输入的ds有很多来源Kafka、Socket、Flume，输出的DStream其实是逻辑级别的Action，是Spark Streaming框架提出的，其底层翻译成为物理级别的额Action，是RDD的Action，中间是处理过程是transformations，状态转换也就是业务处理逻辑的过程。
Spark Streaming二种数据来源：
1、基于DStream数据源。
2、基于其他DStream产生的数据源。
关键性的观点:做大数据的时候不是流失处理，一般会有定时任务，定时任务一般十分钟触发一次、一天触发一次，做大数据的定时任务就是流失处理的感觉，虽然不规范，一切和流处理没有关系的数据都是没有价值的。即使做批处理或数据挖掘其实也是在做数据流处理，只不过是隐形的流处理，所有的数据都会变成流处理。
所以就有统一的抽象，所有处理都是流处理的方式，所有的处理都将会被纳入流处理。企业大型开发项目都有j2ee后台支撑来提供各种人操作大数据中心。
Spark streaming程序入口就有batchDuration时间窗口，每隔五秒钟JobGeneration都会产生一个job，这个job是逻辑级别的，所以逻辑级别要有这个job，并且这个job该琢磨做，但环没有做，由底层物理级别的action去做，底层物理级别是基于rdd的依赖关系。Ds的action操作也是逻辑级别的。Ss根据axtion操作产生逻辑级别的job，但是不会运行，就相当线程runnable接口。逻辑级别的暂时没有身材物理级别的，所以可以去调度和优化，假设讲ds的操作翻译成rdd的action，最后一个操作是rdd的action操作，是不是已翻译就立即触发job，纪要完成翻译又不要生成job的话需要
JavaStreamingContext jsc =newJavaStreamingContext(conf, Durations.seconds(5));
下面主要从三个类进行解析：
1、JobGenerator类：根据batchDuration及内部默认的时间间隔生成Jobs；
2、JobScheduler：根据batchDuration负责Spark Streaming Job的调度；
3、ReceiverTracker：负责Driver端元数据的接收和启动executor中的接收数据线程；
1、JobGenerator类：
**
* This class generates jobs from DStreams as well as drives checkpointing and cleaning
* up DStream metadata.
*/
private[streaming]
classJobGenerator(jobScheduler: JobScheduler)extendsLogging {
注释说基于dsg产生数据源，JobGenerator随着时间推移产生很多jobs，ss中除了定时身材的job，患有其他方式身材的job，例如基于各种聚合和状态的操作，状态操作不是基于batchd，他会对很多btchd处理。为了窗口之类的操作会触发JobGenerator，元素局的清理，作业生成的类。
// eventLoop is created when generator starts.
// This not being null means the scheduler has been started and not stopped
private var eventLoop: EventLoop[JobGeneratorEvent] = null //消息循环体定义
// last batch whose completion,checkpointing and metadata cleanup has been completed
private var lastProcessedBatch: Time = null
/** Start generation of jobs */
def start(): Unit = synchronized {
if (eventLoop != null) return// generator has already been started
// Call checkpointWriter here to initialize it before eventLoop uses it to avoid a deadlock.
// See SPARK-10125
checkpointWriter //执行checkpoint检查点
eventLoop = newEventLoop[JobGeneratorEvent]("JobGenerator") {//内部匿名类创建
override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event) //事件处理逻辑
override protected def onError(e: Throwable): Unit = {
jobScheduler.reportError("Error in job generator", e)
}
}
eventLoop.start()//启动事件处理线程对队列事件进行处理
if (ssc.isCheckpointPresent) {
restart()
} else {
startFirstTime()
}
}
/**
* An event loop to receive events from the caller and process all events in the event thread. It
* will start an exclusive event thread to process all events.
*
* Note: The event queue will grow indefinitely. So subclasses should make sure `onReceive` can
* handle events in time to avoid the potential OOM.
*/
private[spark] abstract classEventLoop[E](name: String) extends Logging {
private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()//消息队列数据结构
private val stopped = new AtomicBoolean(false)//原子变量
private val eventThread = new Thread(name) {//封装线程对象
setDaemon(true) //后台为线程
override def run(): Unit = { //线程执行逻辑
try {
while (!stopped.get) {
val event = eventQueue.take() //从消息队列中逐一获取消息对象
try {
onReceive(event) //对获取的消息对象进行业务处理
} catch {
case NonFatal(e) => { //处理失败后回调错误逻辑执代码
try {
onError(e)
} catch {
case NonFatal(e) => logError("Unexpected error in " + name, e)
}
}
}
}
} catch {
case ie: InterruptedException => // exit even if eventQueue is not empty
case NonFatal(e) => logError("Unexpected error in " + name, e)
}
}
}
def start(): Unit = { //启动当前线程类
if (stopped.get) {
throw new IllegalStateException(name + " has already been stopped")
}
// Call onStart before starting the event thread to make sure it happens before onReceive
onStart()
eventThread.start() //启动当前线程类业务run方法的执行
}
/** Processes all events */
private defprocessEvent(event: JobGeneratorEvent) {//根据消息对象执行相应的处理业务代码
logDebug("Got event " + event)
event match {
case GenerateJobs(time) => generateJobs(time) //根据时间片生成Jobs
case ClearMetadata(time) => clearMetadata(time) //时间片内的Jobs执行完毕，清理Driver上的元数据
case DoCheckpoint(time, clearCheckpointDataLater) =>//时间片内的Jobs执行完毕，清理checkpint数据
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
2、JobSchedule类：
/**
* This class schedules jobs to be run on Spark. It uses the JobGenerator to generate
* the jobs and runs them using a thread pool.
*/
private[streaming]
class JobScheduler(val ssc: StreamingContext) extends Logging {
// Use of ConcurrentHashMap.keySet later causes an odd runtime problem due to Java 7/8 diff
// https://gist.github.com/AlainODea/1375759b8720a3f9f094
private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]//在指定的时间片内生成Jobs集合数据结构
private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
private val jobExecutor =
ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")//启动指定大小的线程池
private val jobGenerator = new JobGenerator(this)//启动JobGenerator对象
val clock = jobGenerator.clock //jobGenerator时钟
val listenerBus = new StreamingListenerBus() //linstenerBus消息总线
// These two are created only when scheduler starts.
// eventLoop not being null means the scheduler has been started and not stopped
var receiverTracker: ReceiverTracker = null //driver端的元数据接收跟踪器
// A tracker to track all the input stream information as well as processed record number
var inputInfoTracker: InputInfoTracker = null //输入流信息跟踪器
private var eventLoop: EventLoop[JobSchedulerEvent] = null //消息循环体对象
def start(): Unit = synchronized { JobScheudler类启动主方法
if (eventLoop != null) return // scheduler has already been started
logDebug("Starting JobScheduler")
eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") {
override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event)
override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e)
}
eventLoop.start()
// attach rate controllers of input streams to receive batch completion updates
for {
inputDStream <- ssc.graph.getInputStreams //数据流
rateController <- inputDStream.rateController //数据接收平率控制
} ssc.addStreamingListener(rateController)
listenerBus.start(ssc.sparkContext) //启动消息总线
receiverTracker = new ReceiverTracker(ssc) //创建接收器对象
inputInfoTracker = new InputInfoTracker(ssc) //创建数据输入对象
receiverTracker.start() //启动数据接收器线程
jobGenerator.start() //启动jobs产生器线程
logInfo("Started JobScheduler")
}
def submitJobSet(jobSet: JobSet) {
if (jobSet.jobs.isEmpty) {
logInfo("No jobs added for time " + jobSet.time)
} else {
listenerBus.post(StreamingListenerBatchSubmitted(jobSet.toBatchInfo))
jobSets.put(jobSet.time, jobSet)
jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job)))
logInfo("Added jobs for time " + jobSet.time)
}
}
private def handleJobStart(job: Job, startTime: Long) {
val jobSet = jobSets.get(job.time)
val isFirstJobOfJobSet = !jobSet.hasStarted
jobSet.handleJobStart(job)
if (isFirstJobOfJobSet) {
// "StreamingListenerBatchStarted" should be posted after calling "handleJobStart" to get the
// correct "jobSet.processingStartTime".
listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo))
}
job.setStartTime(startTime)
listenerBus.post(StreamingListenerOutputOperationStarted(job.toOutputOperationInfo))
logInfo("Starting job " + job.id + " from job set of time " + jobSet.time)
}
private class JobHandler(job: Job) extends Runnable with Logging {
import JobScheduler._
def run() {
try {
val formattedTime = UIUtils.formatBatchTime(
job.time.milliseconds, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false)
val batchUrl = s"/streaming/batch/?id=${job.time.milliseconds}"
val batchLinkText = s"[output operation ${job.outputOpId}, batch time ${formattedTime}]"
ssc.sc.setJobDescription(
s"""Streaming job from $batchLinkText""")
ssc.sc.setLocalProperty(BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString)
ssc.sc.setLocalProperty(OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString)
// We need to assign `eventLoop` to a temp variable. Otherwise, because
// `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then
// it's possible that when `post` is called, `eventLoop` happens to null.
var _eventLoop = eventLoop
if (_eventLoop != null) {
_eventLoop.post(JobStarted(job, clock.getTimeMillis()))
// Disable checks for existing output directories in jobs launched by the streaming
// scheduler, since we may need to write output to an existing directory during checkpoint
// recovery; see SPARK-4835 for more details.
PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
job.run()
}
_eventLoop = eventLoop
if (_eventLoop != null) {
_eventLoop.post(JobCompleted(job, clock.getTimeMillis()))
}
} else {
// JobScheduler has been stopped.
}
} finally {
ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null)
ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null)
}
}
}
}
备注：
资料来源于：DT_大数据梦工厂（Spark发行版本定制）
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