初识SparkStreaming

写一个wordcount:

    linux命令: nc -lk 8888

package day01

import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

  SteamingWordCount {
  def main(args: Array[String]): Unit = {
  //SparkStreaming必须要有两个进程,一个拉取,一个计算

    val conf = new SparkConf().setAppName(\"SteamingWordCount\").setMaster(\"local[*]\")
    val sc = new SparkContext(conf)
    //创建一个SparkSteamingContext   批次的间隔为5秒切分一次
    val ssc = new StreamingContext(sc,Seconds(5))

    //创建一个DStream()  代表一系列的RDD
    //通过一个TCP端口拉取数据创建DStream
    val lines = ssc.socketTextStream(\"192.168.111.101\",8888)
    //对Dstream进行操作
    val words: DStream[String] = lines.flatMap(_.split(\" \"))
    val wordAndOne: DStream[(String, Int)] = words.map((_,1))
    val reduced: DStream[(String, Int)] =wordAndOne.reduceByKey(_+_)
    //展示结果
    reduced.print()
    //需要手动开启,然后会一直执行
    ssc.start()
    //等待命令停止
    ssc.awaitTermination()

//缺点在于只能单词记录,不能累加

  }
}

定义一个动态版的wordcount:

package day01

import org.apache.spark.{HashPartitioner, SparkConf, SparkContext}
import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.DStream

  StateFulWordCount {
  /**String,Seq[Int],Option[Int]
    *第一个:key即单词
    * 第二个:当前批次该单词出现的次数
    * 第三个:初始化或者是以前累加过的值
    */
  val updateFunc =(it:Iterator[(String,Seq[Int],Option[Int])])=>{
  //  it.map(t=>(t._1,t._2.sum + t._3.getOrElse(0)))
    it.map{ case (w,s,o) =>(w,s.sum+o.getOrElse(0))}
  }
  def main(args: Array[String]): Unit = {
   //SparkStreaming必须要有两个进程,一个拉取,一个计算

    val conf = new SparkConf().setAppName(\"SteamingWordCount\").setMaster(\"local[*]\")

    //创建一个SparkSteamingContext   批次的间隔为5秒切分一次
    val ssc = new StreamingContext(conf,Milliseconds(5000))
  //如果想要更新历史状态(累加),要设置checkpoint
    ssc.checkpoint(\"E:\\\\大数据\")
    //创建一个DStream()  代表一系列的RDD
    //通过一个TCP端口拉取数据创建DStream
    val lines = ssc.socketTextStream(\"192.168.111.101\",8888)
    //对Dstream进行操作
    val words: DStream[String] = lines.flatMap(_.split(\" \"))
    val wordAndOne: DStream[(String, Int)] = words.map((_,1))
    val reduced =wordAndOne.updateStateByKey(updateFunc,new HashPartitioner(ssc.sparkContext.defaultParallelism),true)
    //展示结果
    reduced.print()
    //需要手动开启,然后会一直执行
    ssc.start()
    //等待命令停止
    ssc.awaitTermination()
  }
}
//不同的是必须要定义一个checkpoint(\"/\")==> 把前面的数据保存起来
//需要自定义一个函数