Benchmark: For vs map vs R.map 2

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:

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

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for
map
R.map

Fastest: N/A

Slowest: N/A

Latest 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 of the provided benchmark.

**Benchmark Overview**

The benchmark, named "For vs map vs R.map 2", compares three approaches to iterate over an array and perform a calculation on each element: a traditional `for` loop, the `map()` method, and a Ramda function call (`R.map()`).

**Approaches Compared**

1. **Traditional For Loop**: This approach uses a classic `for` loop with an incrementing index variable `i` to iterate over the array.
2. **Array Method (map())**: The `map()` method is called on the array, passing a function reference as its first argument. This function applies the desired transformation to each element in the array and returns a new array with the results.
3. **Ramda Function Call (R.map())**: This approach uses the Ramda library's `map()` function, which is similar to the built-in JavaScript `map()` method but provides additional features and functionality.

**Pros and Cons of Each Approach**

1. **Traditional For Loop**:
	* Pros: Simple, efficient, and widely supported.
	* Cons: Can be less readable and more prone to errors due to its index-based nature.
2. **Array Method (map())**:
	* Pros: Concise, easy to read, and widely supported.
	* Cons: May incur additional overhead due to the creation of a new array with the results.
3. **Ramda Function Call (R.map())**:
	* Pros: Provides additional features, such as handling errors and memoization, making it more flexible and powerful than the built-in JavaScript `map()` method.
	* Cons: May require an additional dependency (the Ramda library) and can be less readable due to its functional programming style.

**Other Considerations**

* **Memory Allocation**: The traditional for loop and array method may allocate memory for a new array with the results, while the Ramda function call avoids this overhead.
* **Readability**: The array method is generally more concise and easier to read than the traditional for loop or the Ramda function call.

**Special JS Feature or Syntax**

The benchmark uses the `map()` method, which is a built-in JavaScript feature. No special JavaScript features or syntax are used in this benchmark.

**Libraries Used**

* **Ramda**: A functional programming library that provides additional features and functionality beyond the built-in JavaScript `map()` method.
* **jQuery**: An optional library included in the benchmark preparation code, but not used in the actual benchmark.

**Alternatives**

If you're looking for alternative approaches to this benchmark, consider:

1. Using a `for...of` loop instead of an index-based `for` loop.
2. Utilizing other array methods, such as `forEach()` or `reduce()`.
3. Exploring other libraries, like Lodash or Underscore.js, which provide additional functional programming utilities.

Keep in mind that the choice of approach depends on your specific use case and requirements.

Related benchmarks:

For vs map vs R.map 2 (version: 1)

Comparing performance of: for vs map vs R.map

Created: 6 years ago by: Registered User

Jump to the latest result

for

map

R.map

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):