Benchmark: Ramda vs lodash vs native map

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>

Script Preparation code:

var array = ['a', 'b', 'c']

Tests:

Lodash
_.map(array, (value, index) => { console.log(value) })
Ramda
R.map((value, index) => { console.log(value) }, array)
Native
array.map((value, index) => { console.log(value) })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Ramda
Native

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down what's being tested in this benchmark.

**Benchmark Goal**

The goal of this benchmark is to compare the performance of three different approaches for mapping over an array:

1. Lodash (a popular JavaScript library)
2. Ramda (another popular JavaScript library)
3. Native JavaScript (the built-in `map` function)

**Comparison Options**

There are two main comparison options being tested:

**Option 1: Lodash and Ramda**

These two libraries provide similar functions for mapping over arrays, but with different implementations. The test case uses the `_map` function from Lodash and `R.map` from Ramda to perform a simple mapping operation on an array.

* **Lodash**: Lodash is a utility library that provides a wide range of functions for common tasks in JavaScript. In this case, `_map` is used to create a new array with the results of applying a given function to each element.
* **Ramda**: Ramda is another functional programming library that provides a more concise and expressive way of working with data in JavaScript. `R.map` achieves the same result as Lodash's `_map`, but with a different syntax.

**Option 2: Native JavaScript**

The third option being tested is the native `map` function in JavaScript, which is built into the language itself. This approach uses the built-in `map` method on the array to create a new array with the results of applying a given function to each element.

**Pros and Cons of Each Approach**

Here are some pros and cons of each approach:

* **Lodash and Ramda**: These libraries provide a lot of convenience and functionality, but they also introduce additional overhead due to their complexity and scope. They may be overkill for simple mapping operations.
	+ Pros: Easy to use, provides a wide range of functions and utilities.
	+ Cons: Adds unnecessary overhead, may not be necessary for simple tasks.
* **Native JavaScript**: The native `map` function is lightweight and efficient, but it requires manual function application and may be less convenient than using a library.
	+ Pros: Lightweight, efficient, easy to understand.
	+ Cons: Requires manual function application, can be less convenient.

**Other Considerations**

The benchmark also takes into account the following factors:

* **Browser**: The test is run in Chrome 74 on a Macintosh with an Intel processor and macOS 10.14.6.
* **Device Platform**: The test is run on a desktop device (not mobile or tablet).
* **Operating System**: The test is run on macOS 10.14.6.

**Alternatives**

Other alternatives for mapping over arrays include:

* Using a different JavaScript library, such as jQuery
* Using a functional programming library like Redux or MobX
* Implementing your own mapping function using recursion or iteration

In summary, this benchmark tests the performance of three approaches for mapping over an array: Lodash and Ramda (two popular libraries), and native JavaScript (the built-in `map` function). The test takes into account various factors to ensure a fair comparison.

Related benchmarks:

Ramda vs lodash vs native map (version: 0)

Comparing performance of: Lodash vs Ramda vs Native

Created: 6 years ago by: Guest

Jump to the latest result

Lodash

Ramda

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):