Spark-Streaming中累加

import org.apache.spark.streaming.{Seconds, StreamingContext}import org.apache.spark.{HashPartitioner, SparkConf, SparkContext}/**  * Created by root on 2016/5/21.  */object StateFulWordCount {  //Seq这个批次某个单词的次数  //Option[Int]：以前的结果  //分好组的数据  val updateFunc = (iter: Iterator[(String, Seq[Int], Option[Int])]) => {    //iter.flatMap(it=>Some(it._2.sum + it._3.getOrElse(0)).map(x=>(it._1,x)))    //iter.map{case(x,y,z)=>Some(y.sum + z.getOrElse(0)).map(m=>(x, m))}    //iter.map(t => (t._1, t._2.sum + t._3.getOrElse(0)))    iter.map{ case(word, current_count, history_count) => (word, current_count.sum + history_count.getOrElse(0)) }  }  def main(args: Array[String]) {    LoggerLevels.setStreamingLogLevels()    //StreamingContext    val conf = new SparkConf().setAppName("StateFulWordCount").setMaster("local[2]")    val sc = new SparkContext(conf)    //updateStateByKey必须设置setCheckpointDir    sc.setCheckpointDir("c://ck")    val ssc = new StreamingContext(sc, Seconds(5))    val ds = ssc.socketTextStream("172.16.0.11", 8888)    //DStream是一个特殊的RDD    //hello tom hello jerry    val result = ds.flatMap(_.split(" ")).map((_, 1)).updateStateByKey(updateFunc, new HashPartitioner(sc.defaultParallelism), true)    result.print()    ssc.start()    ssc.awaitTermination()  }}