DStream上的原语与RDD的类似，分为Transformations（转换）和Output Operations（输出）两种，此外转换操作中还有一些比较特殊的原语，如：updateStateByKey()、transform()以及各种Window相关的原语。
1.Transformations on DStreams
| Transformation | Meaning |
|---|---|
| map(func) | Return a new DStream by passing each element of the source DStream through a function func. |
| flatMap(func) | Similar to map, but each input item can be mapped to 0 or more output items. |
| filter(func) | Return a new DStream by selecting only the records of the source DStream on which func returns true. |
| repartition(numPartitions) | Changes the level of parallelism in this DStream by creating more or fewer partitions. |
| union(otherStream) | Return a new DStream that contains the union of the elements in the source DStream and otherDStream. |
| count() | Return a new DStream of single-element RDDs by counting the number of elements in each RDD of the source DStream. |
| reduce(func) | Return a new DStream of single-element RDDs by aggregating the elements in each RDD of the source DStream using a function func (which takes two arguments and returns one). The function should be associative so that it can be computed in parallel. |
| countByValue() | When called on a DStream of elements of type K, return a new DStream of (K, Long) pairs where the value of each key is its frequency in each RDD of the source DStream. |
| reduceByKey(func, [numTasks]) | When called on a DStream of (K, V) pairs, return a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Note: By default, this uses Spark’s default number of parallel tasks (2 for local mode, and in cluster mode the number is determined by the config property spark.default.parallelism) to do the grouping. You can pass an optional numTasks argument to set a different number of tasks. |
| join(otherStream, [numTasks]) | When called on two DStreams of (K, V) and (K, W) pairs, return a new DStream of (K, (V, W)) pairs with all pairs of elements for each key. |
| cogroup(otherStream, [numTasks]) | When called on a DStream of (K, V) and (K, W) pairs, return a new DStream of (K, Seq[V], Seq[W]) tuples. |
| transform(func) | Return a new DStream by applying a RDD-to-RDD function to every RDD of the source DStream. This can be used to do arbitrary RDD operations on the DStream. |
| updateStateByKey(func) | Return a new “state” DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values for the key. This can be used to maintain arbitrary state data for each key. |
特殊的Transformations
1.UpdateStateByKey Operation
UpdateStateByKey原语用于记录历史记录，上文中Word Count示例中就用到了该特性。若不用UpdateStateByKey来更新状态，那么每次数据进来后分析完成后，结果输出后将不在保存
2.Transform Operation
Transform原语允许DStream上执行任意的RDD-to-RDD函数。通过该函数可以方便的扩展Spark API。此外，MLlib（机器学习）以及Graphx也是通过本函数来进行结合的。
3.Window Operations
Window Operations有点类似于Storm中的State，可以设置窗口的大小和滑动窗口的间隔来动态的获取当前Steaming的允许状态。
