Benchmark: forEach vs slice().map

Tests:

forEach
const array = [{ id: 0, quantity: 2 },{ id: 1, quantity: 3 },{ id: 2, quantity: 4 },{ id: 3, quantity: 2 }] const copyArray = [] const copyArrayValues = (array, copyArray) => { array.forEach(value => { copyArray.push({ ...value, quantity: parseFloat(value.quantity) }) }) } copyArrayValues(array, copyArray) console.log(copyArray)
slice.map()
const array = [{ id: 0, quantity: 2 },{ id: 1, quantity: 3 },{ id: 2, quantity: 4 },{ id: 3, quantity: 2 }] const copyArray = array.slice().map(value => { return { ...value, quantity: parseFloat(value.quantity) } }) console.log(copyArray)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
forEach
slice.map()

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its implications.

**Benchmark Overview**

The benchmark compares two approaches to transform an array of objects into a new array with modified values: `forEach` versus using `slice().map()`. The goal is to determine which approach performs better in terms of execution speed.

**Options Compared**

1. **forEach**: This method iterates over the array elements using a callback function, where each element is processed individually.
2. **slice().map()**: This method uses the `Array.prototype.slice()` method to create a shallow copy of the original array and then applies the `Array.prototype.map()` method to transform its elements.

**Pros and Cons**

*   **forEach**:
    *   Pros: It's a simple, straightforward approach that doesn't require creating an intermediate array.
    *   Cons: The callback function might be slower due to the overhead of function calls and potential optimization issues in older browsers.
*   `slice().map()`:
    *   Pros: It creates an intermediate array (the result of `slice()`) which can be optimized by modern browsers. This approach also avoids the potential callback function slowdowns.
    *   Cons: Creating an intermediate array requires additional memory allocation and might not perform well for large input arrays.

**Library Used**

*   None explicitly mentioned in this benchmark, but if you're using JavaScript in a browser or environment that supports modern DOM APIs, libraries like jQuery (although not directly used here) are worth noting.

However, since there's no library being used here it is irrelevant

**Special JS Feature/Syntax**

The benchmark utilizes ES6 syntax features such as template literals (`...value`) and arrow functions (=>).

**Other Alternatives**

Some other approaches you could consider:

1.  **Use `for` loop instead of `forEach()`**: It's generally slower than `forEach`, but might be more cache-friendly.

```javascript
for (let i = 0; i < array.length; i++) {
    const copyArrayValues = [];
    for (const value of array) {
        copyArrayValues.push({ ...value, quantity: parseFloat(value.quantity) });
    }
}
```

2.  **Use a library like Lodash**: If you need to perform this transformation frequently, using a dedicated library can provide benefits in terms of code readability and execution speed.

```javascript
import _ from 'lodash';

const array = [...]; // your array

const copyArrayValues = _.map(array, value => ({ ...value, quantity: parseFloat(value.quantity) }));

console.log(copyArrayValues);
```

3.  **Use a custom implementation**: Depending on the specific requirements of your project, you might be able to optimize this transformation even further using assembly-like code (with WebAssembly) or specialized libraries.

**Notes**

Always consider factors such as memory allocation, cache locality and potential browser-specific quirks when benchmarking JavaScript code.

Related benchmarks:

forEach vs slice().map (version: 0)

Comparing performance of: forEach vs slice.map()

Created: 3 years ago by: Guest

Jump to the latest result

forEach

slice.map()

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):