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:
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:
checkpoint 的机制保证了需要访问重复数据的应用 Spark 的DAG执行行图可能很庞大,task 中计算链可能会很长,这时如果 task 中途运行出错,那么 task 的整个需要重算非常耗时,因此,有必要将计算代价较大的 RDD checkpoint 一下,当下游 RDD 计算出错时,可以直接从 checkpoint 过的 RDD 那里读取数据继续算。
- 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)
}
}
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runJob
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()
}
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)
}
}
- 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。
参考:https://www.coderfei.com/2018/02/11/spark-6-spark-rdd-cache-checkpoint.html
https://www.jianshu.com/p/653ebabc8f87
2019-02-26 20:25 流氓小伙子