Benchmark: Test22 - MeasureThat.net

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.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:

test1
R.map(double, data);
test2
data.map(double);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
test1
test2

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):

LLMs can make mistakes. Check important info.

I'd be happy to help explain the provided benchmark!

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark consists of two test cases, each testing a different approach to perform a simple transformation on an array of numbers.

**Test Case 1: R.map(double, data)**

This test case uses the Ramda library, which is a popular functional programming library for JavaScript. The `R.map` function applies a given function (in this case, `double`) to each element of the input array (`data`). The test case measures how many elements are mapped in one second.

**Pros and Cons:**

* Pros:
	+ Uses Ramda's `map` function, which is optimized for performance.
	+ Allows for functional programming style, making code more concise and predictable.
* Cons:
	+ Requires the use of an external library (Ramda), which may introduce overhead or conflicts with other libraries.

**Test Case 2: data.map(double)**

This test case uses the built-in `map` function of JavaScript arrays. It applies the same transformation as Test Case 1, but without using any external library.

Pros and Cons:

* Pros:
	+ Does not require an external library.
	+ Native performance since it's implemented in C++ by V8 (the engine used by Google Chrome).
* Cons:
	+ May have slower performance compared to Ramda's optimized `map` function.
	+ Less concise code, as the transformation is applied explicitly.

**Other Considerations:**

When choosing between these two approaches, consider the following factors:

* Performance: If performance is critical, using Ramda's optimized `map` function might be a better choice.
* Code Readability and Maintainability: Using built-in JavaScript functions can make code more readable, as it avoids relying on external libraries.

**Special JS Feature/Syntax:**

In this benchmark, there are no special JavaScript features or syntax being tested. The code uses standard JavaScript syntax for creating an array, defining a function, and applying the transformation using `map`.

**Other Alternatives:**

If you need to perform complex data transformations, you might consider other libraries like:

* Lodash: A popular utility library that provides various functions for working with arrays and objects.
* Array.prototype.reduce(): Another native JavaScript method that can be used for array transformations.

Keep in mind that performance will depend on the specific use case and requirements.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36

Browser/OS: Chrome 72 on Linux

View result in a separate tab

Test name	Executions per second
test1	2417043.8 Ops/sec
test2	1750256.5 Ops/sec

Related benchmarks:

Test22 (version: 0)

Comparing performance of: test1 vs test2

Created: 5 years ago by: Guest

Jump to the latest result

test1

test2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):