RDD的checkpoint源码分析

当调用RDD#checkpoint的，checkpoint的方法如下：

  /**
   * Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint
   * directory set with `SparkContext#setCheckpointDir` and all references to its parent
   * RDDs will be removed. This function must be called before any job has been
   * executed on this RDD. It is strongly recommended that this RDD is persisted in
   * memory, otherwise saving it on a file will require recomputation.
   */
  def checkpoint(): Unit = RDDCheckpointData.synchronized {
    // NOTE: we use a global lock here due to complexities downstream with ensuring
    // children RDD partitions point to the correct parent partitions. In the future
    // we should revisit this consideration.
    if (context.checkpointDir.isEmpty) {
      throw new SparkException("Checkpoint directory has not been set in the SparkContext")
    } else if (checkpointData.isEmpty) {
     //最后生成一个新的ReliableRDDCheckpointData，checkpoint的逻辑主要体现在 ReliableRDDCheckpointData#doCheckpoint函数中。
      checkpointData = Some(new ReliableRDDCheckpointData(this))
    }
  }

从注释上看，只是将此rdd标示为要checkpoint，文件保存在SparkContext#setCheckpointDir定义的目录，并且此rdd所有的父依赖将移除。
此函数一定要在所有job运行之前被执行。强烈建议把这个RDD进行persisted，否则的话数据进将行重新计算。

   /**
   * Materialize this RDD and write its content to a reliable DFS.
   * This is called immediately after the first action invoked on this RDD has completed.
   */
  protected override def doCheckpoint(): CheckpointRDD[T] = {
  //核心代码，将文件写入到目录
    val newRDD = ReliableCheckpointRDD.writeRDDToCheckpointDirectory(rdd, cpDir)

    // Optionally clean our checkpoint files if the reference is out of scope
    if (rdd.conf.getBoolean("spark.cleaner.referenceTracking.cleanCheckpoints", false)) {
      rdd.context.cleaner.foreach { cleaner =>
        cleaner.registerRDDCheckpointDataForCleanup(newRDD, rdd.id)
      }
    }

    logInfo(s"Done checkpointing RDD ${rdd.id} to $cpDir, new parent is RDD ${newRDD.id}")
    newRDD
  }

     /**
   * Write RDD to checkpoint files and return a ReliableCheckpointRDD representing the RDD.
   */
  def writeRDDToCheckpointDirectory[T: ClassTag](
      originalRDD: RDD[T],
      checkpointDir: String,
      blockSize: Int = -1): ReliableCheckpointRDD[T] = {

    val sc = originalRDD.sparkContext

    // Create the output path for the checkpoint
    val checkpointDirPath = new Path(checkpointDir)
    val fs = checkpointDirPath.getFileSystem(sc.hadoopConfiguration)
    if (!fs.mkdirs(checkpointDirPath)) {
      throw new SparkException(s"Failed to create checkpoint path $checkpointDirPath")
    }

    // Save to file, and reload it as an RDD
    val broadcastedConf = sc.broadcast(
      new SerializableConfiguration(sc.hadoopConfiguration))
    // TODO: This is expensive because it computes the RDD again unnecessarily (SPARK-8582)
    //核心代码
    sc.runJob(originalRDD,
      writePartitionToCheckpointFile[T](checkpointDirPath.toString, broadcastedConf) _)

    if (originalRDD.partitioner.nonEmpty) {
      writePartitionerToCheckpointDir(sc, originalRDD.partitioner.get, checkpointDirPath)
    }

    val newRDD = new ReliableCheckpointRDD[T](
      sc, checkpointDirPath.toString, originalRDD.partitioner)
    if (newRDD.partitions.length != originalRDD.partitions.length) {
      throw new SparkException(
        s"Checkpoint RDD $newRDD(${newRDD.partitions.length}) has different " +
          s"number of partitions from original RDD $originalRDD(${originalRDD.partitions.length})")
    }
    newRDD
  }

第23行代码，用到了柯里化的小技巧，我们把方法稍作修改

      // TODO: This is expensive because it computes the RDD again unnecessarily (SPARK-8582)
    sc.runJob(originalRDD,
      writePartitionToCheckpointFile[T](checkpointDirPath.toString, broadcastedConf) _)

      // TODO: This is expensive because it computes the RDD again unnecessarily (SPARK-8582)
    val func : (TaskContext, Iterator[T]) => Unit = writePartitionToCheckpointFile[T](checkpointDirPath.toString, broadcastedConf)
    sc.runJob(originalRDD,func)

此处新提交一个Job，也是对RDD进行计算，那么如果原有的RDD对结果进行了cache的话，那么是不是减少了很多的计算呢，这就是为啥checkpoint的时候强烈推荐进行cache的缘故。

写文件的逻辑

  /**
   * Write a RDD partition's data to a checkpoint file.
   */
  def writePartitionToCheckpointFile[T: ClassTag](
      path: String,
      broadcastedConf: Broadcast[SerializableConfiguration],
      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 = ReliableCheckpointRDD.checkpointFileName(ctx.partitionId())
    val finalOutputPath = new Path(outputDir, finalOutputName)
    val tempOutputPath =
      new Path(outputDir, s".$finalOutputName-attempt-${ctx.attemptNumber()}")

    if (fs.exists(tempOutputPath)) {
      throw new IOException(s"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)
    Utils.tryWithSafeFinally {
      serializeStream.writeAll(iterator)
    } {
      serializeStream.close()
    }

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

  核心代码

      val serializer = env.serializer.newInstance()
    val serializeStream = serializer.serializeStream(fileOutputStream)
    Utils.tryWithSafeFinally {
      serializeStream.writeAll(iterator)
    } {
      serializeStream.close()
    }

    //把iterator返回的结果写到指定目录中。文件命为
    ReliableCheckpointRDD.checkpointFileName(ctx.partitionId())

我们看下定义

      /**
   * Return the checkpoint file name for the given partition.
   */
  private def checkpointFileName(partitionIndex: Int): String = {
    "part-%05d".format(partitionIndex)
  }

这个再RDD恢复的时候会用到这个文件名。在下一篇博客中我将写如何恢复。

以上是我们讲述了checkpoint的流程，那么checkpoint是如何启动的呢？
答案在SparkContext#runJob方法

  /**
   * Run a function on a given set of partitions in an RDD and pass the results to the given
   * handler function. This is the main entry point for all actions in Spark.
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      resultHandler: (Int, U) => Unit): Unit = {
    if (stopped.get()) {
      throw new IllegalStateException("SparkContext has been shutdown")
    }
    val callSite = getCallSite
    val cleanedFunc = clean(func)
    logInfo("Starting job: " + callSite.shortForm)
    if (conf.getBoolean("spark.logLineage", false)) {
      logInfo("RDD's recursive dependencies:
" + rdd.toDebugString)
    }
    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
    progressBar.foreach(_.finishAll())
    rdd.doCheckpoint()
  }

  我们看最后三行，先提交真正我们需要计算的job，然后才是 rdd.doCheckpoint()

    /**
   * 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(): Unit = {
    RDDOperationScope.withScope(sc, "checkpoint", allowNesting = false, ignoreParent = true) {
      if (!doCheckpointCalled) {
        doCheckpointCalled = true
        if (checkpointData.isDefined) {
          checkpointData.get.checkpoint()
        } else {
          dependencies.foreach(_.rdd.doCheckpoint())
        }
      }
    }

从注释上看，此函数是在使用此RDD的的job执行结束后执行，因此结果可能会保存在内存中，这就是提到过的最好对RDD进行cache的缘故。重要的事要说三遍。

最后调用的方法就是 checkpointData.get.checkpoint()

到此为止RDD如何进行checkpoint算是分析完成了。