StreamingContext实例化过程

一.SparkStreaming实例化过程

创建StreamingContext的方法有很多种，示例以SparkCon和任务执行间隔作为参数创建StreamingContext

val ssc = new StreamingContext(conf,Seconds(3))

1.检查SparkContext和CheckPoint，不能同时为空，否则抛出异常

2.检查checkPoint是否启用

3.更新SparkContext，如果SparkContext不存在，则将checkPoint需要的信息写入SparkConf，并以此构建SparkContext；如果SparkContext存在，则使用它

checkpoint需要的信息：

val propertiesToReload = List(
"spark.yarn.app.id",
"spark.yarn.app.attemptId",
"spark.driver.host",
"spark.driver.port",
"spark.master",
"spark.yarn.keytab",
"spark.yarn.principal",
"spark.ui.filters")

4.模式检查，spark.master不能为本地单线程模式

5.通过SparkContext来更新SparkConf和SparkEnv

6.实例化DStreamGraph

private[streaming] val graph: DStreamGraph = {
if (isCheckpointPresent) {
    cp_.graph.setContext(this)
    cp_.graph.restoreCheckpointData()
    cp_.graph
} else {
require(batchDur_ != null, "Batch duration for StreamingContext cannot be null")
val newGraph = new DStreamGraph()
    newGraph.setBatchDuration(batchDur_)
    newGraph
  }
}
7.设置checkPoint目录
private[streaming] var checkpointDir: String = {
if (isCheckpointPresent) {
sc.setCheckpointDir(cp_.checkpointDir)
    cp_.checkpointDir
} else {
null
}
}
8.创建Streaming作业调度器，JobScheduler，使用DStreamGraph产生Job
valscheduler = new JobScheduler(this)
9.创建ContextWaiter
valwaiter = new ContextWaiter




jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
graph.generateJobs(time) // generate jobs using allocated block
val jobOption = outputStream.generateJob(time)