Benchmark: Object.fromEntries(array.map) vs. array.map.reduce

Script Preparation code:

var array = Array.from(Array(1000).keys());

Tests:

Object.fromEntries(array.map)
var metadata = Object.fromEntries(array.map(item => ({[item]: {}})));
array.reduce
var metadata = array.reduce((acc, value) => { acc[value] = {}; return acc; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object.fromEntries(array.map)
array.reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 28 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Object.fromEntries(array.map)	4573.5 Ops/sec
array.reduce	183616.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**Benchmark Definition**

The benchmark tests two JavaScript approaches for transforming an array of key-value pairs into an object with those keys and values. The two approaches are:

1. `Object.fromEntries(array.map())`: This method creates a new object from an array of key-value pairs.
2. `array.reduce()`: This method applies a reduction function to each element in the array, accumulating a result.

**Options Compared**

The benchmark compares the performance of these two approaches on the same dataset:

* The input array is created using `Array.from(Array(1000).keys())`, which generates an array of numbers from 0 to 999.
* The transformation is applied to this array using `Object.fromEntries()` and `array.reduce()`.
* The benchmark measures the number of executions per second (ExecutionsPerSecond) for each approach.

**Pros and Cons**

1. **`Object.fromEntries(array.map())`**:
	* Pros:
		+ Concise and expressive syntax.
		+ Modern JavaScript standardization.
		+ Fast execution (due to V8's optimization).
	* Cons:
		+ Requires modern browsers that support `Object.fromEntries()`.
		+ May have worse performance in older browsers or with large datasets.
2. **`array.reduce()`**:
	* Pros:
		+ Widely supported by older browsers and Node.js versions.
		+ Can be more efficient for smaller datasets or specific use cases.
	* Cons:
		+ Less concise and more verbose syntax compared to `Object.fromEntries()`.
		+ May require additional manual configuration (e.g., accumulator initialization).

**Library Usage**

None of the test cases explicitly use external libraries.

**Special JS Features/Syntax**

The benchmark uses the following modern JavaScript features:

* `Array.from()` (ECMAScript 2015+): Creates a new array from an array-like object or iterable.
* `Object.fromEntries()` (ECMAScript 2017+): Creates a new object from an iterable of key-value pairs.
* Template literals (`\r\n\t`) in the `array.reduce()` implementation.

**Other Alternatives**

If you need to transform an array of key-value pairs into an object, consider using other approaches, such as:

1. `Object.assign()`: A more traditional and widely supported method for creating objects from arrays.
2. `Array.prototype.forEach()` with callback functions: Another common approach for iterating over arrays and accumulating results.

However, keep in mind that the performance of these alternatives might vary depending on your specific use case and requirements.

In summary, the benchmark compares two modern JavaScript approaches for transforming arrays into objects: `Object.fromEntries(array.map())` and `array.reduce()`. The choice between these approaches depends on factors like compatibility requirements, dataset size, and personal preference.

Related benchmarks:

Object.fromEntries(array.map) vs. array.map.reduce (version: 0)

Comparing performance of: Object.fromEntries(array.map) vs array.reduce

Created: one year ago by: Registered User

Jump to the latest result

Object.fromEntries(array.map)

array.reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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