的地方很多(看概述),其中缓存rdd会向storage申请内存,运行Task会向execution申请内存,接下来分别看看是在什么时候申请的。

缓存 RDD

final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
    if (storageLevel != StorageLevel.NONE) {
      getOrCompute(split, context)
    } else {
      computeOrReadCheckpoint(split, context)
    }
  }

每个rdd分区的数据都是通过对应的迭代器得到,其中若存储级别不为NONE,则会先尝试从储存介质中(内存、磁盘文件等)获取,第一次获取当然都没有,只有先计算完缓存起来以供后续的计算直接获取。缓存序列化和非序列化的数据的缓存方式不一样,非序列化的缓存的代码是:

memoryStore.putIteratorAsValues(blockId, iterator(), classTag)
 private[storage] def putIteratorAsValues[T](
      blockId: BlockId,
      values: Iterator[T],
      classTag: ClassTag[T]): Either[PartiallyUnrolledIterator[T], Long] = {

    require(!contains(blockId), s"Block $blockId is already present in the MemoryStore")

    // Number of elements unrolled so far
    var elementsUnrolled = 0
    // Whether there is still enough memory for us to continue unrolling this block
    var keepUnrolling = true
    // Initial per-task memory to request for unrolling blocks (bytes).
    val initialMemoryThreshold = unrollMemoryThreshold
    // How often to check whether we need to request more memory
    val memoryCheckPeriod = 16
    // Memory currently reserved by this task for this particular unrolling operation
    var memoryThreshold = initialMemoryThreshold
    // Memory to request as a multiple of current vector size
    val memoryGrowthFactor = 1.5
    // Keep track of unroll memory used by this particular block / putIterator() operation
    var unrollMemoryUsedByThisBlock = 0L
    // Underlying vector for unrolling the block
    var vector = new SizeTrackingVector[T]()(classTag)

    // Request enough memory to begin unrolling
    keepUnrolling =
      reserveUnrollMemoryForThisTask(blockId, initialMemoryThreshold, MemoryMode.ON_HEAP)

    if (!keepUnrolling) {
      logWarning(s"Failed to reserve initial memory threshold of " +
        s"${Utils.bytesToString(initialMemoryThreshold)} for computing block $blockId in memory.")
    } else {
      unrollMemoryUsedByThisBlock += initialMemoryThreshold
    }

    // Unroll this block safely, checking whether we have exceeded our threshold periodically
    while (values.hasNext && keepUnrolling) {
      vector += values.next()
      if (elementsUnrolled % memoryCheckPeriod == 0) {
        // If our vector's size has exceeded the threshold, request more memory
        val currentSize = vector.estimateSize()
        if (currentSize >= memoryThreshold) {
          val amountToRequest = (currentSize * memoryGrowthFactor - memoryThreshold).toLong
          keepUnrolling =
            reserveUnrollMemoryForThisTask(blockId, amountToRequest, MemoryMode.ON_HEAP)
          if (keepUnrolling) {
            unrollMemoryUsedByThisBlock += amountToRequest
          }
          // New threshold is currentSize * memoryGrowthFactor
          memoryThreshold += amountToRequest
        }
      }
      elementsUnrolled += 1
    }

    if (keepUnrolling) {
      // We successfully unrolled the entirety of this block
      val arrayValues = vector.toArray
      vector = null
      val entry =
        new DeserializedMemoryEntry[T](arrayValues, SizeEstimator.estimate(arrayValues), classTag)
      val size = entry.size
      def transferUnrollToStorage(amount: Long): Unit = {
        // Synchronize so that transfer is atomic
        memoryManager.synchronized {
          releaseUnrollMemoryForThisTask(MemoryMode.ON_HEAP, amount)
          val success = memoryManager.acquireStorageMemory(blockId, amount, MemoryMode.ON_HEAP)
          assert(success, "transferring unroll memory to storage memory failed")
        }
      }
      // Acquire storage memory if necessary to store this block in memory.
      val enoughStorageMemory = {
        if (unrollMemoryUsedByThisBlock <= size) {
          val acquiredExtra =
            memoryManager.acquireStorageMemory(
              blockId, size - unrollMemoryUsedByThisBlock, MemoryMode.ON_HEAP)
          if (acquiredExtra) {
            transferUnrollToStorage(unrollMemoryUsedByThisBlock)
          }
          acquiredExtra
        } else { // unrollMemoryUsedByThisBlock > size
          // If this task attempt already owns more unroll memory than is necessary to store the
          // block, then release the extra memory that will not be used.
          val excessUnrollMemory = unrollMemoryUsedByThisBlock - size
          releaseUnrollMemoryForThisTask(MemoryMode.ON_HEAP, excessUnrollMemory)
          transferUnrollToStorage(size)
          true
        }
      }
      if (enoughStorageMemory) {
        entries.synchronized {
          entries.put(blockId, entry)
        }
        logInfo("Block %s stored as values in memory (estimated size %s, free %s)".format(
          blockId, Utils.bytesToString(size), Utils.bytesToString(maxMemory - blocksMemoryUsed)))
        Right(size)
      } else {
        assert(currentUnrollMemoryForThisTask >= unrollMemoryUsedByThisBlock,
          "released too much unroll memory")
        Left(new PartiallyUnrolledIterator(
          this,
          MemoryMode.ON_HEAP,
          unrollMemoryUsedByThisBlock,
          unrolled = arrayValues.toIterator,
          rest = Iterator.empty))
      }
    } else {
      // We ran out of space while unrolling the values for this block
      logUnrollFailureMessage(blockId, vector.estimateSize())
      Left(new PartiallyUnrolledIterator(
        this,
        MemoryMode.ON_HEAP,
        unrollMemoryUsedByThisBlock,
        unrolled = vector.iterator,
        rest = values))
    }
  }

代码太长了,我自己看到都头大了,没事,咱一点一点的慢慢来~

参数中的blockId是一个block的唯一标示,格式是"rdd_" + rddId + "_" + splitIndex,value就是该partition对应数据的迭代器,

  1. 通过reserveUnrollMemoryForThisTask方法向Storage申请initialMemoryThreshold(初始值可通过spark.storage.unrollMemoryThreshold配置,默认1M)的内存来unroll 迭代器:
    def reserveUnrollMemoryForThisTask(
      blockId: BlockId,
      memory: Long,
      memoryMode: MemoryMode): Boolean = {
    memoryManager.synchronized {
      val success = memoryManager.acquireUnrollMemory(blockId, memory, memoryMode)
      if (success) {
        val taskAttemptId = currentTaskAttemptId()
        val unrollMemoryMap = memoryMode match {
          case MemoryMode.ON_HEAP => onHeapUnrollMemoryMap
          case MemoryMode.OFF_HEAP => offHeapUnrollMemoryMap
        }
        unrollMemoryMap(taskAttemptId) = unrollMemoryMap.getOrElse(taskAttemptId, 0L) + memory
      }
      success
    }
    }
    
    跟进acquireUnrollMemory可看见底层调用的就是前面所讲的向storage申请内存的方法acquireStorageMemory,若申请成功则将对应的onHeapUnrollMemoryMap加上申请到的内存,即unroll使用的内存。
  2. 若申请成功则跟新unrollMemoryUsedByThisBlock的值,即在该block上unroll使用的内存。
  3. 接着进行遍历,停止遍历的条件有两个,一是迭代器全部遍历完,二是没有申请到内存。
    • 每迭代一条数据都会加到SizeTrackingVector类型的vector中(底层由数组实现),每迭代16次都会估算vector的大小是否超过了memoryThreshold(申请的内存)。
    • 若超过了memoryThreshold,则会计算再次申请内存的大小,1.5倍当前vector大小-已经申请到的内存大小。
    • 再次向Storage申请内存,若申请成功,则跟新unrollMemoryUsedByThisBlock,继续遍历进入下次循环,否则停止遍历。
  4. 循环结束后,若keepUnrolling 为 true,则说明values 一定被全部展开了;若为false,则没有全部被展开,说明没有申请到足够的内存来展开这个values,意味着该partition缓存到内存失败。
  5. 在values全部成功展开的前提下,会将vector构造成一个DeserializedMemoryEntry对象,其中包括数据的大小,接着会将展开后的数据大小和申请的内存大小作比较:
    • 若申请的内存比数据小,则再次向storage申请对应的大小,申请成功则将unroll使用的内存转化到storage中去,转化对应的逻辑是:释放掉该Task占用的所有unroll内存,又向storage申请对应的内存,其实unroll内存就是storage内存,即操作的都是storage的内存,减去某值又加上某值,结果没有变,但流程还得这么走,因为为了将 MemoryStore 和 MemoryManager 的解耦。
    • 若申请的内存比数据大,则释放掉对应的unroll内存,接着将unroll使用的内存转化到storage中去。
    • 最后将blockId和对应的entry加入到memorySore所管理的entries中去。

缓存序列化rdd支持 ON_HEAP 和 OFF_HEAP,和缓存非序列化rdd的方式类似,只是以流的形式写到bytebuffer中,其中
MemoryMode 如果是 ON_HEAP,这里的 ByteBuffer 是 HeapByteBuffer(堆上内存);而如果是 OFF_HEAP,这里的 ByteBuffer 则是 DirectByteBuffer(指向的是堆外内存)。最后根据数据构建成SerializedMemoryEntry来保存在memoryStore的entries中。

shuffle中execution内存的使用

在shuffle write的时候,并不会直接将数据写到磁盘(详情请看Shuffle Write解析),而是先写到一个集合中,此集合占用的内存就是execution内存,初始给的大小是5M,可通过spark.shuffle.spill.initialMemoryThreshold进行设置,每写一次数据就判断是否需要溢写到磁盘,溢写之前还尝试会向execution申请来避免溢写,代码如下:

protected def maybeSpill(collection: C, currentMemory: Long): Boolean = {
    var shouldSpill = false
    if (elementsRead % 32 == 0 && currentMemory >= myMemoryThreshold) {
      // Claim up to double our current memory from the shuffle memory pool
      val amountToRequest = 2 * currentMemory - myMemoryThreshold
      val granted = acquireMemory(amountToRequest)
      myMemoryThreshold += granted
      // If we were granted too little memory to grow further (either tryToAcquire returned 0,
      // or we already had more memory than myMemoryThreshold), spill the current collection
      shouldSpill = currentMemory >= myMemoryThreshold
    }
    shouldSpill = shouldSpill || _elementsRead > numElementsForceSpillThreshold
    // Actually spill
    if (shouldSpill) {
      _spillCount += 1
      logSpillage(currentMemory)
      spill(collection)
      _elementsRead = 0
      _memoryBytesSpilled += currentMemory
      releaseMemory()
    }
    shouldSpill
  }

当insert&update的次数是32的倍数且当前集合的大小已经大于等于了已经申请到的内存,此时会尝试向execution申请更多的内存来避免spill,申请的大小为2倍当前集合大小减去已经申请到的内存大小,跟进acquireMemory方法:

 public long acquireMemory(long size) {
    long granted = taskMemoryManager.acquireExecutionMemory(size, this);
    used += granted;
    return granted;
  }

这不就是我们前面讲的向execution申请内存的方法吗,这里就不再叙述。

参考

http://www.jianshu.com/p/999ef21dffe8

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