1. DStream的类说明文档：
/** * A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous * sequence of RDDs (of the same type) representing a continuous stream of data (see * org.apache.spark.rdd.RDD in the Spark core documentation for more details on RDDs). * DStreams can either be created from live data (such as, data from TCP sockets, Kafka, Flume, * etc.) using a [[org.apache.spark.streaming.StreamingContext]] or it can be generated by * transforming existing DStreams using operations such as `map`, * `window` and `reduceByKeyAndWindow`. While a Spark Streaming program is running, each DStream * periodically generates a RDD, either from live data or by transforming the RDD generated by a * parent DStream. * * This class contains the basic operations available on all DStreams, such as `map`, `filter` and * `window`. In addition, [[org.apache.spark.streaming.dstream.PairDStreamFunctions]] contains * operations available only on DStreams of key-value pairs, such as `groupByKeyAndWindow` and * `join`. These operations are automatically available on any DStream of pairs * (e.g., DStream[(Int, Int)] through implicit conversions. * * DStreams internally is characterized by a few basic properties: * - A list of other DStreams that the DStream depends on * - A time interval at which the DStream generates an RDD * - A function that is used to generate an RDD after each time interval */
从上面可以看出来，
1. DStream每隔一段时间仅产生一个RDD
2. DStream如同RDD，也有依赖关系，比如通过转换算子，把一个DStream转换成另外一个DStream
3. DStream需要有一个方法，在指定的时间间隔到达后，用于创建RDD。这个方法同RDD计算它包含的数据行一样，是compute方法
/** Method that generates a RDD for the given time */ def compute (validTime: Time): Option[RDD[T]]
2. DStream的实现类ReceiverInputDStream实现的compute方法
/**
* Generates RDDs with blocks received by the receiver of this stream. */
///根据在一个时间范围内获取到的数据创建RDD(这些接收到的数据由BlockManager管理)
override def compute(validTime: Time): Option[RDD[T]] = {
val blockRDD = {
////如果时间不在要计算的范围内，则返回空RDD(具体是BlockRDD）
if (validTime < graph.startTime) {
// 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
// driver failure without any write ahead log to recover pre-failure data.
new BlockRDD[T](ssc.sc, Array.empty)
} else {
// Otherwise, ask the tracker for all the blocks that have been allocated to this stream
// for this batch
//根据validTime获取Block信息，validTime和Block的对应关系放在一个Map中，因此，validTime是Block的索引，
//因此可以把validTime看成是一个块创建的时间点(此后，一个时间间隔的数据都写入这个块中)
//id is an unique identifier for the receiver input stream.
val blockInfos =
ssc.scheduler.receiverTracker.getBlocksOfBatch(validTime).get(id).getOrElse(Seq.empty)
//对blockInfos中的每个元素（ReceivedBlockInfo）调用blockStoreResult中
val blockStoreResults = blockInfos.map { _.blockStoreResult }
val blockIds = blockStoreResults.map { _.blockId.asInstanceOf[BlockId] }.toArray
// Check whether all the results are of the same type
val resultTypes = blockStoreResults.map { _.getClass }.distinct
if (resultTypes.size > 1) {
logWarning("Multiple result types in block information, WAL information will be ignored.")
}
// If all the results are of type WriteAheadLogBasedStoreResult, then create
// WriteAheadLogBackedBlockRDD else create simple BlockRDD.
if (resultTypes.size == 1 && resultTypes.head == classOf[WriteAheadLogBasedStoreResult]) {
val logSegments = blockStoreResults.map {
_.asInstanceOf[WriteAheadLogBasedStoreResult].segment
}.toArray
// Since storeInBlockManager = false, the storage level does not matter.
new WriteAheadLogBackedBlockRDD[T](ssc.sparkContext,
blockIds, logSegments, storeInBlockManager = false, StorageLevel.MEMORY_ONLY_SER)
} else {
///构造BlockRDD的参数是blockIds，也就是这个RDD包含的数据存放在BlockManager中，可以由blockId读取指定的块
new BlockRDD[T](ssc.sc, blockIds)
}
}
}
Some(blockRDD)
}
3. InputDStream的类说明文档
/** * 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.ReceiverInputDStream]] as the parent class. * * @param ssc_ Streaming context that will execute this input stream */
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InputDStream定义了接收和停止接收数据的方法(start和stop),InputDStream的直接实现者不需要定义专门的Receiver，它在Driver上即可完成生成RDD的任务。
比如FileInputDStream即是这样一个InputDStream，它监听本地或者HDFS上的新文件，然后生成RDD。
问题：如此说来，FileInputDStream是一个没有实现分布式的数据接收器？
4. ReceiverInputDStream
/** * 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 ReceiverInputDStream must * define `the getReceiver()` function that gets the receiver object of type * [[org.apache.spark.streaming.receiver.Receiver]] 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 */
ReceiverInputDStream的实现者需要实现getReceiver来回去数据接收器，这些Reciver将运行在所有的Worker节点上。
5. 总结
从上面的分析中，DStream是InputDStream的父类，而InputDStream是ReceiverInputDStream的父类。
DStream相对于InputDStream，更像是个RDD，即只是一个包含接收数据的一系列的集合，即DStream是一个数据集。
InputDStream在此基础之上需要实现启动接收和停止接收的逻辑，也就是，InputDStream更像是自来水管的水龙头开关，而DStream是流出来的自来水。
ReceiverInputStream将接收数据的逻辑进行分布式接收