Benchmark: Map (Native vs Ramda vs Lodash) 5

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 = [...Array(2000)].map((v, idx) => idx);

Tests:

Array (native)
data.map(double);
Array while
var result = Array(data.length); var idx = 0; while (idx < data.length) { result[idx] = double(data[idx]); idx += 1; }
Ramda
R.map(double, data);
Lodash
_.map(data, double);
Noop
var idx = 0; while (idx < data.length) { idx += 1; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array (native)
Array while
Ramda
Lodash
Noop

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):

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I'll break down the benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

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

1. **Native Array** (using `Array.map()`)
2. **Ramda's Map** (using `R.map()` from the Ramda library)
3. **Lodash's Map** (using `_map()` from the Lodash library)

The benchmark is designed to measure the speed of each approach on a large dataset (2000 elements).

**Test Cases**

Each test case defines a separate benchmarking scenario:

1. **Array (native)**: This test case uses the native `Array.map()` function.
2. **Array while**: This test case uses a while loop to iterate over the array and assign values to an output array.
3. **Ramda**: This test case uses Ramda's `R.map()` function to map over the input data.
4. **Lodash**: This test case uses Lodash's `_map()` function to map over the input data.
5. **Noop**: This test case uses a no-op operation (doing nothing) as the mapping function.

**Library Explanation**

* **Ramda**: Ramda is a functional programming library for JavaScript that provides a set of higher-order functions for working with data. In this benchmark, `R.map()` is used to map over the input array.
* **Lodash**: Lodash is another popular utility library for JavaScript that provides a wide range of functions for working with data, strings, and more. `_map()` is a key function in Lodash's mapping utilities.

**Special JS Features**

None of these test cases use any special JavaScript features or syntax beyond what's standard.

**Pros and Cons**

* **Native Array**: The native `Array.map()` function is often the most efficient way to map over an array, as it's optimized for performance. However, it can be less readable and more error-prone than using a higher-order function like Ramda's `R.map()`.
* **Ramda's Map**: Using Ramda's `R.map()` provides a concise and expressive way to map over data, making the code easier to read and maintain. However, it may introduce additional overhead due to the library's functions being called.
* **Lodash's Map**: Lodash's `_map()` function is similar to Ramda's `R.map()`, offering a concise and readable way to map over data. However, it also introduces additional overhead from the library's functions.

**Other Alternatives**

Other alternatives for mapping over arrays in JavaScript include:

* Using `forEach()` instead of `map()`
* Implementing a custom map function using an arrow function or a traditional function
* Using other libraries like jQuery or Underscore.js

Related benchmarks:

Map (Native vs Ramda vs Lodash) 5 (version: 3)

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

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

Created: 4 years ago by: Registered User

Jump to the latest result

Array (native)

Array while

Ramda

Lodash

Noop

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):