Benchmark: Array.prototype.map vs Lodash.map 4.17.15

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.15/lodash.js"></script>

Script Preparation code:

var data = [1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8,];

Tests:

Array.prototype.map
data.map(function (number) { return number * 2});
Lodash.map
_.map(data, function (number) { return number * 2});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.prototype.map
Lodash.map

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 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
Array.prototype.map	7412017.0 Ops/sec
Lodash.map	14126708.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what's being tested.

**Benchmark Definition**

The benchmark is testing the performance of two approaches to transform an array: `Array.prototype.map` ( native JavaScript function) and `Lodash.map` (a library function).

**Options Compared**

The two options being compared are:

1. **Native JavaScript Function**: `Array.prototype.map`
2. **Lodash Library Function**: `_.map`

Both functions take an array as input, iterate over its elements, apply a transformation to each element, and return a new array with the transformed elements.

**Pros and Cons of Each Approach**

1. **Native JavaScript Function (`Array.prototype.map`)**:
	* Pros:
		+ Built-in function, no extra library required
		+ Typically faster since it's implemented in native code
		+ Simplistic syntax
	* Cons:
		+ Can be slower for very large datasets due to the overhead of the function call and object lookup
		+ May not be compatible with older browsers or environments that don't support modern JavaScript features
2. **Lodash Library Function (`_.map`)**:
	* Pros:
		+ More readable syntax, especially for complex transformations
		+ Often faster than native functions for large datasets due to Lodash's optimized implementation
		+ Can be more flexible and powerful with additional utility functions available in the library
	* Cons:
		+ Requires an extra library download and setup
		+ May introduce overhead due to the need to load and initialize the library

**Library Used: Lodash**

Lodash is a popular JavaScript utility library that provides a collection of functional programming helpers, including `_.map`. It's designed to make common tasks easier by providing pre-built functions for things like array manipulation, string manipulation, and object manipulation.

The version used in this benchmark is 4.17.15.

**Special JS Feature/Syntax: None**

There are no special JavaScript features or syntaxes being tested in this benchmark.

**Other Alternatives**

If you want to test alternative approaches for transforming arrays, you could consider:

1. **Arrow functions**: Instead of using the traditional `function` declaration, you could use an arrow function (`() => { code }`) to define the transformation function.
2. **Promises**: If you're working with asynchronous data or need to handle errors, you could use a promise-based approach to transform the array.
3. **Closures**: You could use closures to encapsulate the transformation logic and avoid passing external variables as arguments.

Keep in mind that these alternatives might not be directly comparable to `Array.prototype.map` or `Lodash.map`, as they often introduce additional complexity or overhead.

Related benchmarks:

Array.prototype.map vs Lodash.map 4.17.15 (version: 0)

Approaches performance

Comparing performance of: Array.prototype.map vs Lodash.map

Created: 5 years ago by: Guest

Jump to the latest result

Array.prototype.map

Lodash.map

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):