1. spark streaming 程序代码实例
代码如下：
object OnlineTheTop3ItemForEachCategory2DB {
def main(args: Array[String]){
val conf = new SparkConf() //创建SparkConf对象
//设置应用程序的名称，在程序运行的监控界面可以看到名称
conf.setAppName("OnlineTheTop3ItemForEachCategory2DB")
conf.setMaster("spark://Master:7077") //此时，程序在Spark集群
//设置batchDuration时间间隔来控制Job生成的频率并且创建Spark Streaming执行的入口
val ssc = new StreamingContext(conf, Seconds(5))
ssc.checkpoint("/root/Documents/SparkApps/checkpoint")
val soketDStream = ssc.socketTextStream("Master", 9999)
/// 业务处理逻辑 .....
ssc.start()
ssc.awaitTermination()
}
}
2. Spark Streaming的运行源码分析
2.1 创建StreamingContext
我们将基于以上实例，粗略地分析一下Spark源码，提示一些有针对性的内容，以了解其运行的主要流程。
1）代码没有直接使用SparkContext，而是使用StreamingContext。
我们来看看StreamingContext 的源码片段：
/**
* Create a StreamingContext by providing the configuration necessary for a new SparkContext.
* @param conf a org.apache.spark.SparkConf object specifying Spark parameters
* @param batchDuration the time interval at which streaming data will be divided into batches
*/
def this(conf: SparkConf, batchDuration: Duration) = {
this(StreamingContext.createNewSparkContext(conf), null, batchDuration)
}
没错，createNewSparkContext就是创建SparkContext：
private[streaming] def createNewSparkContext(conf: SparkConf): SparkContext = {
new SparkContext(conf)
}
这说明Spark Streaming也是Spark上的一个应用程序。
2）案例最开始的地方肯定要通过数据流创建一个InputDStream。
val socketDstram = ssc.socketTextStream("Master", 9999)
socketTextStream方法定义如下：
/**
* Create a input stream from TCP source hostname:port. Data is received using
* a TCP socket and the receive bytes is interpreted as UTF8 encoded `\n` delimited
* lines.
* @param hostname Hostname to connect to for receiving data
* @param port Port to connect to for receiving data
* @param storageLevel Storage level to use for storing the received objects
* (default: StorageLevel.MEMORY_AND_DISK_SER_2)
*/
def socketTextStream(
hostname: String,
port: Int,
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
): ReceiverInputDStream[String] = withNamedScope("socket text stream") {
socketStream[String](hostname, port, SocketReceiver.bytesToLines, storageLevel)
}
3）可看到代码最后面调用socketStream。
socketStream定义如下：
/**
* Create a input stream from TCP source hostname:port. Data is received using
* a TCP socket and the receive bytes it interepreted as object using the given
* converter.
* @param hostname Hostname to connect to for receiving data
* @param port Port to connect to for receiving data
* @param converter Function to convert the byte stream to objects
* @param storageLevel Storage level to use for storing the received objects
* @tparam T Type of the objects received (after converting bytes to objects)
*/
def socketStream[T: ClassTag](
hostname: String,
port: Int,
converter: (InputStream) => Iterator[T],
storageLevel: StorageLevel
): ReceiverInputDStream[T] = {
new SocketInputDStream[T](this, hostname, port, converter, storageLevel)
}
4）实际上生成SocketInputDStream。
SocketInputDStream类如下：
SocketInputDStream继承ReceiverInputDStream。其中实现getReceiver方法，返回SocketReceiver对象。
总结一下SocketInputDStream的继承关系：
SocketInputDStream -> ReceiverInputDStream -> InputDStream -> DStream。
5）DStream是生成RDD的模板，是逻辑级别，当达到Interval的时候这些模板会被batch data实例化成为RDD和DAG。
DStream的generatedRDDs：
// RDDs generated, marked as private[streaming] so that testsuites can access it
@transient
private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] ()
DStream的getOrCompute：
主要是生成RDD，再将生成的RDD放在HashMap中。具体生成RDD过程以后剖析。
目前大致讲了DStream和RDD这些核心概念在Spark Streaming中的使用。
2.2 启动StreamingContext
代码实例中的ssc.start() 方法启动StreamingContext,主要的逻辑发生在这个start方法中：
在StreamingContext调用start方法的内部其实是会启动JobScheduler的Start方法，进行消息循环，在JobScheduler的start内部会构造JobGenerator和ReceiverTacker，并且调用JobGenerator和 ReceiverTacker的start方法：
JobGenerator启动后会不断的根据
batchDuration生成一个个的Job。
其实这里的Job不是Spark Core中所指的Job，它只是基于DStreamGraph而生成的RDD的DAG而已，从Java角度讲，相当于Runnable接口实例，此时要想运行Job需要提交给JobScheduler，在JobScheduler中通过线程池的方式找到一个单独的线程来提交Job到集群运行（其实是在线程中基于RDD的Action触发真正的作业的运行）ReceiverTracker启动后首先在Spark Cluster中启动Receiver（其实是在Executor中先启动ReceiverSupervisor），在Receiver收到数据后会通过ReceiverSupervisor存储到Executor并且把数据的Metadata信息发送给Driver中的ReceiverTracker，在ReceiverTracker内部会通过ReceivedBlockTracker来管理接受到的元数据信息。
体现Spark Streaming应用运行流程的关键类如下图所示。
下面开启神奇的 源码分析之旅,过程痛苦,痛苦之后是大彻大悟的畅快...........
1）先看看ScreamingContext的start()。
start()方法启动StreamContext，由于Spark应用程序不能有多个SparkContext对象实例，所以Spark Streaming框架在启动时对状态进行判断。代码如下：
初始状态时，会启动JobScheduler。
2）接着来看下JobScheduler的启动过程start()。
其中启动了EventLoop、StreamListenerBus、ReceiverTracker和jobGenerator等多项工作。
3）JobScheduler中的消息处理函数processEvent。
处理三类消息：Job已开始，Job已完成，错误报告。
4）我们再粗略地分析一下JobScheduler.start()中启动的工作。
4.1）先看JobScheduler.start()启动的第一项工作EventLoop。
EventLoop用于处理JobScheduler的各种事件。
EventLoop中有事件队列：
private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()
还有一个线程处理队列中的事件：
这个线程中的onReceive、onError，在JobScheduler中的EventLoop实例化时已定义。
4.2）JobScheduler.start()启动的第二项工作StreamListenerBus。
- 用于异步传递StreamingListenerEvents到注册的StreamingListeners。
- 用于更新Spark UI中StreamTab的内容。
4.3）看JobScheduler.start()启动的第三项工作ReceiverTracker。
ReceiverTracker用于管理所有的输入的流，以及他们输入的数据统计。
这些信息将通过 StreamingListener监听。
ReceiverTracker的start()中，会内部实例化ReceiverTrackerEndpoint这个Rpc消息通信体。
1 def start(): Unit = synchronized {
2 if (isTrackerStarted) {
3 throw new SparkException("ReceiverTracker already started")
4 }
5
6 if (!receiverInputStreams.isEmpty) {
7 endpoint = ssc.env.rpcEnv.setupEndpoint(
8 "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv))
9 if (!skipReceiverLaunch) launchReceivers()
10 logInfo("ReceiverTracker started")
11 trackerState = Started
12 }
13 }
在ReceiverTracker启动的过程中会调用其launchReceivers方法：
/**
* Get the receivers from the ReceiverInputDStreams, distributes them to the
* worker nodes as a parallel collection, and runs them.
*/
private def launchReceivers(): Unit = {
val receivers = receiverInputStreams.map(nis => {
val rcvr = nis.getReceiver()
rcvr.setReceiverId(nis.id)
rcvr
})
runDummySparkJob()
logInfo("Starting " + receivers.length + " receivers")
endpoint.send(StartAllReceivers(receivers))
}
其中调用了runDummySparkJob方法来启动Spark Streaming的框架第一个Job，其中collect这个action操作会触发Spark Job的执行。这个方法是为了确保每个Slave都注册上，避免所有Receiver都在一个节点，使后面的计算能负载均衡。
/**
* Run the dummy Spark job to ensure that all slaves have registered. This avoids all the
* receivers to be scheduled on the same node.
*
* TODO Should poll the executor number and wait for executors according to
* "spark.scheduler.minRegisteredResourcesRatio" and
* "spark.scheduler.maxRegisteredResourcesWaitingTime" rather than running a dummy job.
*/
private def runDummySparkJob(): Unit = {
if (!ssc.sparkContext.isLocal) {
ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
}
assert(getExecutors.nonEmpty)
}
ReceiverTracker.launchReceivers()还调用了endpoint.send(StartAllReceivers(receivers))方法，Rpc消息通信体发送StartAllReceivers消息。
ReceiverTrackerEndpoint它自己接收到消息后，先根据调度策略获得Recevier在哪个Executor上运行，然后在调用startReceiver(receiver, executors)方法，来启动Receiver。
override def receive: PartialFunction[Any, Unit] = {
// Local messages
case StartAllReceivers(receivers) =>
val scheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors)
for (receiver <- receivers) {
val executors = scheduledLocations(receiver.streamId)
updateReceiverScheduledExecutors(receiver.streamId, executors)
receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
startReceiver(receiver, executors)
}
在startReceiver方法中，ssc.sparkContext.submitJob提交Job的时候传入startReceiverFunc这个方法，因为startReceiverFunc该方法是在Executor上执行的。而在startReceiverFunc方法中实例化ReceiverSupervisorImpl对象，该对象是对Receiver进行管理和监控。这个Job是Spark Streaming框架为我们启动的第二个Job，且一直运行。因为supervisor.awaitTermination()该方法会阻塞等待退出。
/**
* Start a receiver along with its scheduled executors
*/
private def startReceiver(
receiver: Receiver[_],
scheduledLocations: Seq[TaskLocation]): Unit = {
def shouldStartReceiver: Boolean = {
// ........... 此处省略1万字 (无关代码) , 呵呵哒 .........
// Function to start the receiver on the worker node
val startReceiverFunc: Iterator[Receiver[_]] => Unit =
(iterator: Iterator[Receiver[_]]) => {
if (!iterator.hasNext) {
throw new SparkException(
"Could not start receiver as object not found.")
}
if (TaskContext.get().attemptNumber() == 0) {
val receiver = iterator.next()
assert(iterator.hasNext == false)
//实例化Receiver监控者
val supervisor = new ReceiverSupervisorImpl(
receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
supervisor.start()
supervisor.awaitTermination()
} else {
// It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
}
}
// Create the RDD using the scheduledLocations to run the receiver in a Spark job
val receiverRDD: RDD[Receiver[_]] =
if (scheduledLocations.isEmpty) {
ssc.sc.makeRDD(Seq(receiver), 1)
} else {
val preferredLocations = scheduledLocations.map(_.toString).distinct
ssc.sc.makeRDD(Seq(receiver -> preferredLocations))
}
receiverRDD.setName(s"Receiver $receiverId")
ssc.sparkContext.setJobDescription(s"Streaming job running receiver $receiverId")
ssc.sparkContext.setCallSite(Option(ssc.getStartSite()).getOrElse(Utils.getCallSite()))
val future = ssc.sparkContext.submitJob[Receiver[_], Unit, Unit](
receiverRDD,
startReceiverFunc, //提交Job时候传入startReceiverFunc这个方法，因为startReceiverFunc该方法是在Executor上执行的
Seq(0), (_, _) => Unit, ())
// 一直重启 receiver job直到 ReceiverTracker is stopped
future.onComplete {
case Success(_) =>
if (!shouldStartReceiver) {
onReceiverJobFinish(receiverId)
} else {
logInfo(s"Restarting Receiver $receiverId")
self.send(RestartReceiver(receiver))
}
case Failure(e) =>
if (!shouldStartReceiver) {
onReceiverJobFinish(receiverId)
} else {
logError("Receiver has been stopped. Try to restart it.", e)
logInfo(s"Restarting Receiver $receiverId")
self.send(RestartReceiver(receiver))
}
}(submitJobThreadPool)
logInfo(s"Receiver ${receiver.streamId} started")
}
接下来看下ReceiverSupervisorImpl的启动过程，先启动所有注册上的BlockGenerator对象，然后向ReceiverTrackerEndpoint发送RegisterReceiver消息，再调用receiver的onStart方法。
/** Start the supervisor */
def start() {
onStart()
startReceiver()
}
其中的onStart():启动所有注册上的BlockGenerator对象
override protected def onStart() {
registeredBlockGenerators.foreach { _.start() }
}
其中的startReceiver()方法中调用onReceiverStart()然后再调用receiver的onStart方法。
/** Start receiver */
def startReceiver(): Unit = synchronized {
try {
if (onReceiverStart()) {
logInfo("Starting receiver")
receiverState = Started
receiver.onStart()
logInfo("Called receiver onStart")
} else {
// The driver refused us
stop("Registered unsuccessfully because Driver refused to start receiver " + streamId, None)
}
} catch {
case NonFatal(t) =>
stop("Error starting receiver " + streamId, Some(t))
}
}
在onReceiverStart()中向ReceiverTrackerEndpoint发送RegisterReceiver消息
override protected def onReceiverStart(): Boolean = {
val msg = RegisterReceiver(
streamId, receiver.getClass.getSimpleName, host, executorId, endpoint)
trackerEndpoint.askWithRetry[Boolean](msg)
}
其中在Driver运行的ReceiverTrackerEndpoint对象接收到RegisterReceiver消息后，将streamId, typ, host, executorId, receiverEndpoint封装为ReceiverTrackingInfo保存到内存对象receiverTrackingInfos这个HashMap中。
override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
// Remote messages
case RegisterReceiver(streamId, typ, host, executorId, receiverEndpoint) =>
val successful =
registerReceiver(streamId, typ, host, executorId, receiverEndpoint, context.senderAddress)
context.reply(successful)
registerReceiver方法源码:
/** Register a receiver */
private def registerReceiver(
streamId: Int,
typ: String,
host: String,
executorId: String,
receiverEndpoint: RpcEndpointRef,
senderAddress: RpcAddress
): Boolean = {
if (!receiverInputStreamIds.contains(streamId)) {
throw new SparkException("Register received for unexpected id " + streamId)
}
// ........... 此处省略1万字 (无关代码) , 呵呵哒 .........
if (!isAcceptable) {
// Refuse it since it's scheduled to a wrong executor
false
} else {
val name = s"${typ}-${streamId}"
val receiverTrackingInfo = ReceiverTrackingInfo(
streamId,
ReceiverState.ACTIVE,
scheduledLocations = None,
runningExecutor = Some(ExecutorCacheTaskLocation(host, executorId)),
name = Some(name),
endpoint = Some(receiverEndpoint))
receiverTrackingInfos.put(streamId, receiverTrackingInfo)
listenerBus.post(StreamingListenerReceiverStarted(receiverTrackingInfo.toReceiverInfo))
logInfo("Registered receiver for stream " + streamId + " from " + senderAddress)
true
}
}
Receiver的启动，以ssc.socketTextStream("localhost", 9999)为例，创建的是SocketReceiver对象。内部启动一个线程来连接Socket Server，读取socket数据并存储。
private[streaming]
class SocketReceiver[T: ClassTag](
host: String,
port: Int,
bytesToObjects: InputStream => Iterator[T],
storageLevel: StorageLevel
) extends Receiver[T](storageLevel) with Logging {
def onStart() {
// Start the thread that receives data over a connection
new Thread("Socket Receiver") {
setDaemon(true)
override def run() { receive() }
}.start()
}
/** Create a socket connection and receive data until receiver is stopped */
def receive() {
var socket: Socket = null
try {
logInfo("Connecting to " + host + ":" + port)
socket = new Socket(host, port)
logInfo("Connected to " + host + ":" + port)
val iterator = bytesToObjects(socket.getInputStream())
while(!isStopped && iterator.hasNext) {
store(iterator.next)
}
if (!isStopped()) {
restart("Socket data stream had no more data")
} else {
logInfo("Stopped receiving")
}
} catch {
// ........... 此处省略1万字 (无关代码) , 呵呵哒 .........
}
}
4.4）接下来看JobScheduler.start()中启动的第四项工作JobGenerator。
JobGenerator有成员RecurringTimer，用于启动消息系统和定时器。按照batchInterval时间间隔定期发送GenerateJobs消息。
//根据创建StreamContext时传入的batchInterval，定时发送GenerateJobs消息
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")
JobGenerator的start()方法：
/** 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
eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {
override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)
override protected def onError(e: Throwable): Unit = {
jobScheduler.reportError("Error in job generator", e)
}
}
// 启动消息循环处理线程
eventLoop.start()
if (ssc.isCheckpointPresent) {
restart()
} else {
// 开启定时生成Job的定时器
startFirstTime()
}
}
JobGenerator.start()中的startFirstTime()的定义：
/** Starts the generator for the first time */
private def startFirstTime() {
val startTime = new Time(timer.getStartTime())
graph.start(startTime - graph.batchDuration)
timer.start(startTime.milliseconds)
logInfo("Started JobGenerator at " + startTime)
}
JobGenerator.start()中的processEvent()的定义：
其中generateJobs的定义：
/** Generate jobs and perform checkpoint for the given `time`. */
private def generateJobs(time: Time) {
// Set the SparkEnv in this thread, so that job generation code can access the environment
// Example: BlockRDDs are created in this thread, and it needs to access BlockManager
// Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.
SparkEnv.set(ssc.env)
Try {
// 根据特定的时间获取具体的数据
jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
//调用DStreamGraph的generateJobs生成Job
graph.generateJobs(time) // generate jobs using allocated block
} match {
case Success(jobs) =>
val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
case Failure(e) =>
jobScheduler.reportError("Error generating jobs for time " + time, e)
}
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}
DStreamGraph的generateJobs方法，调用输出流的generateJob方法来生成Jobs集合。
// 输出流：具体Action的输出操作
private val outputStreams = new ArrayBuffer[DStream[_]]()
def generateJobs(time: Time): Seq[Job] = {
logDebug("Generating jobs for time " + time)
val jobs = this.synchronized {
outputStreams.flatMap { outputStream =>
val jobOption = outputStream.generateJob(time)
jobOption.foreach(_.setCallSite(outputStream.creationSite))
jobOption
}
}
logDebug("Generated " + jobs.length + " jobs for time " + time)
jobs
}
来看下DStream的generateJob方法，调用getOrCompute方法来获取当Interval的时候，DStreamGraph会被BatchData实例化成为RDD，如果有RDD则封装jobFunc方法，里面包含context.sparkContext.runJob(rdd, emptyFunc)，然后返回封装后的Job。
/**
* Generate a SparkStreaming job for the given time. This is an internal method that
* should not be called directly. This default implementation creates a job
* that materializes the corresponding RDD. Subclasses of DStream may override this
* to generate their own jobs.
*/
private[streaming] def generateJob(time: Time): Option[Job] = {
getOrCompute(time) match {
case Some(rdd) => {
val jobFunc = () => {
val emptyFunc = { (iterator: Iterator[T]) => {} }
context.sparkContext.runJob(rdd, emptyFunc)
}
Some(new Job(time, jobFunc))
}
case None => None
}
}
接下来看JobScheduler的submitJobSet方法，向线程池中提交JobHandler。而JobHandler实现了Runnable 接口，最终调用了job.run()这个方法。看一下Job类的定义，其中run方法调用的func为构造Job时传入的jobFunc，其包含了context.sparkContext.runJob(rdd, emptyFunc)操作，最终导致Job的提交。
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)
}
}
JobHandler实现了Runnable 接口，最终调用了job.run()这个方法:
private class JobHandler(job: Job) extends Runnable with Logging {
import JobScheduler._
def run() {
try {
// *********** 此处省略无关代码 *******************
// 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)
}
}
}
}
Job的代码片段：
private[streaming]
class Job(val time: Time, func: () => _) {
private var _id: String = _
private var _outputOpId: Int = _
private var isSet = false
private var _result: Try[_] = null
private var _callSite: CallSite = null
private var _startTime: Option[Long] = None
private var _endTime: Option[Long] = None
def run() {
_result = Try(func())
}
以上是主要源码的分析.