Benchmark: ramda vs lodash/fp vs native

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-fp/0.10.4/lodash-fp.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);
Lodash/fp
_.map(data, double);
Array
data.map(double);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Lodash/fp
Array

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 dive into the provided benchmarking test.

**Benchmark Overview**

The benchmark measures the performance of three different approaches to map over an array:

1. Ramda (a functional programming library)
2. Lodash/fp (a functional programming version of Lodash with functional programming utilities)
3. Native JavaScript (using the `map()` method directly on the array)

**Test Cases**

There are three test cases:

* **Ramda**: Maps over the data array using `R.map(double, data)`.
	+ **Library**: Ramda is a pure functional programming library for JavaScript.
	+ **Purpose**: Provides higher-order functions and utilities for working with arrays and other data structures in a functional style.
	+ **Pros**: Can be more concise and expressive than native JavaScript code, especially for complex operations. Can provide better type safety and predictability.
	+ **Cons**: May introduce additional overhead due to the abstraction layer, and can be less efficient than native code for very simple operations.
* **Lodash/fp**: Maps over the data array using `_.map(data, double)`.
	+ **Library**: Lodash is a utility library that provides a wide range of functions for working with arrays, objects, and other data structures. The `/fp` suffix indicates it's a functional programming version of Lodash.
	+ **Purpose**: Provides a set of higher-order functions and utilities for working with arrays and other data structures in a functional style.
	+ **Pros**: Similar to Ramda, can be more concise and expressive than native JavaScript code, and provides better type safety and predictability. Also provides additional utility functions that may not be part of the standard library.
	+ **Cons**: May introduce additional overhead due to the abstraction layer, and can be less efficient than native code for very simple operations.
* **Native JavaScript**: Maps over the data array using `data.map(double)`.
	+ **Pros**: No overhead from an abstraction layer, as it's simply calling a native function on an array. Typically the fastest option for simple operations.
	+ **Cons**: Can be less concise and expressive than Ramda or Lodash/fp code, especially for complex operations.

**Benchmark Considerations**

* The benchmark is running in Chrome 80 on a desktop platform (Mac OS X 10.15.3).
* The `double` function is simply multiplying its input by 2.
* The data array contains 30 elements, all starting from 1 and incrementing by 1.

**Alternative Approaches**

Other approaches to map over an array might include:

* Using a loop or a for-each loop to iterate over the array and apply the operation.
* Using `reduce()` instead of `map()`, although this would likely be less efficient in most cases.
* Using a different library or framework that provides its own implementation of `map()` (e.g. React Hooks).

In general, when working with arrays, native JavaScript code is usually the fastest option for simple operations like mapping over an array. However, for more complex operations or when you need to write concise and expressive code, a functional programming library like Ramda or Lodash/fp can be a good choice.

Related benchmarks:

ramda vs lodash/fp vs native (version: 0)

Comparing performance of: Ramda vs Lodash/fp vs Array

Created: 6 years ago by: Guest

Jump to the latest result

Ramda

Lodash/fp

Array

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):