Flink执行时之client提交作业图-下
submitJob方法分析
JobClientActor通过向JobManager的Actor发送SubmitJob消息来提交Job，JobManager接收到消息对象之后。构建一个JobInfo对象以封装Job的基本信息。然后将这两个对象传递给submitJob方法：
case SubmitJob(jobGraph, listeningBehaviour) =>
val client = sender()
val jobInfo = new JobInfo(client, listeningBehaviour, System.currentTimeMillis(),
jobGraph.getSessionTimeout)
submitJob(jobGraph, jobInfo)我们会以submitJob的关键方法调用来串讲其主要逻辑。首先推断jobGraph參数，假设为空则直接回应JobResultFailure消息：
if (jobGraph == null) {
jobInfo.client ! decorateMessage(JobResultFailure(
new SerializedThrowable(
new JobSubmissionException(null, "JobGraph must not be null.")
)
))
}接着，向类库缓存管理器注冊该Job相关的库文件、类路径：
libraryCacheManager.registerJob(jobGraph.getJobID, jobGraph.getUserJarBlobKeys,
jobGraph.getClasspaths)必须确保该步骤领先成功运行，由于一旦兴许产生不论什么异常才干够确保上传的类库和Jar等被成功从类库缓存管理器中移除。从这開始的整个代码段都被包裹在try语句块中。一旦捕获到不论什么异常。会通过libraryCacheManager的unregisterJob方法将相关Jar文件删除：
catch { case t: Throwable =>
libraryCacheManager.unregisterJob(jobId)
//...
}接下来是获得用户代码的类载入器classLoader以及发生失败时的重新启动策略restartStrategy：
val userCodeLoader = libraryCacheManager.getClassLoader(jobGraph.getJobID)
val restartStrategy = Option(jobGraph.getRestartStrategyConfiguration())
.map(RestartStrategyFactory.createRestartStrategy(_)) match {
case Some(strategy) => strategy
case None => defaultRestartStrategy
}接着，获得运行图ExecutionGraph对象的实例。首先尝试从缓存中查找。假设缓存中存在则直接返回，否则直接创建然后增加缓存：
executionGraph = currentJobs.get(jobGraph.getJobID) match {
case Some((graph, currentJobInfo)) =>
currentJobInfo.setLastActive()
graph
case None =>
val graph = new ExecutionGraph(
executionContext,
jobGraph.getJobID,
jobGraph.getName,
jobGraph.getJobConfiguration,
timeout,
restartStrategy,
jobGraph.getUserJarBlobKeys,
jobGraph.getClasspaths,
userCodeLoader)
currentJobs.put(jobGraph.getJobID, (graph, jobInfo))
graph
}获得了executionGraph之后会对其相关属性进行设置。这些属性包括调度模式、是否同意被增加调度队列、计划的Json格式表示。
executionGraph.setScheduleMode(jobGraph.getScheduleMode())
executionGraph.setQueuedSchedulingAllowed(jobGraph.getAllowQueuedScheduling())
executionGraph.setJsonPlan(JsonPlanGenerator.generatePlan(jobGraph))接下来初始化JobVertex的一些属性：
val numSlots = scheduler.getTotalNumberOfSlots()
for (vertex <- jobGraph.getVertices.asScala) {
val executableClass = vertex.getInvokableClassName
if (vertex.getParallelism() == ExecutionConfig.PARALLELISM_AUTO_MAX) {
vertex.setParallelism(numSlots)
}
vertex.initializeOnMaster(userCodeLoader)
}获得JobGraph中从source開始的依照拓扑顺序排序的顶点集合，然后将该集合附加到ExecutionGraph上，附加的过程完毕了非常多事情。我们兴许进行分析：
val sortedTopology = jobGraph.getVerticesSortedTopologicallyFromSources()
executionGraph.attachJobGraph(sortedTopology)接下来将快照配置和检查点配置的信息写入ExecutionGraph：
val snapshotSettings = jobGraph.getSnapshotSettings
if (snapshotSettings != null) {
val jobId = jobGraph.getJobID()
val idToVertex: JobVertexID => ExecutionJobVertex = id => {
val vertex = executionGraph.getJobVertex(id)
vertex
}
val triggerVertices: java.util.List[ExecutionJobVertex] =
snapshotSettings.getVerticesToTrigger().asScala.map(idToVertex).asJava
val ackVertices: java.util.List[ExecutionJobVertex] =
snapshotSettings.getVerticesToAcknowledge().asScala.map(idToVertex).asJava
val confirmVertices: java.util.List[ExecutionJobVertex] =
snapshotSettings.getVerticesToConfirm().asScala.map(idToVertex).asJava
val completedCheckpoints = checkpointRecoveryFactory
.createCompletedCheckpoints(jobId, userCodeLoader)
val checkpointIdCounter = checkpointRecoveryFactory.createCheckpointIDCounter(jobId)
executionGraph.enableSnapshotCheckpointing(
snapshotSettings.getCheckpointInterval,
snapshotSettings.getCheckpointTimeout,
snapshotSettings.getMinPauseBetweenCheckpoints,
snapshotSettings.getMaxConcurrentCheckpoints,
triggerVertices,
ackVertices,
confirmVertices,
context.system,
leaderSessionID.orNull,
checkpointIdCounter,
completedCheckpoints,
recoveryMode,
savepointStore)
}JobManager自身会注冊Job状态变更的事件回调：
executionGraph.registerJobStatusListener(new AkkaActorGateway(self, leaderSessionID.orNull))假设Client也须要感知到运行结果以及Job状态的变更，那么也会为Client注冊事件回调：
if (jobInfo.listeningBehaviour == ListeningBehaviour.EXECUTION_RESULT_AND_STATE_CHANGES) {
val gateway = new AkkaActorGateway(jobInfo.client, leaderSessionID.orNull)
executionGraph.registerExecutionListener(gateway)
executionGraph.registerJobStatusListener(gateway)
}以上这些代码从将Job相关的Jar增加到类库缓存管理器開始，都被包裹在try块中。假设产生异常将进入catch代码块中进行异常处理：
catch {
case t: Throwable =>
log.error(s"Failed to submit job $jobId ($jobName)", t)
libraryCacheManager.unregisterJob(jobId)
currentJobs.remove(jobId)
if (executionGraph != null) {
executionGraph.fail(t)
}
val rt: Throwable = if (t.isInstanceOf[JobExecutionException]) {
t
} else {
new JobExecutionException(jobId, s"Failed to submit job $jobId ($jobName)", t)
}
jobInfo.client ! decorateMessage(JobResultFailure(new SerializedThrowable(rt)))
return
}异常处理时首先依据jobID移除类库缓存中跟当前Job有关的类库，接着从currentJobsMap中移除job相应的ExecutionGraph，JobInfo元组信息。然后调用ExecutionGraph的fail方法。促使其失败。最后。将产生的异常以JobResultFailure消息告知客户端并结束方法调用。
从当前開始直到最后的这段代码可能会造成堵塞，将会被包裹在future块中并以异步的方式运行。先推断当前的是否是恢复模式，假设是恢复模式则从近期的检查点恢复：
if (isRecovery) {
executionGraph.restoreLatestCheckpointedState()
}假设不是恢复模式，但快照配置中存在保存点路径。也将基于保存点来重置状态：
executionGraph.restoreSavepoint(savepointPath) 然后会把当前的JobGraph信息写入SubmittedJobGraphStore，它主要用于恢复的目的
submittedJobGraphs.putJobGraph(new SubmittedJobGraph(jobGraph, jobInfo))运行到这一步。就能够向Client回复JobSubmitSuccess消息了：
jobInfo.client ! decorateMessage(JobSubmitSuccess(jobGraph.getJobID))接下来会基于ExecutionGraph触发Job的调度，这是Task被运行的前提：
if (leaderElectionService.hasLeadership) {
executionGraph.scheduleForExecution(scheduler)
} else {
self ! decorateMessage(RemoveJob(jobId, removeJobFromStateBackend = false))
}为了防止多个JobManager同一时候调度同样的Job的情况产生，这里首先推断当前节点是否是Leader。
假设是，才会进行调度。
否则将会向自身发送一条RemoveJob消息。以进入其它处理逻辑。
到此为止，submitJob方法的梳理就算完毕了。
由于这是JobManager接收到Client提交的Job后的主要处理方法，所以包括的逻辑比較多。
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