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Benchmarking array functions
var Benchmark = require('benchmark');
var suite = new Benchmark.Suite();
const testMap = (array, transform) => {
return array.map(item => transform(item));
};
const testReduce = (array, transform) => {
return array.reduce((acc, item) => acc.concat(transform(item)), []);
};
const testForEach = (array, transform) => {
let result = [];
array.forEach(item => {
result.push(transform(item));
});
return result;
};
const testForLoop = (array, transform) => {
let result = [];
for (i = 0; i < array.length; i++) {
result.push(transform(array[i]));
}
return result;
};
const testForEachInPlace = (array, transform) => {
array.forEach((item, i) => {
array[i] = transform(item);
});
return array;
};
const testForLoopInPlace = (array, transform) => {
for (i = 0; i < array.length; i++) {
array[i] = transform(array[i]);
}
return array;
};
const runSuite = (createArray, transform) => {
suite
.add('map', testMap.bind(null, createArray(), transform))
.add('reduce', testReduce.bind(null, createArray(), transform))
.add('forEach', testForEach.bind(null, createArray(), transform))
.add('for-loop', testForLoop.bind(null, createArray(), transform))
.add(
'forEach in-place',
testForEachInPlace.bind(null, createArray(), transform)
)
.add(
'for-loop in-place',
testForLoopInPlace.bind(null, createArray(), transform)
)
.on('cycle', event => {
console.log(String(event.target));
})
.on('complete', function() {
console.log('Fastest is ' + this.filter('fastest').map('name'));
})
.run({ async: false });
};
runSuite(
() => Array.from(Array(1000).map(() => ({ key: 'value' }))),
item => ({
...item,
// idempotent by design so repeated transforms makes no difference
key: 'value',
})
);
// these don't play nice right now so you might need to comment one `runSuite` call out to run the other one
runSuite(() => Array.from(Array(1000).keys()), item => item + 1);
// Results:
// Array of 1000 integers in ascending order, mapping with incremented values
// map x 386,175 ops/sec ±13.96% (91 runs sampled)
// reduce x 257 ops/sec ±1.34% (85 runs sampled)
// forEach x 193,596 ops/sec ±0.78% (92 runs sampled)
// for-loop x 190,224 ops/sec ±0.36% (96 runs sampled)
// for-loop in-place x 444,796 ops/sec ±0.61% (93 runs sampled)
// forEach in-place x 805,311 ops/sec ±19.99% (92 runs sampled)
// Fastest is forEach in-place
// Array of 1000 objects, mapping with merged key/value pair
// map x 48,774 ops/sec ±11.75% (86 runs sampled)
// reduce x 215 ops/sec ±2.39% (83 runs sampled)
// forEach x 45,489 ops/sec ±2.12% (92 runs sampled)
// for-loop x 48,263 ops/sec ±4.41% (93 runs sampled)
// forEach in-place x 3,440 ops/sec ±0.69% (90 runs sampled)
// for-loop in-place x 3,430 ops/sec ±1.31% (91 runs sampled)
// Fastest is for-loop
// Conclusions:
// If in-place mutation is off the table, `.map` is by far superior to the alternatives for simple transformations like incrementing numbers. If you allow for in-place mutation, forEach takes the cake, being roughly twice as fast as both `.map` and the in-place for loop.
// When the transformation requires creating a new object e.g. when merging a key/value pair into an object, everything is slower, but the for-loop is the fastest. The in-place `.forEach` and for-loop approaches are notably quite slow compared to in the previous case. I suspect this is because objects don't have fixed size, meaning replacing an object at a given index in an array full of objects is a more expensive operation than simply appending to a new array. We might see an increase in speed if we were simply overwriting a single key on the object rather than replacing the object with a new one in each iteration.
// In all cases, reduce is the slowest by a large margin. This would be because it has to recreate a new array in each iteration and populate it. No doubt in situations where it's cheaper to create the new value of the accumulator, the difference would not be as steep.
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