2016 第四讲 Scala模式匹配、类型系统彻底精通与Spark源码阅读

20160104 19:00-21:00 课程笔录

Scala模式匹配：可以对值，类型，集合进行匹配

（1）值匹配

// a. data值为Spark,Hadoop或其他时

def bigData(data: String){

data match {

  case "Spark" => println("Wow!!!")

  case "Hadoop" => println("Ok")

  case _ => println(" Something others")

} 

} 

bigData:(data: String)Unit

>bigData("Spark")

Spark

>bigData("Hadoop")

Hadoop

>bigData("else")

Something others

//b. 注意 data值为Flink时的写法,  data_

def bigData(data: String){

data match {

case "Spark" => println("Wow!!!")

case "Hadoop" => println("Ok")

case data_  ifdata_ == "Flink" => println("Cool")

case _  => println ("Something else")

} 

} 

>bigData("Flink")

Cool

//c.

def bigData(data: String){

data match {

case "Spark" => println("Wow!!!")

case "Hadoop" => println("Ok")

case _  if data == "Flink" => println("Cool")

case _  => println("Something else")

} 

} 

>bigData("Flink")

//d. 

def bigData(data: String){

data match {

case "Spark" => println("Wow!!!")

case "Hadoop" => println("Ok")

case data  if data == "Flink" => println("Cool")

case _  => println("Something else")

} 

} 

>bigData("Flink")

(2)类型匹配

import java.io._

def exception(e:Exception) {

e match {

            case fileException: FileNotFoundException => println("File not found :" + fileException)

            case _: Exception => println("Exception getting thread dump from executor $executorId", e)

}

}

>exception(new FileNotFoundException(" Oops!!!")

(3)集合匹配

def data(array: Array[String]){

array match {

case Array("Scala") =>println("Scala")

case Array(spark, hadoop, flink) =>println(spark + " : " + hadoop + " : " + flink)

case Array("Spark", _*) => println("Spark ...")

case _ => println("Unknown")

}

}

>data(Array("Scala"))

Scala

>data(Array("hello","world","google"))

hello : world : google

>data(Array("Spark"))   

Spark...

>data(Array("Spark","test"))

Spark...

类型系统

//相当于java的Bean

case class  

case class Person

//实例化

case class Person(name: String)

>class Person

>case class Worker(name: String, salary: Double)  extends Person

>case class Student(name: String, score: Double)  extends Person

def sayHi(person: Person){

    person match {

        case Student(name, score) => println(name + score)

        case Worker(name, salary) => println(name + salary)

        case _ => println("Unknown")  

    }

}

>sayHi(Worker("Spark ",6.4))

>sayHi(Student("Spark ",6.5))

>sayHi(Student("Hadoop ",6.6)

sayHi(Worker("Spark ",6.4))

类型体系

泛型类、泛型函数、类型界定（upperBound）、视图界定（View Bounds）、协变和逆变等

类型参数，主要用于类型限定，加强代码的健壮性

1. 泛型类和泛型函数

2. 类型界定

对类型也可以限定边界，upperBounds（<:表示，上边界，是它或它的子类），（>:表示，下边界，是某个类的父类或类本身）

3. 视图界定

//

Manifest -> ClassTag

abstract class RDD[T: ClassTag](
@transient private var _sc: SparkContext,
@transient private var deps: Seq[Dependency[_]]
)

RDD[T: ClassTag]

T:ClassTag

//

{{{
* scala> def mkArray[T : ClassTag](elems: T*) = Array[T](elems: _*)
* mkArray: [T](elems: T*)(implicit evidence$1: scala.reflect.ClassTag[T])Array[T]
*
* scala> mkArray(42, 13)
* res0: Array[Int] = Array(42, 13)
*
* scala> mkArray("Japan","Brazil","Germany")
* res1: Array[String] = Array(Japan, Brazil, Germany)
* }}}

作业：

阅读Spark源码 RDD、HadoopRDD、SparkContext、Master、Worker的源码，

并分析里面使用的所有的模式匹配和类型参数的内容,  

从http://weibo.com/ilovepains获取代码 并完成作业。