Benchmark: Map (Native vs Ramda vs Lodash)

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 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);
Lodash
_.map(data, double);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Array (native)
Lodash

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its components.

**Benchmark Overview**

The benchmark measures the performance of three map functions:

1. Native Array.map
2. Ramda's R.map
3. Lodash's _.map

These functions are used to apply a transformation (in this case, doubling numbers) to an array.

**Options Compared**

The three options being compared are:

* **Native Array.map**: This is the built-in map function provided by JavaScript arrays.
* **Ramda's R.map**: Ramda is a functional programming library that provides a higher-order mapping function. R.map applies a given function to each element of an array.
* **Lodash's _.map**: Lodash is another popular utility library for JavaScript, providing a map function as part of its _. collection.

**Pros and Cons**

Here are some general pros and cons of each approach:

* **Native Array.map**:
	+ Pros: Built-in, no additional dependencies, efficient.
	+ Cons: May have less readable code due to the lack of abstraction.
* **Ramda's R.map**:
	+ Pros: Higher-order mapping function provides more flexibility and expressiveness, abstracts away the iteration logic.
	+ Cons: Requires an external library dependency (Ramda), may be slower due to additional overhead.
* **Lodash's _.map**:
	+ Pros: Similar to Ramda's approach, but part of a larger utility library with other useful functions. May be faster than Ramda due to better optimization.
	+ Cons: Requires an additional library dependency, may have more overhead compared to native Array.map.

**Library Usage**

* **Ramda**: A functional programming library that provides various higher-order mapping and filtering functions.
* **Lodash**: A utility library providing a wide range of functions for common tasks, including array manipulation (like map).

**Special JS Features/Syntax**

None mentioned in this benchmark. However, the use of Ramda's R.map and Lodash's _.map highlights the importance of higher-order functions and abstraction in JavaScript code.

**Alternative Options**

Other alternatives to consider when working with arrays and transformations might include:

* Vanilla JavaScript: Using simple loops or recursion for mapping and filtering.
* Other functional programming libraries (e.g., Underscore.js, John Resig's jsZip).
* Libraries specifically designed for performance optimization (e.g., Fastify).

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

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36

Browser/OS: Chrome 84 on Mac OS X 10.15.5

View result in a separate tab

Test name	Executions per second
Ramda	1621043.9 Ops/sec
Array (native)	1156654.6 Ops/sec
Lodash	2043046.4 Ops/sec

Related benchmarks:

Map (Native vs Ramda vs Lodash) (version: 0)

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

Comparing performance of: Ramda vs Array (native) vs Lodash

Created: 6 years ago by: Guest

Jump to the latest result

Ramda

Array (native)

Lodash

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):