scala – Spark流示例使用其他参数调用updateStateByKey

>您可以看到不同的Spark版本分支：https://github.com/apache/spark/blob/branch-1.3/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala.在Spark 1.2中,此代码只使用updateStateByKey接收单个函数作为参数,而在1.3中它们已经对它进行了优化
> 1.2和1.3中都存在不同版本的updateStateByKey.但是在1.2中没有带有4个参数的版本,它仅在1.3：https://github.com/apache/spark/blob/branch-1.3/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala中引入

这是代码：

/**
* 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 of each key.
* org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. Note,that this function may generate a different
* tuple with a different key than the input key. Therefore keys may be removed
* or added in this way. It is up to the developer to decide whether to
* remember the partitioner despite the key being changed.
* @param partitioner Partitioner for controlling the partitioning of each RDD in the new
* DStream
* @param rememberPartitioner Whether to remember the paritioner object in the generated RDDs.
* @param initialRDD initial state value of each key.
* @tparam S State type
*/
def updateStateByKey[S: ClassTag](
    updateFunc: (Iterator[(K,Seq[V],Option[S])]) => Iterator[(K,S)],partitioner: Partitioner,rememberPartitioner: Boolean,initialRDD: RDD[(K,S)]
): DStream[(K,S)] = {
    new StateDStream(self,ssc.sc.clean(updateFunc),partitioner,rememberPartitioner,Some(initialRDD))
}