Benchmark: Ramda map vs Array.map anonymous function (not arrow)

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(function(n) { return n*2}, data);
Array (native)
data.map(function(n) {return n*2});

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

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

LLMs can make mistakes. Check important info.

Let's dive into the benchmark definition and explain what is being tested.

**Benchmark Definition:**

The benchmark measures the speed of two different approaches to map over an array:

1. **Ramda `R.map`**: This uses Ramda, a functional programming library for JavaScript. Specifically, it uses `R.map`, which applies a given function to each element of an array and returns a new array with the transformed elements.
2. **Array's native `map` (non-arrow)**: This is the built-in `map` method on JavaScript arrays. It takes two arguments: a callback function and an array. The callback function is executed for each element in the array, and its return value becomes the corresponding element in the new array.

**Options Compared:**

The benchmark compares the performance of Ramda's `R.map` versus the native `map` method on arrays (non-arrow). This allows users to see how a functional programming library performs compared to the built-in JavaScript method.

**Pros and Cons:**

* **Ramda's `R.map`**:
	+ Pros:
		- Provides a more concise and expressive way of mapping over arrays using a functional programming style.
		- Often leads to more readable and maintainable code.
	+ Cons:
		- May have overhead due to the use of a library, which might impact performance.
* **Array's native `map` (non-arrow)**:
	+ Pros:
		- Built-in method with minimal overhead.
		- Native performance optimizations.
	+ Cons:
		- Can be less readable and maintainable compared to functional programming styles.

**Library:**

The library being used here is Ramda, which provides a wide range of utility functions for functional programming. In this case, it's used for its `map` function, which applies a given function to each element of an array.

**Special JS Feature or Syntax:**

There are no special JavaScript features or syntaxes being used in this benchmark. It only involves standard JavaScript methods and libraries (Ramda).

**Other Considerations:**

Other alternatives to consider when mapping over arrays include:

* **Arrow functions**: Using arrow functions instead of traditional function expressions can improve readability but may have similar performance characteristics to the native `map` method.
* **Generator functions**: Generator functions can be used for mapping over arrays, especially for large datasets. However, their use might not be as straightforward as using the native `map` method or Ramda's `R.map`.
* **Other libraries**: Other libraries like Lodash or Underscore.js provide similar functionality to Ramda and can be used for mapping over arrays.

Keep in mind that the performance differences between these approaches may vary depending on specific use cases, array sizes, and JavaScript environments.

Related benchmarks:

Ramda map vs Array.map anonymous function (not arrow) (version: 0)

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

Comparing performance of: Ramda vs Array (native)

Created: 4 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):