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()
}
}
