Benchmark: Array.prototype.map vs. Ramda.map

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js"></script>
<script type='text/javascript' src='https://cdnjs.cloudflare.com/ajax/libs/immutable/3.8.2/immutable.min.js'></script>

Script Preparation code:

function double(n) {
	return n*2;
}
var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30];

Tests:

Ramda.map
R.map(double, data);
Array.prototype.map
data.map(double);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:103.0) Gecko/20100101 Firefox/103.0

Browser/OS: Firefox 103 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
Ramda.map	4177805.0 Ops/sec
Array.prototype.map	5979048.5 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmarking test created on the MeasureThat.net website. The benchmark tests two approaches to performing array transformations: `Array.prototype.map` and Ramda's `map` function.

**What is being tested?**

In this benchmark, we are testing the performance of two functions:

1. **Array.prototype.map**: This is a built-in JavaScript method that applies a given function to each element of an array.
2. **Ramda.map**: This is a higher-order function from the Ramda library that applies a given function to each element of an array.

The benchmark compares the execution times of these two functions on a specific input dataset, which consists of 30 numbers.

**Options compared**

The benchmark is comparing two options:

1. **Array.prototype.map**
2. **Ramda.map**

Both approaches are performing the same operation: applying a function to each element of an array and returning a new array with the transformed values.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **Array.prototype.map**:
	* Pros:
		+ Built-in JavaScript method, which means it's widely supported and optimized for performance.
		+ Easy to use and understand.
	* Cons:
		+ Performance can vary depending on the browser and environment.
		+ May not be as efficient as other libraries or specialized implementations.
2. **Ramda.map**:
	* Pros:
		+ Optimized for performance, especially when used with large datasets.
		+ Provides a more functional programming style, which can lead to more concise and composable code.
	* Cons:
		+ Requires the Ramda library to be included in the project (which may add overhead).
		+ May have a higher learning curve due to its functional programming nature.

**Library: Ramda**

Ramda is a popular JavaScript library for functional programming. It provides a wide range of higher-order functions, including `map`, which applies a given function to each element of an array and returns a new array with the transformed values.

**Special JS feature or syntax**

There are no special JS features or syntaxes used in this benchmark, apart from the use of Ramda's `map` function. However, it's worth noting that some other libraries or implementations may use more advanced techniques or optimizations to achieve better performance.

**Other alternatives**

If you're looking for alternative approaches to array transformations, here are a few options:

1. **Lodash.map**: Lodash is another popular JavaScript library that provides a wide range of utility functions, including `map`.
2. **Immutable.js`: Immutable.js is a library that provides a way to work with immutable data structures in JavaScript.
3. **Built-in filter() and reduce() methods**: Some developers might consider using the `filter()` and `reduce()` methods for array transformations, although this approach can be less efficient than using specialized libraries or built-in methods like `map()`.

Keep in mind that the choice of implementation depends on the specific requirements and constraints of your project.

Related benchmarks:

Array.prototype.map vs. Ramda.map (version: 0)

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

Created: 6 years ago by: Guest

Jump to the latest result

Ramda.map

Array.prototype.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):