Spark持久化以及checkpoint剖析

在Spark 的持久化使用中，我们会将一些经常使用到的数据进行持久化，比如使用cache()或者persist()方法进行持久化操作，但是当某个节点或者executor挂掉之后，持久化的数据会丢失，因为我们的数据是保存在内存当中的，这时就会重新计算RDD，如果某个之前的RDD需要大量的计算时间，这时将会浪费很多时间，因此，我们有时候需要使用checkpoint操作来将一些数据持久化可容错文件系统中，比如HDFS文件系统中，虽然这种方式可能对性能带来了一定的影响（磁盘IO），但是为了避免大量的重复计算操作，有时也可以使用性能代价来换取时间效率上的提升。

当我们对某个RDD进行了缓存操作之后，首先会去CaacheManager中去找，然后紧接着去BlockManager中去获取内存或者磁盘中缓存的数据，如果没有进行缓存或者缓存丢失，那么就会去checkpoint的容错文件系统中查找数据，如果最终没有找到，那就会按照RDD lineage重新计算。

checkpoint原理

在代码中，当使用SparkContext可以设置一个checkpointFile文件目录，比如HDFS文件目录。
在代码中对需要checkpoint的RDD调用checkpoint方法。
RDDCheckpointData（spark内部的API），接管你的RDD，会标记为marked for checkpointing，准备进行checkpoint。
你的job运行完之后，会调用一个finalRDD.doCheckpoint()方法，会顺着rdd lineage，回溯扫描，发现有标记为待checkpoint的rdd，就会进行二次标记，标记为checkpointing in progress，正在接受checkpoint操作。
job执行完之后，就会启动一个内部的新job，去将标记为checkpointing in progress的rdd的数据，都写入hdfs文件中。（如果rdd之前cache过，会直接从缓存中获取数据，写入hdfs中；如果没有cache过，那么就会重新计算一遍这个rdd，再checkpoint）
将checkpoint过的rdd之前的依赖rdd，改成一个CheckpointRDD*，强制改变你的rdd的lineage。后面如果rdd的cache数据获取失败，直接会通过它的上游CheckpointRDD，去容错的文件系统，比如hdfs，中，获取checkpoint的数据。

RDDCheckpointData源码如下：

/**
 * Enumeration to manage state transitions of an RDD through checkpointing
 * [ Initialized --> marked for checkpointing --> checkpointing in progress --> checkpointed ]
 */
private[spark] object CheckpointState extends Enumeration {
  type CheckpointState = Value
  val Initialized, MarkedForCheckpoint, CheckpointingInProgress, Checkpointed = Value
}

/**
 * This class contains all the information related to RDD checkpointing. Each instance of this
 * class is associated with a RDD. It manages process of checkpointing of the associated RDD,
 * as well as, manages the post-checkpoint state by providing the updated partitions,
 * iterator and preferred locations of the checkpointed RDD.
 */
private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
  extends Logging with Serializable {

  import CheckpointState._

  // The checkpoint state of the associated RDD.
  var cpState = Initialized

  // The file to which the associated RDD has been checkpointed to
  @transient var cpFile: Option[String] = None

  // The CheckpointRDD created from the checkpoint file, that is, the new parent the associated RDD.
  var cpRDD: Option[RDD[T]] = None

  // Mark the RDD for checkpointing
  def markForCheckpoint() {
    RDDCheckpointData.synchronized {
      if (cpState == Initialized) cpState = MarkedForCheckpoint
    }
  }

  // Is the RDD already checkpointed
  def isCheckpointed: Boolean = {
    RDDCheckpointData.synchronized { cpState == Checkpointed }
  }

  // Get the file to which this RDD was checkpointed to as an Option
  def getCheckpointFile: Option[String] = {
    RDDCheckpointData.synchronized { cpFile }
  }

  // Do the checkpointing of the RDD. Called after the first job using that RDD is over.
  def doCheckpoint() {
    // If it is marked for checkpointing AND checkpointing is not already in progress,
    // then set it to be in progress, else return
    RDDCheckpointData.synchronized {
      if (cpState == MarkedForCheckpoint) {
        cpState = CheckpointingInProgress
      } else {
        return
      }
    }

    // Create the output path for the checkpoint
    val path = new Path(rdd.context.checkpointDir.get, "rdd-" + rdd.id)
    //获取checkpoint文件路径
    val fs = path.getFileSystem(rdd.context.hadoopConfiguration)
    if (!fs.mkdirs(path)) {
      throw new SparkException("Failed to create checkpoint path " + path)
    }

    // Save to file, and reload it as an RDD
    val broadcastedConf = rdd.context.broadcast(
      new SerializableWritable(rdd.context.hadoopConfiguration))
      //checkpoint数据到文件系统中
    rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _)
    val newRDD = new CheckpointRDD[T](rdd.context, path.toString)
    if (newRDD.partitions.size != rdd.partitions.size) {
      throw new SparkException(
        "Checkpoint RDD " + newRDD + "(" + newRDD.partitions.size + ") has different " +
          "number of partitions than original RDD " + rdd + "(" + rdd.partitions.size + ")")
    }

    // Change the dependencies and partitions of the RDD
    RDDCheckpointData.synchronized {
      cpFile = Some(path.toString)
      cpRDD = Some(newRDD)
      rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and partitions
      cpState = Checkpointed
    }
    logInfo("Done checkpointing RDD " + rdd.id + " to " + path + ", new parent is RDD " + newRDD.id)
  }

  // Get preferred location of a split after checkpointing
  def getPreferredLocations(split: Partition): Seq[String] = {
    RDDCheckpointData.synchronized {
      cpRDD.get.preferredLocations(split)
    }
  }

  def getPartitions: Array[Partition] = {
    RDDCheckpointData.synchronized {
      cpRDD.get.partitions
    }
  }

  def checkpointRDD: Option[RDD[T]] = {
    RDDCheckpointData.synchronized {
      cpRDD
    }
  }
}

在CheckPointRDD中写文件的操作如下：

def writeToFile[T: ClassTag](
      path: String,
      broadcastedConf: Broadcast[SerializableWritable[Configuration]],
      blockSize: Int = -1
    )(ctx: TaskContext, iterator: Iterator[T]) {
    val env = SparkEnv.get
    val outputDir = new Path(path)
    val fs = outputDir.getFileSystem(broadcastedConf.value.value)

    val finalOutputName = splitIdToFile(ctx.partitionId)
    val finalOutputPath = new Path(outputDir, finalOutputName)
    val tempOutputPath =
      new Path(outputDir, "." + finalOutputName + "-attempt-" + ctx.attemptNumber)

    if (fs.exists(tempOutputPath)) {
      throw new IOException("Checkpoint failed: temporary path " +
        tempOutputPath + " already exists")
    }
    val bufferSize = env.conf.getInt("spark.buffer.size", 65536)

    val fileOutputStream = if (blockSize < 0) {
      fs.create(tempOutputPath, false, bufferSize)
    } else {
      // This is mainly for testing purpose
      fs.create(tempOutputPath, false, bufferSize, fs.getDefaultReplication, blockSize)
    }
    val serializer = env.serializer.newInstance()
    val serializeStream = serializer.serializeStream(fileOutputStream)
    serializeStream.writeAll(iterator)
    serializeStream.close()

    if (!fs.rename(tempOutputPath, finalOutputPath)) {
      if (!fs.exists(finalOutputPath)) {
        logInfo("Deleting tempOutputPath " + tempOutputPath)
        fs.delete(tempOutputPath, false)
        throw new IOException("Checkpoint failed: failed to save output of task: "
          + ctx.attemptNumber + " and final output path does not exist")
      } else {
        // Some other copy of this task must've finished before us and renamed it
        logInfo("Final output path " + finalOutputPath + " already exists; not overwriting it")
        fs.delete(tempOutputPath, false)
      }
    }
  }

在RDD类的源码中，两个方法如下所示：

 /**
   * Performs the checkpointing of this RDD by saving this. It is called after a job using this RDD
   * has completed (therefore the RDD has been materialized and potentially stored in memory).
   * doCheckpoint() is called recursively on the parent RDDs.
   */
  private[spark] def doCheckpoint() {
    if (!doCheckpointCalled) {
      doCheckpointCalled = true
      if (checkpointData.isDefined) {
        checkpointData.get.doCheckpoint()
      } else {
        dependencies.foreach(_.rdd.doCheckpoint())
      }
    }
  }

  /**
   * Changes the dependencies of this RDD from its original parents to a new RDD (`newRDD`)
   * created from the checkpoint file, and forget its old dependencies and partitions.
   */
  private[spark] def markCheckpointed(checkpointRDD: RDD[_]) {
    clearDependencies()
    partitions_ = null
    deps = null    // Forget the constructor argument for dependencies too
  }

在我们的应用程序中，在使用checkpoint的时候只需要进行两步简单的操作即可，使用SparkContext设置一个checkPoint文件目录，在需要checkpoint的RDD中调用doCheckpoint方法即可。

Spark持久化以及checkpoint剖析

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