checkpoint
1、checkpoint重大价值
2、Checkpoint运行原理图
3、Checkpoint源码解析
一、Checkpoint到底是什么？
1、Spark在生产环境下经常会面临Transformation的RDD非常多（例如一个Job中包含1万个RDD）或者具体Transformation产生的RDD本身计算特别复杂和耗时（例如计算时常超过1个小时），此时我们必须考虑对计算结果数据的持久化。
2、Spark擅长多步骤迭代，同时擅长基于Job的复用，这时如果能够对曾经计算的过程产生的数据进行复用，就可以极大的提升效率；
3、如果采用persist把数据放在内存中的话，虽然是最快速的但是也是最不可靠的；如果放在磁盘上也不是完全可靠的，例如磁盘会损坏，管理员可能清空磁盘等。
4、Checkpoint的产生就是为了相对而言更加可靠的持久化数据，在Checkpoint可以指定把数据放在本地并且是多副本的方式，但是在正常的生产环境下是放在HDFS上，这就天然的借助了HDFS高容错高可靠的特征来完成了最大化的可靠的持久化数据的方式；
5、Checkpoint是为了最大程度保证绝对可靠的复用RDD计算数据的Spark高级功能。通过Checkpoint，我们通常把数据持久化到HDFS来保证数据最大程度的安全性；
6、Checkpoint就是针对整个RDD计算链条中特别需要数据持久化的环节（后面会反复使用当前环节的RDD）开启基于HDFS等的数据持久化复用策略，通过对RDD启动Checkpoint机制来实现容错和高可用；
二、Checkpoint原理机制
1、通过调用Sparkcontext.setCheckpointDir方法来指定进行Checkpoint操作的RDD把数据放在哪里，在生产集群中是放在HDFS上的。同时为了提高效率，在进行Checkpoint的时候可以指定多个目录
/**
* 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("Checkpoint directory must be non-local " +
"if Spark is running on a cluster: " + directory)
}
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
}
}
2、在进行RDD的checkpoint的时候其所依赖的所有的RDD都会从计算链条中清空掉
/**
* 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))
}
}
3、作为最佳实践，一般在进行checkpoint方法调用前通常都要进行persist来把当前RDD的数据持久化到内存或者磁盘上，这是因为checkpoint是lazy级别，必须有Job的执行且在Job执行完成后才会从后往前回溯哪个RDD进行了Checkpoint标记然后对该标记了要进行Checkpoint的RDD新启动一个Job执行具体的Checkpoint的过程。
4、Checkpoint改变了RDD的Lineage
/**
* 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))
}
}
5、当我们调用了checkpoint方法要对RDD进行Checkpoint操作的话，此时框架会自动生成RDDCheckpointData，当RDD上运行过一个Job后就会立即触发RDDCheckpointData中的checkpoint方法，在其内部会调用doCheckpoint,实际在生产环境下会调用ReliableRDDCheckpointData的doCheckpoint方法，在生产环境下会导致ReliableCheckpointRDD的writeRDDToCheckpointDirectory的调用，而在该方法内部会触发runJob执行来把当前RDD中的数据写到Checkpoint目录中，同时会产生ReliableCheckpointRDD实例