option的匹配

val map = Map(("zhangsan",2000),("lisi",2500),("wangwu",3000))
val option:Any = map.get("zhangsan")
option match {
  case Some(v) =>println(v)
  case None=>println("nothing")
  case _=>println()
}

作业题：

object Test2 {
  def main(args: Array[String]): Unit = {
    //mr mapper lineRecordReader iterator list
     val lines:Iterator[String] = Source.fromFile("log.txt").getLines()
//     println("site3,,".split(",",-1).length)
    val filterData:Iterator[String] = lines.filter(_.split(",").length==3)
    //1 site 2 user 3 time
    //day uv pv
    val data1:Iterator[(String,String,String)] = filterData.map(t=>{
      val strs:Array[String] = t.split(",")
      (strs(0),strs(1),strs(2).split(" ")(0))
      //site user day
    })
    val data2:Map[(String,String),List[(String,String,String)]] = data1.toList.groupBy(t=>(t._1,t._3))

   val dayPV = data2.mapValues(_.length)

    dayPV.foreach(println)

    val dayUV = data2.mapValues(t=>t.map(_._2).distinct.length)

    dayUV.foreach(println)
  }
}
//site4,user5,
//site3,,
//site1,user1,2018-03-02 02:25:25
object Test3 {
  def main(args: Array[String]): Unit = {
    //mr mapper lineRecordReader iterator list
    val lines:Iterator[String] = Source.fromFile("log.txt").getLines()
    //     println("site3,,".split(",",-1).length)
    val filterData:Iterator[String] = lines.filter(_.split(",").length==3)
    //1 site 2 user 3 time
    //day uv pv
    val data1:Iterator[(String,String,String,String)] = filterData.map(t=>{
      val strs:Array[String] = t.split(",")
      val times = strs(2).split(" ")
      val hour = times(1).split(":")(0)
      (strs(0),strs(1),times(0),hour)
      //site user day
    })
    val data2:Map[(String,String,String),List[(String,String,String,String)]] = data1.toList.groupBy(t=>(t._1,t._3,t._4))

    val hourPV = data2.mapValues(_.length)

    hourPV.foreach(println)

    println("**********************")

    val hourUV = data2.mapValues(t=>t.map(_._2).distinct.length)

    hourUV.foreach(println)
  }
}

object MovieTest {
  def main(args: Array[String]): Unit = {
    val ratingData = Source.fromFile("ratings.txt").getLines()
    val movieData = Source.fromFile("movies.txt").getLines()
    val ratingData1:Iterator[(String,String,String)] = ratingData.map(t=>{
      val strs = t.split(",")
      (strs(0),strs(1),strs(2))
      //uid mid
    })
    val movieData1:Iterator[(String,String)] = movieData.map(t=>{
      val strs = t.split(",")
      (strs(0),strs(strs.length-1))
      //mid types
    })
    // mid type
      val mAndTypes:Map[String,String] = movieData1.toMap

   val umScore:Iterator[(String,String,String)] = ratingData1.flatMap(t=>{
      //t uid mid score
     val types:String = mAndTypes(t._2) // 动作|惊悚|犯罪
//      (t._1,t._3,types)
      //uid score types
      val typess = types.split("\\|")
      val userMovieScore:Array[(String,String,String)] = typess.map(e=>{
        (t._1,t._3,e)
        //uid score type
      })
      userMovieScore
    })
                      //uid    type          uid    score   type
    val lt = umScore.toList
    val groupData:Map[(String,String),List[(String,String,String)]] = lt.groupBy(t=>(t._1,t._3))

    val utypeAvg:Map[(String,String),Double] = groupData.mapValues(t=>{
      val avg = t.map(_._2.toDouble).sum/t.length
      avg
    })

    val list:List[((String,String),Double)] = utypeAvg.toList
    val list1:List[(String,String,Double)] =list.map(t=>{
      (t._1._1,t._1._2,t._2)
    })

    val groupList:Map[String,List[(String,String,Double)]] = list1.groupBy(_._1)

    val result:Map[String,(String,Double)] = groupList.mapValues(t=>{
      val lst: List[(String, String, Double)] = t.sortBy(-_._3)
      (lst(0)._2,lst(0)._3)
    })

    result.foreach(println)
  }
}

偏函数

专门是匹配的函数

scala> var arr = Array(1,2,3,4,5,6)

arr: Array[Int] = Array(1, 2, 3, 4, 5, 6)

scala> def pf:PartialFunction[Int,Int]={

| case x=>x*10

| }

pf: PartialFunction[Int,Int]

scala> arr.map(pf)

res1: Array[Int] = Array(10, 20, 30, 40, 50, 60)

scala> var arr = Array(("zhangsan",2000),("lisi",2500))

arr: Array[(String, Int)] = Array((zhangsan,2000), (lisi,2500))

scala> def pf:PartialFunction[(String,Int),(String,Int)]={

| case (x,y)=>(x,y+1000)

| }

pf: PartialFunction[(String, Int),(String, Int)]

scala> arr.map(pf)

res2: Array[(String, Int)] = Array((zhangsan,3000), (lisi,3500))

定义偏函数

def methodName:PartitionFunction[inType,outType]={

case =>

}

AKKA

akka是一个通信机制，相当于hadoop中的RPC协议

akka就是spark1.6以前的通信协议，1.6以后使用的通信协议是netty

akka相当于多线程 ---> 多线程 --->单线程处理能力差不能解决并发的问题---->多线程就是多个线程一起工作 ---> 线程数据混乱（多线程之间不会进行通信）

python mysql

hadoop

scala(06)----(AKKA)

AKKA

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