Benchmark: Ramda map vs Array.map vs for

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.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
R.map(double, data);
Array (native)
data.map(double);
for i native
const res = []; for (let i = 0; i < data.length; i++) { res.push(double(data[i])); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Array (native)
for i native

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided benchmark and its test cases.

**Benchmark Overview**

The benchmark compares the performance of three approaches to perform a simple mapping operation on an array: Ramda's `R.map` function, JavaScript's native `Array.map` method, and a manual implementation using a `for` loop. The goal is to measure which approach is the fastest.

**Options Compared**

1. **Ramda's `R.map`**: A functional programming library that provides a mapping function (`map`) for working with arrays.
2. **JavaScript's native `Array.map`**: A built-in method for mapping over an array, which is called when using `data.map(double)`.
3. **Manual implementation using `for` loop**: A straightforward approach to iterate over the array and apply the transformation to each element.

**Pros and Cons of Each Approach**

1. **Ramda's `R.map`**:
	* Pros: concise code, functional programming paradigm, often faster than native JavaScript methods due to its Just-In-Time (JIT) compilation.
	* Cons: requires including an additional library (Ramda), may not be as well-known or supported by all browsers.
2. **JavaScript's native `Array.map`**:
	* Pros: widely supported, optimized for performance, and easy to use.
	* Cons: may not be as efficient as Ramda's implementation due to the overhead of the `map` method.
3. **Manual implementation using `for` loop**:
	* Pros: simple, well-known, and widely supported.
	* Cons: can be error-prone, less concise than other approaches.

**Library Used**

In this benchmark, Ramda is used as a library to provide its `R.map` function. Ramda is a functional programming library that provides a set of functions for working with arrays, objects, and more. Its `map` function applies a given transformation to each element of an array, returning a new array with the transformed elements.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntax used in this benchmark. The code is straightforward and uses standard JavaScript constructs.

**Alternative Approaches**

Other approaches to mapping over an array include:

1. **Lodash's `map` function**: Similar to Ramda, but part of the Lodash library.
2. **Arrow functions with `map`**: Using arrow functions (`data.map(double => double*2)`) can be a concise alternative to traditional function expressions.

In summary, this benchmark provides a simple and informative comparison of three approaches to mapping over an array: Ramda's `R.map`, JavaScript's native `Array.map`, and a manual implementation using a `for` loop.

Related benchmarks:

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

measures the speed of ramda's map vs Array's native map

Comparing performance of: Ramda vs Array (native) vs for i native

Created: 6 years ago by: Guest

Jump to the latest result

Ramda

Array (native)

for i native

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):