Spark中对于数据的保存除了持久化操作之外，还提供了一种检查点的机制，检查点（本质是通过将RDD写入Disk做检查点）是为了通过lineage（血统）做容错的辅助，lineage过长会造成容错成本过高，这样就不如在中间阶段做检查点容错，如果之后有节点出现问题而丢失分区，从做检查点的RDD开始重做Lineage，就会减少开销。检查点通过将数据写入到HDFS文件系统实现了RDD的检查点功能。
cache和checkpoint的区别：
缓存（cache）把 RDD 计算出来然后放在内存中，但是RDD 的依赖链（相当于数据库中的redo 日志），也不能丢掉，当某个点某个 executor 宕了，上面cache 的RDD就会丢掉，需要通过依赖链重放计算出来。不同的是，checkpoint是把 RDD 保存在 HDFS中， 是多副本可靠存储，所以依赖链就可以丢掉了，就斩断了依赖链， 是通过复制实现的高容错。
如果存在以下场景，则比较适合使用检查点机制：
1) DAG中的Lineage过长，如果重算，则开销太大（如在PageRank中）。
2) 在宽依赖上做Checkpoint获得的收益更大。
为当前RDD设置检查点。该函数将会创建一个二进制的文件，并存储到checkpoint目录中，该目录是用SparkContext.setCheckpointDir()设置的。在checkpoint的过程中，该RDD的所有依赖于父RDD中的信息将全部被移出。对RDD进行checkpoint操作并不会马上被执行，必须执行Action操作才能触发。
checkpoint写流程
RDD checkpoint 过程中会经过以下几个状态：
[ Initialized → marked for checkpointing → checkpointing in progress → checkpointed ]
转换流程如下：
RDD 需要经过 [ Initialized --> marked for checkpointing --> checkpointing in progress --> checkpointed ] 这几个阶段才能被 checkpoint。
Initialized： 首先 driver program 需要使用 rdd.checkpoint() 去设定哪些 rdd 需要 checkpoint，设定后，该 rdd 就接受 RDDCheckpointData 管理。用户还要设定 checkpoint 的存储路径，一般在 HDFS 上。
marked for checkpointing：初始化后，RDDCheckpointData 会将 rdd 标记为 MarkedForCheckpoint，这时候标记为 Initialized 状态。
checkpointing in progress：每个 job 运行结束后会调用 finalRdd.doCheckpoint()，finalRdd 会顺着 computing chain 回溯扫描，碰到要 checkpoint 的 RDD 就将其标记为 CheckpointingInProgress，然后将写磁盘（比如写 HDFS）需要的配置文件（如 core-site.xml 等）broadcast 到其他 worker 节点上的 blockManager。完成以后，启动一个 job 来完成 checkpoint（使用 rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path.toString, broadcastedConf))）。
checkpointed：job 完成 checkpoint 后，将该 rdd 的 dependency 全部清掉， 怎么清除依赖的呢， 就是把RDD 变量的强引用设置为 null，垃圾回收了，会触发 ContextCleaner 里面的监听，清除实际 BlockManager 缓存中的数据。并设定该 rdd 状态为 checkpointed。然后，为该 rdd 强加一个依赖，设置该 rdd 的 parent rdd 为 CheckpointRDD，该 CheckpointRDD 负责以后读取在文件系统上的 checkpoint 文件，生成该 rdd 的 partition。
checkpoint读流程
在 runJob() 的时候会先调用 finalRDD 的 partitions() 来确定最后会有多个 task。rdd.partitions() 会去检查（通过 RDDCheckpointData 去检查，因为它负责管理被 checkpoint 过的 rdd）该 rdd 是会否被 checkpoint 过了，如果该 rdd 已经被 checkpoint 过了，直接返回该 rdd 的 partitions 也就是 Array[Partition]。
当调用 rdd.iterator() 去计算该 rdd 的 partition 的时候，会调用 computeOrReadCheckpoint(split: Partition) 去查看该 rdd 是否被 checkpoint 过了，如果是，就调用该 rdd 的 parent rdd 的 iterator() 也就是 CheckpointRDD.iterator()，CheckpointRDD 负责读取文件系统上的文件，生成该 rdd 的 partition。这就解释了为什么那么 trickly 地为 checkpointed rdd 添加一个 parent CheckpointRDD。
总结：
- 下面来看一个关于checkpoint的例子：
object testCheckpoint { def main(args: Array[String]): Unit = { val sc =new SparkContext(new SparkConf().setAppName("testCheckpoint").setMaster("local[*]")) //设置检查点目录 sc.setCheckpointDir("file:///f:/spark/checkpoint") val rdd=sc.textFile("file:///F:/spark/b.txt").flatMap{line=>line.split(" ")}.map(word=>(word,1)).reduceByKey(_+_) rdd.checkpoint() //rdd.count() rdd.groupBy(x=>x._2).collect().foreach(println) } }
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checkpoint流程分析
checkpoint初始化
我们可以看到最先调用了
SparkContext的setCheckpointDir设置了一个checkpoint 目录
我们跟进这个方法看一下/** * Set the directory under which RDDs are going to be checkpointed. The directory must * be a HDFS path if running on a cluster. */ def setCheckpointDir(directory: String) { // If we are running on a cluster, log a warning if the directory is local. // Otherwise, the driver may attempt to reconstruct the checkpointed RDD from // its own local file system, which is incorrect because the checkpoint files // are actually on the executor machines. 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 } }
这个方法挺简单的，就创建了一个目录，接下来我们看RDD核心的
checkpoint方法，跟进去/** * 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) { checkpointData = Some(new ReliableRDDCheckpointData(this)) } }
这个方法没有返回值，逻辑只有一个判断，
checkpointDir刚才设置过了，不为空，然后创建了一个ReliableRDDCheckpointData,我们来看ReliableRDDCheckpointData/** * An implementation of checkpointing that writes the RDD data to reliable storage. * This allows drivers to be restarted on failure with previously computed state. */ private[spark] class ReliableRDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) extends RDDCheckpointData[T](rdd) with Logging { 。。。。。 }
这个
ReliableRDDCheckpointData的父类RDDCheckpointData我们再继续看它的父类/** * RDD 需要经过 * [ Initialized --> CheckpointingInProgress--> Checkpointed ] * 这几个阶段才能被 checkpoint。 */ private[spark] object CheckpointState extends Enumeration { type CheckpointState = Value val Initialized, CheckpointingInProgress, Checkpointed = Value } private[spark] abstract class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) extends Serializable { import CheckpointState._ // The checkpoint state of the associated RDD. protected var cpState = Initialized 。。。。。。 }
RDD 需要经过
[ Initialized --> CheckpointingInProgress--> Checkpointed ]
这几个阶段才能被 checkpoint。
这类里面有一个枚举来标识CheckPoint的状态，第一次初始化时是Initialized。
checkpoint这个一步已经完成了，回到我们的RDD成员变量里checkpointData这个变量指向的RDDCheckpointData的实例。
checkpoint什么时候写入数据
- 我们知道一个spark job运行最终会调用
SparkContext的runJob方法将任务提交给Executor去执行，我们来看runJobdef 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() }
最后一行代码调用了
doCheckpoint，在dagScheduler将任务提交给集群运行之后，我来看这个doCheckpoint方法/** * 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) { if (checkpointAllMarkedAncestors) { // TODO We can collect all the RDDs that needs to be checkpointed, and then checkpoint // them in parallel. // Checkpoint parents first because our lineage will be truncated after we // checkpoint ourselves dependencies.foreach(_.rdd.doCheckpoint()) } checkpointData.get.checkpoint() } else { dependencies.foreach(_.rdd.doCheckpoint()) } } } }
这个是一个递归，遍历RDD依赖链条，当rdd是checkpointData不为空时，调用checkpointData的checkpoint()方法。还记得checkpointData类型是什么吗？就是RDDCheckpointData，我们来看它的checkpoint方法，以下/** * Materialize this RDD and persist its content. * This is called immediately after the first action invoked on this RDD has completed. */ 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) {
//标记当前状态为 CheckpointingInProgress cpState = CheckpointingInProgress } else { return } } //这里调用的是子类的 doCheckPoint() val newRDD = doCheckpoint() // Update our state and truncate the RDD lineage RDDCheckpointData.synchronized { cpRDD = Some(newRDD) cpState = Checkpointed rdd.markCheckpointed() } }这个方法开始做checkpoint操作了。
checkpoint什么时候读取数据
- 我们知道Task是spark运行任务的最小单元，当Task执行失败的时候spark会重新计算，这里Task进行计算的地方就是读取checkpoint的入口。我们可以看一下
ShuffleMapTask里的计算方法runTask,如下override def runTask(context: TaskContext): MapStatus = { // Deserialize the RDD using the broadcast variable. val threadMXBean = ManagementFactory.getThreadMXBean val deserializeStartTime = System.currentTimeMillis() val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) { threadMXBean.getCurrentThreadCpuTime } else 0L val ser = SparkEnv.get.closureSerializer.newInstance() val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])]( ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader) _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime _executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) { threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime } else 0L var writer: ShuffleWriter[Any, Any] = null try { val manager = SparkEnv.get.shuffleManager writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context) writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]]) writer.stop(success = true).get } catch { case e: Exception => try { if (writer != null) { writer.stop(success = false) } } catch { case e: Exception => log.debug("Could not stop writer", e) } throw e } }
这是spark真正调用计算方法的逻辑
runTask调用rdd.iterator()去计算该 rdd 的 partition 的，我们来看RDD的iterator()/** * Internal method to this RDD; will read from cache if applicable, or otherwise compute it. * This should ''not'' be called by users directly, but is available for implementors of custom * subclasses of RDD. */ final def iterator(split: Partition, context: TaskContext): Iterator[T] = { if (storageLevel != StorageLevel.NONE) { getOrCompute(split, context) } else { computeOrReadCheckpoint(split, context) } }
- 我们知道Task是spark运行任务的最小单元，当Task执行失败的时候spark会重新计算，这里Task进行计算的地方就是读取checkpoint的入口。我们可以看一下
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- 这里会继续调用
computeOrReadCheckpoint,我们看该方法/** * Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing. */ private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] = { if (isCheckpointedAndMaterialized) { firstParent[T].iterator(split, context) } else { compute(split, context) } }
当调用rdd.iterator()去计算该 rdd 的 partition 的时候，会调用computeOrReadCheckpoint(split: Partition)去查看该 rdd 是否被 checkpoint 过了，如果是，就调用该 rdd 的 parent rdd 的 iterator() 也就是 CheckpointRDD.iterator()，否则直接调用该RDD的compute, 那么我们就跟进CheckpointRDD的compute/** * Read the content of the checkpoint file associated with the given partition. */ override def compute(split: Partition, context: TaskContext): Iterator[T] = { val file = new Path(checkpointPath, ReliableCheckpointRDD.checkpointFileName(split.index)) ReliableCheckpointRDD.readCheckpointFile(file, broadcastedConf, context) }
这里就两行代码，意思是从Path上读取我们的CheckPoint数据，看一下
readCheckpointFile/** * Read the content of the specified checkpoint file. */ def readCheckpointFile[T]( path: Path, broadcastedConf: Broadcast[SerializableConfiguration], context: TaskContext): Iterator[T] = { val env = SparkEnv.get val fs = path.getFileSystem(broadcastedConf.value.value) val bufferSize = env.conf.getInt("spark.buffer.size", 65536) val fileInputStream = fs.open(path, bufferSize) val serializer = env.serializer.newInstance() val deserializeStream = serializer.deserializeStream(fileInputStream) // Register an on-task-completion callback to close the input stream. context.addTaskCompletionListener(context => deserializeStream.close()) deserializeStream.asIterator.asInstanceOf[Iterator[T]] }
CheckpointRDD负责读取文件系统上的文件，生成该 rdd 的 partition。这就解释了为什么要为调用了checkpoint的RDD 添加一个parent CheckpointRDD的原因。
到此，整个checkpoint的流程就结束了。
- 这里会继续调用
参考：https://www.coderfei.com/2018/02/11/spark-6-spark-rdd-cache-checkpoint.html
https://www.jianshu.com/p/653ebabc8f87