DStream是类似于RDD概念，是对数据的抽象封装。它是一序列的RDD，事实上，它大部分的操作都是对RDD支持的操作的封装，不同的是，每次DStream都要遍历它内部所有的RDD执行这些操作。它可以由StreamingContext通过流数据产生或者其他DStream使用map方法产生（与RDD一样）
time属性对DStream而言非常重要，DStream里面的RDD就是通过某个时间间隔产生的，而且以产生的时间为索引。所以在访问DStream的某个RDD时，实际上是访问它在某个时间点的RDD。
/**
* 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 when
* `org.apache.spark.streaming.StreamingContext._` is imported.
*
* 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
*/
abstract class DStream[T: ClassTag] (
@transient private[streaming] var ssc: StreamingContext
) extends Serializable with Logging {
重要属性：
// =======================================================================
// Methods that should be implemented by subclasses of DStream
// =======================================================================
/** Time interval after which the DStream generates a RDD */
def slideDuration: Duration
/** List of parent DStreams on which this DStream depends on */
def dependencies: List[DStream[_]]
/** Method that generates a RDD for the given time */
def compute (validTime: Time): Option[RDD[T]]
当前已经产生了的RDD，以产生的时间为索引
// =======================================================================
// Methods and fields available on all DStreams
// =======================================================================
// RDDs generated, marked as private[streaming] so that testsuites can access it
@transient
private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] ()
为某个时间点产生一个RDD
/**
* Get the RDD corresponding to the given time; either retrieve it from cache
* or compute-and-cache it.
*/
private[streaming] def getOrCompute(time: Time): Option[RDD[T]] = {
