Benchmark: Reduce(spread) vs Map

Script Preparation code:

var a = [];
for (let i = 0; i < 50; i++) {
    a.push(Number(i) / 10)
}
var mapping = x => ({
    id: x
})
var reducing = (acc, x) => {
    return [...acc, {
        id: x
    }]
}

Tests:

Reduce
a.reduce(reducing, [])
Map
a.map(mapping)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
Map

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the details of this benchmark.

**Benchmark Description**

The benchmark compares two approaches to transforming an array of numbers: using `Array.prototype.reduce()` with a custom reducer function, and using `Array.prototype.map()` with a custom mapping function.

**Test Cases**

There are two test cases:

1. **Reduce**: This test uses the `reduce()` method with a custom reducer function `reducing`. The reducer takes an accumulator (`acc`) and an element (`x`) as input, and returns a new array by spreading the accumulator and adding a new object with the current element's value to it.
```javascript
var reducing = (acc, x) => {
  return [...acc, { id: x }]
}
```
2. **Map**: This test uses the `map()` method with a custom mapping function `mapping`. The mapping function takes an element (`x`) as input and returns a new object with the current element's value.
```javascript
var mapping = x => ({ id: x })
```

**Pros and Cons**

* **Reduce**:
	+ Pros: Can be more memory-efficient when dealing with large arrays, as it only needs to keep track of the accumulator array.
	+ Cons: May be slower than `map()` for small arrays or simple transformations, since it needs to iterate through the entire array even if the transformation is trivial.
* **Map**:
	+ Pros: Can be faster than `reduce()` for small arrays or simple transformations, as it only creates a new array with the transformed elements without needing an accumulator.
	+ Cons: May consume more memory than `reduce()` when dealing with large arrays, since it needs to create a new array with all the transformed elements.

**Other Considerations**

* **Library**: There is no external library used in this benchmark.
* **JS Feature or Syntax**: This test case uses ES6 features such as arrow functions and template literals. The `reduce()` method is also an older API, but still widely supported.
* **Alternatives**: Other alternatives to consider when transforming arrays include using `forEach()`, `every()`, or `some()`. However, these methods have different use cases and may not be the best choice for all scenarios.

In this specific benchmark, the results show that the `map()` approach is faster than the `reduce()` approach on Chrome 110 desktop. However, the actual performance difference will depend on the specifics of your use case and environment.

Related benchmarks:

Reduce(spread) vs Map (version: 0)

Comparing performance of: Reduce vs Map

Created: 3 years ago by: Registered User

Jump to the latest result

Reduce

Map

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):