对于NetworkInputDStream而言，其实不是真正的流方式，将数据读出来后不是直接去处理，而是先写到blocks中，后面的RDD再从blocks中读取数据继续处理
这就是一个将stream离散化的过程
NetworkInputDStream就是封装了将数据从source中读出来，然后放到blocks里面去的逻辑（Receiver线程）
还需要一个可以管理NetworkInputDStream，以及把NetworkInputDStream.Receiver部署到集群上执行的角色，这个就是NetworkInputTracker
NetworkInputTracker会负责执行一个独立的job，把各个Receiver以RDD的task的形式，分布到各个worknode上去执行
InputDStream
/** * This is the abstract base class for all input streams. This class provides methods * start() and stop() which is called by Spark Streaming system to start and stop receiving data. * Input streams that can generate RDDs from new data by running a service/thread only on * the driver node (that is, without running a receiver on worker nodes), can be * implemented by directly inheriting this InputDStream. For example, * FileInputDStream, a subclass of InputDStream, monitors a HDFS directory from the driver for * new files and generates RDDs with the new files. For implementing input streams * that requires running a receiver on the worker nodes, use * [[org.apache.spark.streaming.dstream.NetworkInputDStream]] as the parent class. * * @param ssc_ Streaming context that will execute this input stream */
abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext)
extends DStream[T](ssc_) {
private[streaming] var lastValidTime: Time = null
ssc.graph.addInputStream(this) // 首先将InputStream加入graph中
override def dependencies = List()
override def slideDuration: Duration = {
if (ssc == null) throw new Exception("ssc is null")
if (ssc.graph.batchDuration == null) throw new Exception("batchDuration is null")
ssc.graph.batchDuration
}
/** Method called to start receiving data. Subclasses must implement this method. */
def start()
/** Method called to stop receiving data. Subclasses must implement this method. */
def stop()
}
NetworkInputDStream
NetworkInputDStream是比较典型的Input，主要接口两个
getReceiver，Receiver对于NetworkInputDStream是最关键的，里面封装了如果从数据源读到数据，如果切分并写到blocks中去
compute，由于Receiver只会把数据写到blocks中去，问题我们如何取到这些数据了?
Receiver在写block的同时，会发event给networkInputTracker注册block
所以NetworkInputDStream.compute是无法直接算出数据来，而是先从networkInputTracker查询出blockids，并从BlockManager中读出数据
/** * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]] * that has to start a receiver on worker nodes to receive external data. * Specific implementations of NetworkInputDStream must * define the getReceiver() function that gets the receiver object of type * [[org.apache.spark.streaming.dstream.NetworkReceiver]] that will be sent * to the workers to receive data. * @param ssc_ Streaming context that will execute this input stream * @tparam T Class type of the object of this stream */
abstract class NetworkInputDStream[T: ClassTag](@transient ssc_ : StreamingContext)
extends InputDStream[T](ssc_) {
// This is an unique identifier that is used to match the network receiver with the
// corresponding network input stream.
val id = ssc.getNewNetworkStreamId() // network stream id
/** * Gets the receiver object that will be sent to the worker nodes * to receive data. This method needs to defined by any specific implementation * of a NetworkInputDStream. */
def getReceiver(): NetworkReceiver[T]
override def compute(validTime: Time): Option[RDD[T]] = {
// If this is called for any time before the start time of the context,
// then this returns an empty RDD. This may happen when recovering from a
// master failure
if (validTime >= graph.startTime) {
val blockIds = ssc.scheduler.networkInputTracker.getBlockIds(id, validTime) // 从networkInputTracker中查询blockids
Some(new BlockRDD[T](ssc.sc, blockIds))
} else {
Some(new BlockRDD[T](ssc.sc, Array[BlockId]()))
}
}
}
NetworkReceiver
private[streaming] sealed trait NetworkReceiverMessage
private[streaming] case class StopReceiver(msg: String) extends NetworkReceiverMessage
private[streaming] case class ReportBlock(blockId: BlockId, metadata: Any)
extends NetworkReceiverMessage
private[streaming] case class ReportError(msg: String) extends NetworkReceiverMessage
/** * Abstract class of a receiver that can be run on worker nodes to receive external data. See * [[org.apache.spark.streaming.dstream.NetworkInputDStream]] for an explanation. */
abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging {
lazy protected val env = SparkEnv.get
lazy protected val actor = env.actorSystem.actorOf( // 创建NetworkReceiverActor（lazy），用于和networkInputTracker通信
Props(new NetworkReceiverActor()), "NetworkReceiver-" + streamId)
lazy protected val receivingThread = Thread.currentThread()
protected var streamId: Int = -1
/** * This method will be called to start receiving data. All your receiver * starting code should be implemented by defining this function. */
protected def onStart()
/** This method will be called to stop receiving data. */
protected def onStop()
/** Conveys a placement preference (hostname) for this receiver. */
def getLocationPreference() : Option[String] = None
/** * Starts the receiver. First is accesses all the lazy members to * materialize them. Then it calls the user-defined onStart() method to start * other threads, etc required to receiver the data. */
def start() {
try {
// Access the lazy vals to materialize them
env
actor
receivingThread
// Call user-defined onStart()
onStart()
} catch {
case ie: InterruptedException =>
logInfo("Receiving thread interrupted")
//println("Receiving thread interrupted")
case e: Exception =>
stopOnError(e)
}
}
/** * Stops the receiver. First it interrupts the main receiving thread, * that is, the thread that called receiver.start(). Then it calls the user-defined * onStop() method to stop other threads and/or do cleanup. */
def stop() {
receivingThread.interrupt()
onStop()
//TODO: terminate the actor
}
/** * Stops the receiver and reports exception to the tracker. * This should be called whenever an exception is to be handled on any thread * of the receiver. */
protected def stopOnError(e: Exception) {
logError("Error receiving data", e)
stop()
actor ! ReportError(e.toString)
}
/** * Pushes a block (as an ArrayBuffer filled with data) into the block manager. */
def pushBlock(blockId: BlockId, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) {
env.blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]], level)
actor ! ReportBlock(blockId, metadata)
}
/** * Pushes a block (as bytes) into the block manager. */
def pushBlock(blockId: BlockId, bytes: ByteBuffer, metadata: Any, level: StorageLevel) {
env.blockManager.putBytes(blockId, bytes, level)
actor ! ReportBlock(blockId, metadata)
}
}
NetworkReceiverActor
用于将Receiver的event转发给TrackerActor
/** A helper actor that communicates with the NetworkInputTracker */
private class NetworkReceiverActor extends Actor {
logInfo("Attempting to register with tracker")
val ip = env.conf.get("spark.driver.host", "localhost")
val port = env.conf.getInt("spark.driver.port", 7077)
val url = "akka.tcp://spark@%s:%s/user/NetworkInputTracker".format(ip, port)
val tracker = env.actorSystem.actorSelection(url)
val timeout = 5.seconds
override def preStart() {
val future = tracker.ask(RegisterReceiver(streamId, self))(timeout)
Await.result(future, timeout)
}
override def receive() = {
case ReportBlock(blockId, metadata) =>
tracker ! AddBlocks(streamId, Array(blockId), metadata)
case ReportError(msg) =>
tracker ! DeregisterReceiver(streamId, msg)
case StopReceiver(msg) =>
stop()
tracker ! DeregisterReceiver(streamId, msg)
}
}
protected[streaming] def setStreamId(id: Int) {
streamId = id
}
BlockGenerator
3个关键的接口，
+=，用于调用者将数据不断加到currentBuffer上
updateCurrentBuffer，定时将currentBuffer的数据，生成block对象放到blocksForPushing队列上（blockIntervalTimer调用）
keepPushingBlocks, 不断将blocksForPushing队列上的blocks取出，并写到blockmanager中去（blockPushingThread调用）
/** * Batches objects created by a [[org.apache.spark.streaming.dstream.NetworkReceiver]] and puts * them into appropriately named blocks at regular intervals. This class starts two threads, * one to periodically start a new batch and prepare the previous batch of as a block, * the other to push the blocks into the block manager. */
class BlockGenerator(storageLevel: StorageLevel)
extends Serializable with Logging {
case class Block(id: BlockId, buffer: ArrayBuffer[T], metadata: Any = null)
val clock = new SystemClock()
val blockInterval = env.conf.getLong("spark.streaming.blockInterval", 200)
val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer)
val blockStorageLevel = storageLevel
val blocksForPushing = new ArrayBlockingQueue[Block](1000)
val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
var currentBuffer = new ArrayBuffer[T]
def start() {
blockIntervalTimer.start()
blockPushingThread.start()
logInfo("Data handler started")
}
def stop() {
blockIntervalTimer.stop()
blockPushingThread.interrupt()
logInfo("Data handler stopped")
}
def += (obj: T): Unit = synchronized {
currentBuffer += obj
}
private def updateCurrentBuffer(time: Long): Unit = synchronized {
try {
val newBlockBuffer = currentBuffer
currentBuffer = new ArrayBuffer[T]
if (newBlockBuffer.size > 0) {
val blockId = StreamBlockId(NetworkReceiver.this.streamId, time - blockInterval)
val newBlock = new Block(blockId, newBlockBuffer)
blocksForPushing.add(newBlock)
}
} catch {
case ie: InterruptedException =>
logInfo("Block interval timer thread interrupted")
case e: Exception =>
NetworkReceiver.this.stop()
}
}
private def keepPushingBlocks() {
logInfo("Block pushing thread started")
try {
while(true) {
val block = blocksForPushing.take()
NetworkReceiver.this.pushBlock(block.id, block.buffer, block.metadata, storageLevel)
}
} catch {
case ie: InterruptedException =>
logInfo("Block pushing thread interrupted")
case e: Exception =>
NetworkReceiver.this.stop()
}
}
}
SocketInputDStream
Socket作为最为典型的NetworkInputDStream，看看是如何实现的
对于SocketInputDStream，关键实现getReceiver接口，可以获取SocketReceiver对象
而对于SocketReceiver关键是实现onStart接口，将从socket上读到的数据写到blockGenerator的currentBuffer上
private[streaming]
class SocketInputDStream[T: ClassTag](
@transient ssc_ : StreamingContext,
host: String,
port: Int,
bytesToObjects: InputStream => Iterator[T],
storageLevel: StorageLevel
) extends NetworkInputDStream[T](ssc_) {
def getReceiver(): NetworkReceiver[T] = { //关键是实现getReceiver接口
new SocketReceiver(host, port, bytesToObjects, storageLevel)
}
}
private[streaming]
class SocketReceiver[T: ClassTag](
host: String,
port: Int,
bytesToObjects: InputStream => Iterator[T],
storageLevel: StorageLevel
) extends NetworkReceiver[T] {
lazy protected val blockGenerator = new BlockGenerator(storageLevel) // 创建BlockGenerator
override def getLocationPreference = None
protected def onStart() {
val socket = new Socket(host, port)
blockGenerator.start()
val iterator = bytesToObjects(socket.getInputStream())
while(iterator.hasNext) {
val obj = iterator.next
blockGenerator += obj // 核心逻辑就是将从socket上读到的数据写到blockGenerator的currentBuffer上
}
}
protected def onStop() {
blockGenerator.stop()
}
}
NetworkInputTracker
NetworkInputTracker用于管理和监控所有的NetworkInputDStream
首先NetworkInputTrackerActor，可以从NetworkInputDStream接收RegisterReceiver，AddBlocks，和DeregisterReceiver事件
从而知道有多少NetworkInputDStream，并且每个读取并存储了多少的blocks
再者，在ReceiverExecutor中他负责启动所有NetworkInputDStream的Receivers，做法比较奇特，也是依赖于RDD
将每个receiver封装在RDD的一个partition里，partition会作为一个task被调度，最后runjob去执行startReceiver，这样每个receiver都会在task被执行的时候start
而外部通过getBlockIds，来取得某NetworkInputDStream所有的blockids，从而取到数据
private[streaming] sealed trait NetworkInputTrackerMessage //定义Tracker可能从receiver收到的event类型
private[streaming] case class RegisterReceiver(streamId: Int, receiverActor: ActorRef)
extends NetworkInputTrackerMessage
private[streaming] case class AddBlocks(streamId: Int, blockIds: Seq[BlockId], metadata: Any)
extends NetworkInputTrackerMessage
private[streaming] case class DeregisterReceiver(streamId: Int, msg: String)
extends NetworkInputTrackerMessage
/** * This class manages the execution of the receivers of NetworkInputDStreams. Instance of * this class must be created after all input streams have been added and StreamingContext.start() * has been called because it needs the final set of input streams at the time of instantiation. */
private[streaming]
class NetworkInputTracker(ssc: StreamingContext) extends Logging {
val networkInputStreams = ssc.graph.getNetworkInputStreams() //获取所有的networkInputStreams
val networkInputStreamMap = Map(networkInputStreams.map(x => (x.id, x)): _*)
val receiverExecutor = new ReceiverExecutor()
val receiverInfo = new HashMap[Int, ActorRef] //用于记录所有receivers的信息
val receivedBlockIds = new HashMap[Int, Queue[BlockId]] //用于记录每个InputDStream接受到的blockids
val timeout = AkkaUtils.askTimeout(ssc.conf)
// actor is created when generator starts.
// This not being null means the tracker has been started and not stopped
var actor: ActorRef = null
var currentTime: Time = null
/** Start the actor and receiver execution thread. */
def start() {
if (!networkInputStreams.isEmpty) {
actor = ssc.env.actorSystem.actorOf(Props(new NetworkInputTrackerActor), // 创建NetworkInputTrackerActor，用于和receivers通信
"NetworkInputTracker")
receiverExecutor.start() // 启动receiverExecutor
}
}
/** Stop the receiver execution thread. */
def stop() {
if (!networkInputStreams.isEmpty && actor != null) {
receiverExecutor.interrupt()
receiverExecutor.stopReceivers()
ssc.env.actorSystem.stop(actor)
logInfo("NetworkInputTracker stopped")
}
}
/** Return all the blocks received from a receiver. */
def getBlockIds(receiverId: Int, time: Time): Array[BlockId] = synchronized { //用于获取某个InputDStream相关的blockids
val queue = receivedBlockIds.synchronized {
receivedBlockIds.getOrElse(receiverId, new Queue[BlockId]())
}
val result = queue.synchronized {
queue.dequeueAll(x => true)
}
logInfo("Stream " + receiverId + " received " + result.size + " blocks")
result.toArray
}
/** Actor to receive messages from the receivers. */
private class NetworkInputTrackerActor extends Actor {
def receive = {
case RegisterReceiver(streamId, receiverActor) => { // Receiver向traker发送的register事件
receiverInfo += ((streamId, receiverActor)) // 将该Receiver加入receiverInfo
sender ! true
}
case AddBlocks(streamId, blockIds, metadata) => {
val tmp = receivedBlockIds.synchronized {
if (!receivedBlockIds.contains(streamId)) {
receivedBlockIds += ((streamId, new Queue[BlockId])) // Receiver通知tracker接受到新的block
}
receivedBlockIds(streamId)
}
tmp.synchronized {
tmp ++= blockIds
}
networkInputStreamMap(streamId).addMetadata(metadata)
}
case DeregisterReceiver(streamId, msg) => { // Receiver取消注册
receiverInfo -= streamId
}
}
}
/** This thread class runs all the receivers on the cluster. */
class ReceiverExecutor extends Thread {
val env = ssc.env
override def run() {
try {
SparkEnv.set(env)
startReceivers() //启动所有的Receivers
} catch {
case ie: InterruptedException => logInfo("ReceiverExecutor interrupted")
} finally {
stopReceivers()
}
}
/** * Get the receivers from the NetworkInputDStreams, distributes them to the * worker nodes as a parallel collection, and runs them. */
def startReceivers() {
val receivers = networkInputStreams.map(nis => { //取出所有networkInputStreams的receivers
val rcvr = nis.getReceiver()
rcvr.setStreamId(nis.id)
rcvr
})
// Right now, we only honor preferences if all receivers have them
val hasLocationPreferences = receivers.map(_.getLocationPreference().isDefined) //看看是否有LocationPreferences
.reduce(_ && _)
// Create the parallel collection of receivers to distributed them on the worker nodes
val tempRDD =
if (hasLocationPreferences) {
val receiversWithPreferences =
receivers.map(r => (r, Seq(r.getLocationPreference().toString)))
ssc.sc.makeRDD[NetworkReceiver[_]](receiversWithPreferences)
}
else {
ssc.sc.makeRDD(receivers, receivers.size) //makeRDD,使用ParallelCollectionRDD，这里其实就是将每个receiver封装成RDD的一个partition（task）
}
// Function to start the receiver on the worker node
val startReceiver = (iterator: Iterator[NetworkReceiver[_]]) => {
if (!iterator.hasNext) {
throw new Exception("Could not start receiver as details not found.")
}
iterator.next().start() //启动receiver
}
// Run the dummy Spark job to ensure that all slaves have registered.
// This avoids all the receivers to be scheduled on the same node.
if (!ssc.sparkContext.isLocal) {
ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
}
// Distribute the receivers and start them
ssc.sparkContext.runJob(tempRDD, startReceiver) //被封装成task的receiver会在workernode上调用startReceiver，而startReceiver最终调用receiver.start()
}
/** Stops the receivers. */
def stopReceivers() {
// Signal the receivers to stop
receiverInfo.values.foreach(_ ! StopReceiver)
}
}
}
