InputDStream的继承关系。他们都是使用InputDStream这个抽象类的接口进行操作的。特别注意ReceiverInputDStream这个类，大部分时候我们使用的是它作为扩展的基类，因为它才能（更容易）使接收数据的工作分散到各个worker上执行，更符合分布式计算的理念。

所有的输入流都某个时间间隔将数据以block的形式保存到spark memory中，但以spark core不同的是，spark streaming默认是将对象序列化后保存到内存中。

/** * This is the abstract base class . This class provides methods * start() and stop() which is called by Spark Streaming system to . * Input streams that can  For example, * FileInputDStream, a subclass of InputDStream, monitors a HDFS directory from the driver for * new files and generates RDDs with the new files. . * * @param ssc_ Streaming context that will execute this input stream */abstract class T@transient extends Tprivatevar lastValidTimenull

  graphthis

/** * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]] * that has to start a receiver on worker nodes to receive external data. *  * @param ssc_ Streaming context that will execute this input stream * @tparam T Class type of the object of this stream */abstract class T@transient extends T/** Keeps all received blocks information */  private lazy val new , /** This is an unique identifier for the network input stream. */  val 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(): Receiver[T]

最终都是以BlockRDD返回的

/** Ask ReceiverInputTracker for received data blocks and generates RDDs with them. */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 blockInfo = ssc.scheduler.receiverTracker.getReceivedBlockInfo(id)receivedBlockInfo(validTime) = blockInfoval blockIds = blockInfo.map(_.blockId.asInstanceOf[BlockId])Some(new BlockRDD[T](ssc.sc, blockIds))  } else {Some(new BlockRDD[T](ssc.sc, Array[BlockId]()))  }}

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