Benchmark: Map (Native vs Ramda vs Lodash) vs for

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(20)].map((v, idx) => idx);

Tests:

Ramda
R.map(double, data);
Array (native)
data.map(double);
Lodash
_.map(data, double);
For
for (var i =0; i < data.length; i++){ double(data[i]) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Array (native)
Lodash
For

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 explanation.

**Benchmark Definition JSON**

The provided JSON represents a benchmark test case for measuring the speed of different approaches to map over an array:

* `Map (Native vs Ramda vs Lodash) vs for`: This is the overall test name, describing what's being measured.
* `Script Preparation Code`: This is the code that sets up the necessary environment for the test. In this case, it defines a simple function `double(n)` and creates an array `data` with 20 elements, each indexed by its position in the array.

**Individual Test Cases**

The test cases are defined as an array of objects, each representing a specific approach to map over the `data` array:

* **Ramda**: The `R.map(double, data);` line uses Ramda's `map` function, which applies the `double` function to each element in the array.
* **Array (native)**: The `data.map(double);` line uses the native `map` method of the JavaScript Array prototype, applying the `double` function to each element in the array.
* **Lodash**: The `_.map(data, double);` line uses Lodash's `map` function, which is similar to Ramda's but with some additional features. In this case, it also applies the `double` function to each element in the array.
* **For**: The `for (var i =0; i < data.length; i++){\r\n  double(data[i])\r\n}` line uses a traditional `for` loop to iterate over the array and apply the `double` function to each element.

**Options Compared**

The test compares the performance of four different approaches:

1. Ramda's `map` function
2. The native JavaScript Array prototype's `map` method
3. Lodash's `map` function
4. A traditional `for` loop

**Pros and Cons**

Here are some pros and cons for each approach:

* **Ramda's map**: Pros: concise and expressive syntax, easy to learn and use. Cons: may be slower than native JavaScript methods due to the overhead of a library.
* **Native Array map**: Pros: fast and efficient, optimized by the browser engine. Cons: requires more boilerplate code and may not be as convenient to read or write.
* **Lodash's map**: Pros: similar to Ramda's, but with additional features like handling null/undefined values. Cons: may add unnecessary overhead compared to native JavaScript methods.
* **For loop**: Pros: simple and easy to understand for beginners, no dependencies on libraries. Cons: slower and less concise than other approaches.

**Library Use**

The test uses three external libraries:

1. Ramda (for its `map` function)
2. Lodash (for its `map` function)

Both libraries are popular choices for functional programming in JavaScript, but they have different designs and philosophies. Ramda is known for its concise and expressive syntax, while Lodash has a more feature-rich API.

**Special JS Feature or Syntax**

There doesn't appear to be any special JavaScript features or syntax used in this test beyond the standard `map` method and `for` loop.

Related benchmarks:

Map (Native vs Ramda vs Lodash) vs for (version: 0)

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

Comparing performance of: Ramda vs Array (native) vs Lodash vs For

Created: 4 years ago by: Guest

Jump to the latest result

Ramda

Array (native)

Lodash

For

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):