前言

在spark應用程序中，常常會遇到運算量很大經過很復雜的 Transformation才能得到的RDD即Lineage鏈較長、寬依賴的RDD，此時我們可以考慮將這個RDD持久化。

cache也是可以持久化到磁盤，只不過是直接將partition的輸出數據寫到磁盤，而checkpoint是在邏輯job完成后，若有需要checkpoint的RDD，再單獨啟動一個job去完成checkpoint，這樣該RDD就被計算了兩次，所以建議在有checkpoint的時候先將該RDD cache到內存，到時候直接寫到磁盤就行了。

checkpoint的實現

需要使用checkpoint都需要通過sparkcontext的setCheckpointDir方法設置一個目錄以存checkpoint的各種信息數據，下面我們來看看該方法：

def setCheckpointDir(directory: String) {
    if (!isLocal && Utils.nonLocalPaths(directory).isEmpty) {
      logWarning("Spark is not running in local mode, therefore the checkpoint directory " +
        s"must not be on the local filesystem. Directory '$directory' " +
        "appears to be on the local filesystem.")
    }
    checkpointDir = Option(directory).map { dir =>
      val path = new Path(dir, UUID.randomUUID().toString)
      val fs = path.getFileSystem(hadoopConfiguration)
      fs.mkdirs(path)
      fs.getFileStatus(path).getPath.toString
    }
  }

在非local模式下，directory必須是HDFS的目錄；在該目錄下創建一個以UUID生成的一個唯一的目錄名的目錄。
通過rdd.checkpoint()即可checkpoint此RDD

def checkpoint(): Unit = RDDCheckpointData.synchronized { 
    if (context.checkpointDir.isEmpty) {
      throw new SparkException("Checkpoint directory has not been set in the SparkContext")
    } else if (checkpointData.isEmpty) {
      checkpointData = Some(new ReliableRDDCheckpointData(this))
    }
  }

先判斷是否設置了checkpointDir，再判斷checkpointData.isEmpty是否成立，checkpointData的定義是這樣的：

private[spark] var checkpointData: Option[RDDCheckpointData[T]] = None

RDDCheckpointData和RDD一一對應，保存著和checkpoint相關的信息。這里通過new ReliableRDDCheckpointData(this)實例化了checkpointData ，ReliableRDDCheckpointData是其子類，這里相當于是checkpoint的一個標記，并沒有真正執行checkpoint。

什么時候checkpoint

在有action動作時，會觸發sparkcontext對runJob的調用：

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:\n" + rdd.toDebugString)
    }
    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
    progressBar.foreach(_.finishAll())
    rdd.doCheckpoint()
  }

我們可以看到在執行完job后會執行 rdd.doCheckpoint()，這里就是對前面標記了的RDD的checkpoint，我們繼續看這個方法：

private[spark] def doCheckpoint(): Unit = {
    RDDOperationScope.withScope(sc, "checkpoint", allowNesting = false, ignoreParent = true) {
      if (!doCheckpointCalled) {
        doCheckpointCalled = true
        if (checkpointData.isDefined) {
          if (checkpointAllMarkedAncestors) {
              dependencies.foreach(_.rdd.doCheckpoint())
          }
          checkpointData.get.checkpoint()
        } else {
      dependencies.foreach(_.rdd.doCheckpoint())
        }
      }
    }
  }

先判斷是否已經被處理過checkpoint，沒有才執行，并將doCheckpointCalled 設為true，因為前面已經初始化過了checkpointData，所以checkpointData.isDefined也滿足，若想要把checkpointData定義過的RDD的parents也進行checkpoint的話，那么我們需要先對parents checkpoint。因為，如果RDD把自己checkpoint了，那么它就將lineage中它的parents給切除了。繼續跟進checkpointData.get.checkpoint()

final def checkpoint(): Unit = {
    // Guard against multiple threads checkpointing the same RDD by
    // atomically flipping the state of this RDDCheckpointData
    RDDCheckpointData.synchronized {
      if (cpState == Initialized) {
        cpState = CheckpointingInProgress
      } else {
        return
      }
    }

    val newRDD = doCheckpoint()

    // Update our state and truncate the RDD lineage
    RDDCheckpointData.synchronized {
      cpRDD = Some(newRDD)
      cpState = Checkpointed
      rdd.markCheckpointed()
    }
  }

先將checkpoint的狀態改為CheckpointingInProgress，再執行doCheckpoint，返回一個newRDD，看doCheckpoint做了什么：

protected override def doCheckpoint(): CheckpointRDD[T] = {
    val newRDD = ReliableCheckpointRDD.writeRDDToCheckpointDirectory(rdd, cpDir)
    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
  }

ReliableCheckpointRDD.writeRDDToCheckpointDirectory(rdd, cpDir)，將一個RDD寫入到多個checkpoint文件，并返回一個ReliableCheckpointRDD來代表這個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)
    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
  }

獲取一些配置信息廣播輸出等操作，然后啟動一個Job去寫Checkpint文件，主要由ReliableCheckpointRDD.writeCheckpointFile來實現寫操作，寫完checkpoint后new一個ReliableCheckpointRDD實例返回，看看具體的writePartitionToCheckpointFile實現：

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(fs.getWorkingDirectory), 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}")
        }
      }
    }
  }

這里的代碼就是普通的對HDFS寫文件的操作，將一個RDD partition的數據寫到checkpoint目錄下。

doCheckpoint()操作已經完成，返回了一個new RDD:ReliableCheckpointRDD引用給cpRDD，接著標記checkpoint的狀態為Checkpointed，rdd.markCheckpointed()干了什么呢?

private[spark] def markCheckpointed(): Unit = {
    clearDependencies()
    partitions_ = null
    deps = null    // Forget the constructor argument for dependencies too
  }

最后再清除RDD的所有依賴。

寫checkpoint總結

• Initialized
• marked for checkpointing
• checkpointing in progress
• checkpointed

什么時候讀checkpoint

在需要讀取一個partition的數據時，會通過rdd.iterator() 去計算該 rdd 的 partition 的，我們來看RDD的iterator()實現：

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

在cache中沒有讀到數據時再判斷該RDD是否被checkpoint過，isCheckpointedAndMaterialized就是在checkpoint成功時的一個狀態標記：cpState = Checkpointed。

private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] =
  {
    if (isCheckpointedAndMaterialized) {
      firstParent[T].iterator(split, context)
    } else {
      compute(split, context)
    }
  }

當該RDD被成功checkpoint了，直接使用parent rdd 的 iterator() 也就是 CheckpointRDD.iterator()，否則直接調用該RDD的compute方法。

final def dependencies: Seq[Dependency[_]] = {
    checkpointRDD.map(r => List(new OneToOneDependency(r))).getOrElse {
      if (dependencies_ == null) {
        dependencies_ = getDependencies
      }
      dependencies_
    }
  }

獲取RDD的依賴時，會先嘗試從checkpointRDD中獲取依賴，若成功則返回被OneToOneDependency包裝過的ReliableCheckpointRDD對象，否則獲取真正的依賴。