Benchmark: Map (Native vs Ramda vs Lodash FP vs Immutable)

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>
<script src="https://cdn.jsdelivr.net/g/lodash@4(lodash.min.js+lodash.fp.min.js)"></script>
<script type='text/javascript' src='https://cdnjs.cloudflare.com/ajax/libs/immutable/3.8.2/immutable.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.map
R.map(double, data);
Array.prototype.map
data.map(double);
lodash.map
_.map(data, double);
Immutable.map
Immutable.fromJS(data).map(double)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda.map
Array.prototype.map
lodash.map
Immutable.map

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is designed to compare the performance of different approaches for applying a transformation function to an array:

1. **Native Array**: Using the native `map()` method on the JavaScript Array prototype.
2. **Ramda`: Using the `R.map()` function from the Ramda library, which provides functional programming utilities.
3. **Lodash FP**: Using the `_.map()` function with the `fp` namespace from Lodash, which provides functional programming utilities.
4. **Immutable**: Using the `Immutable.map()` function from the Immutable.js library, which is designed for working with immutable data structures.

**Options Compared**

The benchmark compares the performance of each approach on a specific input:

* The input array `data` contains 30 elements and is used to apply the transformation function (`double(n)`) to each element.
* The input array is created using JavaScript's built-in `Array` constructor.

**Pros and Cons of Each Approach**

1. **Native Array (map())**:
	* Pros: Wide support, simple to use, and optimized for performance.
	* Cons: Can be less efficient than other approaches, especially for large arrays, due to the creation of intermediate arrays.
2. **Ramda (R.map())**:
	* Pros: Provides functional programming utilities, can be more concise, and is designed for performance.
	* Cons: Requires importing an external library, may have a higher overhead compared to native Array methods.
3. **Lodash FP (_.map())**:
	* Pros: Provides functional programming utilities, similar to Ramda, but with additional features.
	* Cons: Requires importing two separate libraries (Lodash and FP namespace), which can add overhead.
4. **Immutable (Immutable.map())**:
	* Pros: Designed for working with immutable data structures, provides predictable behavior, and can be more efficient than native Array methods.
	* Cons: Requires importing an external library, may have a higher overhead compared to native Array methods.

**Library Descriptions**

1. **Ramda**: A functional programming library that provides a wide range of utilities for working with arrays, objects, and functions.
2. **Lodash FP**: A functional programming namespace within the Lodash library, providing additional utilities for working with arrays, objects, and functions.
3. **Immutable.js**: A library designed for working with immutable data structures, providing a predictable and efficient way to work with data.

**Special JS Features or Syntax**

None mentioned in this benchmark. The transformation function `double(n)` is a simple JavaScript function that takes an argument `n` and returns its double value.

**Other Alternatives**

If you're looking for alternative approaches to the native Array methods, consider:

1. **Arrow functions**: Can provide a concise way to define small functions.
2. **Promises**: Can be used to create asynchronous transformations, but may not provide the same level of performance as native Array methods.
3. **Generator functions**: Can be used to create generators for working with arrays, providing an alternative to traditional array methods.

Keep in mind that this benchmark is designed to compare specific approaches (native Array, Ramda, Lodash FP, and Immutable), but there are many other alternatives available depending on your specific use case and requirements.

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36

Browser/OS: Chrome 76 on Windows

View result in a separate tab

Test name	Executions per second
Ramda.map	1117873.5 Ops/sec
Array.prototype.map	785923.9 Ops/sec
lodash.map	155951.3 Ops/sec
Immutable.map	132685.2 Ops/sec

Related benchmarks:

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

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

Comparing performance of: Ramda.map vs Array.prototype.map vs lodash.map vs Immutable.map

Created: 5 years ago by: Guest

Jump to the latest result

Ramda.map

Array.prototype.map

lodash.map

Immutable.map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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