前言
在spark应用程序中，常常会遇到运算量很大经过很复杂的 Transformation才能得到的RDD即Lineage链较长、宽依赖的RDD，此时我们可以考虑将这个RDD持久化。
cache也是可以持久化到磁盘，只不过是直接将partition的输出数据写到磁盘，而checkpoint是在逻辑job完成后，若有需要checkpoint的RDD，再单独启动一个job去完成checkpoint，这样该RDD就被计算了两次，所以建议在有checkpoint的时候先将该RDD cache到内存，到时候直接写到磁盘就行了。
checkpoint的实现
需要使用checkpoint都需要通过sparkcontext的setCheckpointDir方法设置一个目录以存checkpoint的各种信息数据，下面我们来看看该方法：
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必须是HDFS的目录；在该目录下创建一个以UUID生成的一个唯一的目录名的目录。
通过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))
}
}先判断是否设置了checkpointDir，再判断checkpointData.isEmpty是否成立，checkpointData的定义是这样的：
private[spark] var checkpointData: Option[RDDCheckpointData[T]] = None
RDDCheckpointData和RDD一一对应，保存着和checkpoint相关的信息。这里通过new ReliableRDDCheckpointData(this)实例化了checkpointData ，ReliableRDDCheckpointData是其子类，这里相当于是checkpoint的一个标记，并没有真正执行checkpoint。
什么时候checkpoint
在有action动作时，会触发sparkcontext对runJob的调用：
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()
}我们可以看到在执行完job后会执行 rdd.doCheckpoint()，这里就是对前面标记了的RDD的checkpoint，我们继续看这个方法：
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())
}
}
}
}先判断是否已经被处理过checkpoint，没有才执行，并将doCheckpointCalled 设为true，因为前面已经初始化过了checkpointData，所以checkpointData.isDefined也满足，若想要把checkpointData定义过的RDD的parents也进行checkpoint的话，那么我们需要先对parents checkpoint。因为，如果RDD把自己checkpoint了，那么它就将lineage中它的parents给切除了。继续跟进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()
}
}先将checkpoint的状态改为CheckpointingInProgress，再执行doCheckpoint，返回一个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)，将一个RDD写入到多个checkpoint文件，并返回一个ReliableCheckpointRDD来代表这个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
}获取一些配置信息广播输出等操作，然后启动一个Job去写Checkpint文件，主要由ReliableCheckpointRDD.writeCheckpointFile来实现写操作，写完checkpoint后new一个ReliableCheckpointRDD实例返回，看看具体的writePartitionToCheckpointFile实现：
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}")
}
}
}
}这里的代码就是普通的对HDFS写文件的操作，将一个RDD partition的数据写到checkpoint目录下。
doCheckpoint()操作已经完成，返回了一个new RDD:ReliableCheckpointRDD引用给cpRDD，接着标记checkpoint的状态为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的所有依赖。
写checkpoint总结
Initialized
marked for checkpointing
checkpointing in progress
checkpointed
什么时候读checkpoint
在需要读取一个partition的数据时，会通过rdd.iterator() 去计算该 rdd 的 partition 的，我们来看RDD的iterator()实现：
final def iterator(split: Partition, context: TaskContext): Iterator[T] = { if (storageLevel != StorageLevel.NONE) {
getOrCompute(split, context)
} else {
computeOrReadCheckpoint(split, context)
}
}在cache中没有读到数据时再判断该RDD是否被checkpoint过，isCheckpointedAndMaterialized就是在checkpoint成功时的一个状态标记：cpState = Checkpointed。
private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] =
{ if (isCheckpointedAndMaterialized) {
firstParent[T].iterator(split, context)
} else {
compute(split, context)
}
}当该RDD被成功checkpoint了，直接使用parent rdd 的 iterator() 也就是 CheckpointRDD.iterator()，否则直接调用该RDD的compute方法。
final def dependencies: Seq[Dependency[_]] = {
checkpointRDD.map(r => List(new OneToOneDependency(r))).getOrElse { if (dependencies_ == null) {
dependencies_ = getDependencies
}
dependencies_
}
}获取RDD的依赖时，会先尝试从checkpointRDD中获取依赖，若成功则返回被OneToOneDependency包装过的ReliableCheckpointRDD对象，否则获取真正的依赖。
作者：BIGUFO
链接：https://www.jianshu.com/p/4e636e575b8a
随时随地看视频
