使用javascript Array reduce()方法有什么好处吗?

时间:2012-03-09 05:36:52

标签: javascript arrays reduce

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

2 个答案:

答案 0 :(得分:5)

  • 你可能想要确定范围。例如,您可能想要创建回调函数或引用javascript对象。有关更多信息,请参阅为什么javascript未被阻止作用域。 [编辑:现代javascript现在支持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。
  • 您的代码并不总是需要以全机速运行。您可能甚至没有优化瓶颈中的代码。
  • 此外,您不提供“JSPerf测试”,因此我们可以批评它。例如,如果你已经有一个缩减函数(或map或forEach函数),那么我敢打赌,性能将与之相提并论。即使没有,测试方法也可能存在缺陷,特别是考虑到许多浏览器可能会有不同的优化或具有不同的函数调用开销。

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().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 loopfor reversefor 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