Scala Array和List的区别
Difference between Array and List in scala
Q:什么时候用Array(Buffer)和List(Buffer)?
A:Scala中的List是不可变的递归数据(immutable recursive data),是Scala中的一种基础结构,你应该多用List而不是Array(Array实际上是mutable,不可变(immutable)的Array是IndexedSeq)
Mutable Structures
ListBuffer提供一个常数时间的转换到List。
一个Scala的Array应该是由Java array生成的,因此一个Array[Int]也许比List[Int]更有效率。
但是,我认为Scala中数组尽量少用,因为它感觉是你真的需要知道底层发生了什么,来决定是否Array将所需的基本数据类型进行备份,或者可能boxed as a wrapper type.
Performance differences |
Array |
List |
Access the ith element |
O(1) |
O(i) |
Discard the ith element |
O(n) |
O(i) |
Insert an element at i |
O(n) |
O(i) |
Reverse |
O(n) |
O(n) |
Concatenate (length m,n) |
O(n+m) |
O(n) |
Calculate the length |
O(1) |
O(n) |
memory differences |
Array |
List |
Get the first i elements |
O(i) |
O(i) |
Drop the first i elements |
O(n-i) |
O(1) |
Insert an element at i |
O(n) |
O(i) |
Reverse |
O(n) |
O(n) |
Concatenate (length m,n) |
O(n+m) |
O(n) |
所以,除非你需要快速随机访问或需要count batches of elements,否则,列表比数组更好。
Scala快排List和Array数组效率实测
代码
- package com.tingfeng.scala.test
- import scala.annotation.tailrec
- import scala.util.{Random, Sorting}
- /**
- * 快速排序测试
- */
- object SortTest {
- /**
- * 初始化一个数组,产生随机数字填充
- * @param size
- * @return
- */
- def initRandomList(size :Int):List[Int]={
- val random = new Random()
- def initList(size :Int,random: Random):List[Int] = size match {
- case 0 => Nil
- case 1 => List(random.nextInt())
- case s:Int =>
- val value = s / 2
- if( s % 2 == 0) {
- initList(value,random) ++ initList(value,random)
- }else{
- initList(value,random) ++ initList(value + 1,random)
- }
- }
- initList(size,random)
- }
- /**
- * 打印出使用的时间
- * @param call
- */
- def printTime(call : => Unit,tag: String = ""){
- val startTime = System.currentTimeMillis()
- println(tag)
- call
- println
- println(s"use time : ${System.currentTimeMillis() - startTime}\n")
- }
- /**
- * 交换数组中两个位置的值,经过测试这种按位与的方式比普通建立变量交换的效率更高
- * @param array
- * @param x
- * @param y
- */
- def swap(array: Array[Int],x:Int,y:Int):Unit ={
- val t = array(x) ^ array(y)
- array(x) = t ^ array(x)
- array(y) = t ^ array(y)
- }
- /**
- * 将传入的值直接返回,并且执行逻辑
- * @param call
- * @param any
- * @tparam A
- */
- def doThing[A<:Any](any: A,call: A => Unit):A = {
- call(any)
- any
- }
- /**
- * 打印列表
- */
- def printList[A<%Seq[Any]](seq:A,size :Int = 10):Unit={
- seq.splitAt(size)._1.foreach(it => print(s"$it,"))
- }
- def shuffleIntSeq(seq: Array[Int],size: Int):Unit={
- val random = new Random()
- val maxSize = size/2
- for(i <- 0 to maxSize){
- swap(seq,i,maxSize + random.nextInt(maxSize))
- }
- }
- def main(args: Array[String]): Unit = {
- val size = 5000000
- val printSize = 10
- val list = initRandomList(size)
- //打印出钱100个,和List快速排序的时间花费
- printTime(printList[List[Int]](qSortList(list),Math.min(10,size)),"qSortList")
- val array = list.toArray
- printTime(printList[Array[Int]](doThing[Array[Int]](array,Sorting.quickSort),Math.min(printSize,size)),"Sorting.quickSort")
- shuffleIntSeq(array,size)
- printTime(printList[Array[Int]](doThing[Array[Int]](array,qSortArray1),Math.min(printSize,size)),"qSortArray1")
- shuffleIntSeq(array,size)
- printTime(printList[Array[Int]](doThing[Array[Int]](array,qSortArray2),Math.min(printSize,size)),"qSortArray2")
- shuffleIntSeq(array,size)
- printTime(printList[Array[Int]](doThing[Array[Int]](array,qSortArray3),Math.min(printSize,size)),"qSortArray3")
- shuffleIntSeq(array,size)
- printTime(printList[Array[Int]](doThing[Array[Int]](array,qSortArray4),Math.min(printSize,size)),"qSortArray4")
- }
- /**
- * 对List快速排序
- * @param list
- * @return
- */
- def qSortList(list: List[Int]):List[Int] = list match {
- case Nil => Nil
- case head :: other =>
- val (left, right) = other.partition(_ < head)
- (qSortList(left) :+ head) ++ qSortList(right)
- }
- /**
- * 通过每次比较数组‘head'值与其余值的方式直接实现
- * 比‘head'小的值移动到其前,比‘head'大的移动到其之后
- * @param array
- */
- def qSortArray1(array: Array[Int]):Unit = {
- def sort(ay : Array[Int],start: Int,end: Int):Unit={
- if(start >= end) {
- return
- }
- val head = ay(start)
- var spliteIndex = start
- for (i <- start + 1 to end){
- if(ay(i) < head){
- swap(array,spliteIndex,i)
- spliteIndex += 1
- }
- }
- if(start != spliteIndex){
- sort(ay, start, spliteIndex)
- }
- if(start == spliteIndex){
- spliteIndex += 1
- }
- if(spliteIndex != end){
- sort(ay, spliteIndex, end)
- }
- }
- sort(array,0,array.size - 1)
- }
- /**
- * 将数据以中线拆分左右两部分,交换值,使得右边值比左边大,
- * 再以左或者右边交换的界限分为两部分做递归
- * @param array
- */
- def qSortArray2(array: Array[Int]) {
- def sort(l: Int, r: Int) {
- val pivot = array((l + r) / 2)
- var lv = l; var rv = r
- while (lv <= rv) {
- while (array(lv) < pivot) lv += 1
- while (array(rv) > pivot) rv -= 1
- if (lv <= rv) {
- swap(array,lv, rv)
- lv += 1
- rv -= 1
- }
- }
- if (l < rv) sort(l, rv)
- if (rv < r) sort(lv, r)
- }
- sort(0, array.length - 1)
- }
- /**
- * 系统自带的过滤函数,无法排序成功,因为filter返回的是引用
- * @param xs
- * @return
- */
- def qSortArray3(xs: Array[Int]): Array[Int] ={
- if (xs.length <= 1){
- xs
- }else {
- val pivot = xs(xs.length / 2)
- val left = xs filter (pivot > _)
- val cu = xs filter (pivot == _ )
- val right = xs filter (pivot < _ )
- Array.concat(
- qSortArray3(left),cu,qSortArray3(right))
- }
- }
- /**
- * 系统自带的分割函数,无法排序成功,因为partition返回的是引用,数据量大的时候会栈溢出失败
- * @param xs
- * @return
- */
- def qSortArray4(array: Array[Int]): Array[Int] ={
- if (array.length <= 1){
- array
- }else {
- val head = array(0)
- val (left,right) = array.tail partition (_ < head )
- Array.concat(qSortArray4(left),Array(head),qSortArray4(right))
- }
- }
- }
测试结果
qSortList
-2147483293,-2147483096,-2147481318,-2147480959,-2147479572,-2147479284,-2147478285,-2147477579,-2147476191,-2147475936,
use time : 28808
Sorting.quickSort
-2147483293,-2147483096,-2147481318,-2147480959,-2147479572,-2147479284,-2147478285,-2147477579,-2147476191,-2147475936,
use time : 773
qSortArray1
-2147483293,-2147483096,-2147481318,-2147480959,-2147479572,-2147479284,-2147478285,-2147477579,-2147476191,-2147475936,
use time : 1335
qSortArray2
-2147483293,-2147483096,-2147481318,-2147480959,-2147479572,-2147479284,-2147478285,-2147477579,-2147476191,-2147475936,
use time : 629
qSortArray3
508128328,554399267,876118465,968407914,1274954088,1550124974,296879812,2125832312,1874291320,965362519,
use time : 10617
qSortArray4
865409973,-645195021,-735017922,-1893119148,1838343395,1038029591,-560471115,-182627393,-228613831,220531987,
use time : 6904
Process finished with exit code 0
环境:版本Scala2.12.6 , win10 ,ryzen5 1600 , 8G
以上为个人经验,希望能给大家一个参考,也希望大家多多支持w3xue。