Benchmark: Map (Native vs Ramda)

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.27.0/ramda.min.js"></script>

Script Preparation code:

function double(n) {
	return n*2;
}
var data = [...Array(20)].map((v, idx) => idx);

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Array (native)

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases to understand what's being tested.

**Benchmark Definition**

The website uses JSON data to define benchmarks, which includes:

1. **Script Preparation Code**: This is the code that sets up the test environment. In this case, it defines a `double` function and generates an array of numbers from 0 to 19 using the native `Array.map()` method.
2. **Html Preparation Code**: This is the HTML code that includes references to external libraries or frameworks used in the benchmark. In this case, it links to Ramda's JavaScript library.

**Test Cases**

The website runs multiple test cases for each benchmark definition. The current test cases are:

1. **Ramda**: Tests the `R.map()` function from Ramda against the native `Array.map()` method.
2. **Array (native)**: Tests the native `Array.map()` method without using any external libraries.

**What's being tested?**

The test case is measuring the performance difference between using an external library (Ramda) versus the native JavaScript implementation (`Array.map()`) for the `map()` function on a simple array of numbers.

**Options compared**

Two options are being compared:

1. **Native Array.map()**: The built-in JavaScript method that applies a function to each element in an array.
2. **Ramda R.map()**: A version of the `map()` function from the Ramda library, which is designed for functional programming and provides additional features.

**Pros and Cons**

Here's a brief summary:

* **Native Array.map():**
	+ Pros:
		- Built-in performance and compatibility
		- Simple and lightweight
	+ Cons:
		- May not provide the same level of functionality as external libraries like Ramda
		- Can be slower due to overhead or specific browser implementations
* **Ramda R.map():**
	+ Pros:
		- Provides additional features and flexibility for functional programming
		- Often optimized for performance
	+ Cons:
		- External dependency (additional bundle size, potential security concerns)
		- May introduce extra complexity

**Other considerations**

* **DevicePlatform**: The benchmark is executed on a mobile device with Chrome Mobile 87. This might impact the results due to differences in browser behavior or hardware constraints.
* **OperatingSystem**: The test runs on Android. While this might not be relevant, it's worth noting that performance can vary across different OS versions.

**Other alternatives**

For similar benchmarks, other libraries or methods could be used:

1. **Lodash map()**: Another popular utility library with a `map()` function.
2. **Lo-Dash map()**: A variation of Lodash with some additional features and optimizations.
3. **ES6 Array.prototype.map()**: If you're targeting modern browsers, using the ES6 standard's `Array.prototype.map()` method might be more suitable.

Keep in mind that these alternatives would change the scope of the benchmark or add new variables to consider.

That concludes our analysis!

Related benchmarks:

Map (Native vs Ramda) (version: 0)

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

Comparing performance of: Ramda vs Array (native)

Created: 5 years ago by: Guest

Jump to the latest result

Ramda

Array (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):