Benchmark: Ramda map vs native map vs for

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1/jquery.min.js"></script>

Script Preparation code:

 // before all
var genericText = "Generic prepended text, calculated value is: ";
var arr = new Array();
for (var i = 0; i < 100000; i++) {
  arr[i] = i;
}
var $arr = $(arr);
var newArr = [];

function multReturn(x) {
  return genericText + Math.sqrt(x * x);
}

Tests:

R.map
newArr = R.map(multReturn, arr);
map
newArr = arr.map(multReturn);
for
for (var i = 0; i < arr.length; i++) { newArr[i] = multReturn(arr[i]); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
R.map
map
for

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

**Benchmark Definition**

The benchmark is designed to measure the performance of three different approaches:

1. `R.map` (using Ramda library): This approach uses the Ramda library's `map` function to apply the `multReturn` function to each element in the array.
2. `map`: This is a built-in JavaScript method that applies a given function to each element in an array.
3. `for`: This is a traditional loop-based approach where a `for` loop iterates over the elements of the array and assigns the result of `multReturn(arr[i])` to each index.

**Library: Ramda**

The Ramda library is a functional programming utility library for JavaScript. The `map` function in Ramda takes two arguments: the input array and a transformation function (in this case, `multReturn`). It returns a new array with the transformed elements.

In the benchmark code, `$arr` represents the jQuery-wrapped version of the array `arr`. This is likely used to simulate how arrays are typically used in web applications.

**Special JS Feature/Syntax: None**

There's no special JavaScript feature or syntax being tested in this benchmark. All three approaches use standard JavaScript constructs.

**Options Compared**

The benchmark compares the performance of:

1. `R.map`: Using Ramda's `map` function.
2. `map`: The built-in JavaScript method for applying a function to each element in an array.
3. `for`: A traditional loop-based approach using a `for` loop.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **R.map (Ramda)**:
	* Pros: More concise, readable code; allows for functional programming style; often faster than built-in methods.
	* Cons: Requires an additional library dependency; may have overhead due to library initialization.
2. **map (built-in JavaScript method)**:
	* Pros: No additional library dependency required; easy to understand and use.
	* Cons: May be less concise or readable; can be slower than Ramda's `map` function.
3. **for**:
	* Pros: No additional dependencies required; often the most straightforward approach.
	* Cons: Can be verbose and harder to read; may have performance overhead due to loop iteration.

**Other Alternatives**

If you're looking for alternative approaches, consider:

1. `Array.prototype.reduce()`: Another built-in JavaScript method that can be used to apply a function to each element in an array. However, it's less concise than `map` and `R.map`.
2. Lodash: A popular utility library for JavaScript that provides an `each` function similar to Ramda's `map`.
3. Underscore.js: Another functional programming library that provides a `map` function similar to Ramda.

Keep in mind that the performance differences between these approaches may not be significant in all scenarios, and other factors like code readability, maintainability, and conciseness should also be considered when choosing an approach.

Related benchmarks:

Ramda map vs native map vs for (version: 0)

Comparing performance of: R.map vs map vs for

Created: 6 years ago by: Guest

Jump to the latest result

R.map

map

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):