reduce()方法的大多数用例都可以使用for循环轻松重写。在JSPerf上进行测试表明,reduce()通常会慢60%-75%,具体取决于每次迭代中执行的操作。
除了能够以“功能样式”编写代码之外,还有任何真正的理由使用reduce()吗?如果通过编写更多代码可以获得60%的性能提升,那么为什么要使用reduce()?
编辑:事实上,其他功能方法如forEach()和map()都表现出类似的性能,比简单的循环慢至少60%。
这是JSPerf测试的链接(带有函数调用):forloop vs forEach
答案 0 :(得分:5)
let
变量。回到ESv6之前,当你声明一个var
变量时,它被提升,就像它被写在函数代码块的顶部一样,所以你经常需要编写for循环体作为函数。以下内容仍然适用:] 如果您编写了函数,除非它是一个重要的瓶颈,否则也可以使用函数式。旁注:这是语法之间的有效性能比较,但当语法不是手头的问题时,无效的性能比较:
myArray.map(function(x){return x+1})
// ...versus...
for(var i=0; i<myArray.length; i++) {
myArray[i] = myArray[i]+1;
}
这将是一个有效的性能比较:
myArray.forEach(function(x){return x+1})
// ...versus...
var plusOne = function(x){return x+1};
for(var i=0; i<myArray.length; i++) {
plusOne(myArray[i]);
}
// (may need a side-effect if the compiler is smart enough to optimize this)
(同样在回复您的修改时:.forEach()
和.map()
提供了更多清晰度,并且无需显式循环int i=0; i<array.length; i++
参数。)
答案 1 :(得分:0)
方法的性能可能会因数据大小而异。
速度也受编译器优化和数据预热的影响。
因此,在小数据for of
上获胜,在大数据reduce
上微不足道地获胜。
您可以通过运行测试来亲自查看:
const LOOP = 3
test(dataGenerator(5))
test(dataGenerator(500))
test(dataGenerator(50000))
test(dataGenerator(500000))
test(dataGenerator(5000000))
function test(dataSet) {
let sum
console.log('Data length:', dataSet.length)
for (let x = 0; x < LOOP; x++) {
sum = 0
console.time(`${x} reduce`)
sum = dataSet.reduce((s, d) => s += d.data, 0)
console.timeEnd(`${x} reduce`)
}
for (let x = 0; x < LOOP; x++) {
sum = 0
console.time(`${x} map`)
dataSet.map((i) => sum += i.data)
console.timeEnd(`${x} map`)
}
for (let x = 0; x < LOOP; x++) {
sum = 0
console.time(`${x} for loop`)
for (let i = 0; i < dataSet.length; i++) {
sum += dataSet[i].data
}
console.timeEnd(`${x} for loop`)
}
for (let x = 0; x < LOOP; x++) {
sum = 0
console.time(`${x} for reverse`)
for (let i = dataSet.length; i--;) {
sum += dataSet[i].data
}
console.timeEnd(`${x} for reverse`)
}
for (let x = 0; x < LOOP; x++) {
sum = 0
console.time(`${x} for of`)
for (const item of dataSet) {
sum += item.data
}
console.timeEnd(`${x} for of`)
}
for (let x = 0; x < LOOP; x++) {
sum = 0
console.time(`${x} for each`)
dataSet.forEach(element => {
sum += element.data
})
console.timeEnd(`${x} for each`)
}
console.log()
}
function dataGenerator(rows) {
const dataSet = []
for (let i = 0; i < rows; i++) {
dataSet.push({id: i, data: Math.floor(100 * Math.random())})
}
return dataSet
}
这些是我的笔记本电脑上的性能测试的结果。
for loop
与for reverse
和for of
不同,运行不稳定。
➜ node reduce_vs_for.js
Data length: 5
0 reduce: 0.127ms
1 reduce: 0.008ms
2 reduce: 0.006ms
0 map: 0.036ms
1 map: 0.007ms
2 map: 0.018ms
0 for loop: 0.005ms
1 for loop: 0.014ms
2 for loop: 0.004ms
0 for reverse: 0.009ms
1 for reverse: 0.005ms
2 for reverse: 0.004ms
0 for of: 0.008ms
1 for of: 0.004ms
2 for of: 0.004ms
0 for each: 0.046ms
1 for each: 0.003ms
2 for each: 0.003ms
Data length: 500
0 reduce: 0.031ms
1 reduce: 0.027ms
2 reduce: 0.026ms
0 map: 0.039ms
1 map: 0.036ms
2 map: 0.033ms
0 for loop: 0.029ms
1 for loop: 0.028ms
2 for loop: 0.028ms
0 for reverse: 0.027ms
1 for reverse: 0.026ms
2 for reverse: 0.026ms
0 for of: 0.051ms
1 for of: 0.063ms
2 for of: 0.051ms
0 for each: 0.030ms
1 for each: 0.030ms
2 for each: 0.027ms
Data length: 50000
0 reduce: 1.986ms
1 reduce: 1.017ms
2 reduce: 1.017ms
0 map: 2.142ms
1 map: 1.352ms
2 map: 1.310ms
0 for loop: 2.407ms
1 for loop: 12.170ms
2 for loop: 0.246ms
0 for reverse: 0.226ms
1 for reverse: 0.225ms
2 for reverse: 0.223ms
0 for of: 0.217ms
1 for of: 0.213ms
2 for of: 0.215ms
0 for each: 0.391ms
1 for each: 0.409ms
2 for each: 1.020ms
Data length: 500000
0 reduce: 1.920ms
1 reduce: 1.837ms
2 reduce: 1.860ms
0 map: 13.140ms
1 map: 12.762ms
2 map: 14.584ms
0 for loop: 15.325ms
1 for loop: 2.295ms
2 for loop: 2.014ms
0 for reverse: 2.163ms
1 for reverse: 2.138ms
2 for reverse: 2.182ms
0 for of: 1.990ms
1 for of: 2.009ms
2 for of: 2.108ms
0 for each: 2.226ms
1 for each: 2.583ms
2 for each: 2.238ms
Data length: 5000000
0 reduce: 18.763ms
1 reduce: 17.155ms
2 reduce: 26.592ms
0 map: 145.415ms
1 map: 135.946ms
2 map: 144.325ms
0 for loop: 29.273ms
1 for loop: 28.365ms
2 for loop: 21.131ms
0 for reverse: 21.301ms
1 for reverse: 27.779ms
2 for reverse: 29.077ms
0 for of: 19.094ms
1 for of: 19.338ms
2 for of: 26.567ms
0 for each: 22.456ms
1 for each: 26.224ms
2 for each: 20.769ms