272 lines
5.9 KiB
Markdown
272 lines
5.9 KiB
Markdown
# Scala映射和元组
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## 一、映射(Map)
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### 1.1 构造映射
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```scala
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scala> import scala.collection.immutable.HashMap
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import scala.collection.immutable.HashMap
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// 初始化一个空map
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scala> val scores01 = new HashMap[String, Int]
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scores01: scala.collection.immutable.HashMap[String,Int] = Map()
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// 从指定的值初始化映射（方式一）
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scala> val scores02 = Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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scores02: scala.collection.immutable.Map[String,Int] = Map(hadoop -> 10, spark -> 20, storm -> 30)
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// 从指定的值初始化映射（方式二）
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scala> val scores03 = Map(("hadoop", 10), ("spark", 20), ("storm", 30))
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scores03: scala.collection.immutable.Map[String,Int] = Map(hadoop -> 10, spark -> 20, storm -> 30)
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```
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采用上面方式得到的都是不可变(immutable)映射，想要得到可变映射，则用：
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```scala
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scala> val scores04 = scala.collection.mutable.Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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scores04: scala.collection.mutable.Map[String,Int] = Map(spark -> 20, hadoop -> 10, storm -> 30)
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```
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### 1.2 获取值
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```scala
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object ScalaApp extends App {
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val scores = Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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// 1.获取指定key对应的值
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println(scores("hadoop"))
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// 2. 如果对应的值不存在则使用默认值
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println(scores.getOrElse("hadoop01", 100))
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}
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```
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### 1.3 新增/修改/删除值
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可变映射允许进行新增、修改、删除等操作。
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```scala
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object ScalaApp extends App {
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val scores = scala.collection.mutable.Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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// 1.如果key存在则更新
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scores("hadoop") = 100
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// 2.如果key不存在则新增
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scores("flink") = 40
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// 3.可以通过+=来进行多个更新或新增操作
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scores += ("spark" -> 200, "hive" -> 50)
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// 4.可以通过-= 来移除某个键和值
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scores -= "storm"
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for (elem <- scores) {println(elem)}
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}
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// 输出内容如下
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(spark,200)
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(hadoop,100)
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(flink,40)
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(hive,50)
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```
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不可变映射不允许进行新增、修改、删除等操作，但是允许由不可变映射产生新的映射。
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```scala
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object ScalaApp extends App {
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val scores = Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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val newScores = scores + ("spark" -> 200, "hive" -> 50)
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for (elem <- scores) {println(elem)}
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}
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// 输出内容如下
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(hadoop,10)
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(spark,200)
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(storm,30)
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(hive,50)
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```
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### 1.4 遍历映射
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```java
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object ScalaApp extends App {
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val scores = Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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// 1. 遍历键
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for (key <- scores.keys) { println(key) }
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// 2. 遍历值
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for (value <- scores.values) { println(value) }
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// 3. 遍历键值对
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for ((key, value) <- scores) { println(key + ":" + value) }
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}
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```
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### 1.5 产生新映射
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可以使用`yield`关键字从现有映射产生新的映射。
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```scala
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object ScalaApp extends App {
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val scores = Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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// 1.将scores中所有的值扩大10倍
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val newScore = for ((key, value) <- scores) yield (key, value * 10)
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for (elem <- newScore) { println(elem) }
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// 2.将键和值互相调换
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val reversalScore: Map[Int, String] = for ((key, value) <- scores) yield (value, key)
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for (elem <- reversalScore) { println(elem) }
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}
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// 输出
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(hadoop,100)
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(spark,200)
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(storm,300)
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(10,hadoop)
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(20,spark)
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(30,storm)
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```
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### 1.6 有序映射
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在使用Map时候，如果不指定，默认使用的是HashMap，如果想要使用`TreeMap`或者`LinkedHashMap`，则需要显式的指定。
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```scala
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object ScalaApp extends App {
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// 1.使用TreeMap,按照键的字典序进行排序
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val scores01 = scala.collection.mutable.TreeMap("B" -> 20, "A" -> 10, "C" -> 30)
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for (elem <- scores01) {println(elem)}
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// 2.使用LinkedHashMap,按照键值对的插入顺序进行排序
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val scores02 = scala.collection.mutable.LinkedHashMap("B" -> 20, "A" -> 10, "C" -> 30)
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for (elem <- scores02) {println(elem)}
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}
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// 输出
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(A,10)
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(B,20)
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(C,30)
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(B,20)
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(A,10)
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(C,30)
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```
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### 1.7 其他方法
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```scala
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object ScalaApp extends App {
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val scores = scala.collection.mutable.TreeMap("B" -> 20, "A" -> 10, "C" -> 30)
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// 1. 获取长度
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println(scores.size)
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// 2. 判断是否为空
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println(scores.isEmpty)
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// 3. 判断是否包含特定的key
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println(scores.contains("A"))
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}
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```
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### 1.8 与Java互操作
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```scala
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import java.util
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import scala.collection.{JavaConverters, mutable}
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object ScalaApp extends App {
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val scores = Map("hadoop" -> 10, "spark" -> 20, "storm" -> 30)
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// scala map转java map
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val javaMap: util.Map[String, Int] = JavaConverters.mapAsJavaMap(scores)
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// java map转scala map
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val scalaMap: mutable.Map[String, Int] = JavaConverters.mapAsScalaMap(javaMap)
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for (elem <- scalaMap) {println(elem)}
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}
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```
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## 二、元组(Tuple)
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元组与数组类似，但是数组中所有的元素必须是同一种类型，而元组则可以包含不同类型的元素。
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```scala
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scala> val tuple=(1,3.24f,"scala")
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tuple: (Int, Float, String) = (1,3.24,scala)
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```
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### 2.1 模式匹配
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可以通过模式匹配来进行获取元组中的值并赋予对应的变量：
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```scala
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scala> val (a,b,c)=tuple
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a: Int = 1
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b: Float = 3.24
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c: String = scala
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```
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如果某些位置不需要赋值，则可以使用下划线代替：
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```scala
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scala> val (a,_,_)=tuple
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a: Int = 1
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```
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### 2.2 Zip方法
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```scala
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object ScalaApp extends App {
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val array01 = Array("hadoop", "spark", "storm")
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val array02 = Array(10, 20, 30)
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// 1.zip方法得到的是多个tuple组成的数组
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val tuples: Array[(String, Int)] = array01.zip(array02)
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// 2.也可以在zip后调用toMap方法转换为映射
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val map: Map[String, Int] = array01.zip(array02).toMap
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for (elem <- tuples) { println(elem) }
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for (elem <- map) {println(elem)}
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}
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// 输出
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(hadoop,10)
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(spark,20)
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(storm,30)
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(hadoop,10)
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(spark,20)
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(storm,30)
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```
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## 参考资料
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1. Martin Odersky . Scala编程(第3版)[M] . 电子工业出版社 . 2018-1-1
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2. 凯.S.霍斯特曼 . 快学Scala(第2版)[M] . 电子工业出版社 . 2017-7 |