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。
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