Benchmark: Lodash clamp VS Math.min, Math.max (Conditional)

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.7.11/lodash.min.js'></script>

Script Preparation code:

const clamp = (number, boundOne, boundTwo) => {
    if (!boundTwo) {
        return Math.max(number, boundOne) === boundOne ? number : boundOne;
    } else if (Math.min(number, boundOne) === number) {
        return boundOne;
    } else if (Math.max(number, boundTwo) === number) {
        return boundTwo;
    }
    return number;
};

Tests:

Lodash clamp
_.clamp(150, 10, 100);
Manual Math.min Math.max
Math.min(Math.max(150, 10), 100);
Conditional Math clamp
((number = 150, boundOne = 10, boundTwo = 100) => { if (!boundTwo) { return Math.max(number, boundOne) === boundOne ? number : boundOne; } else if (Math.min(number, boundOne) === number) { return boundOne; } else if (Math.max(number, boundTwo) === number) { return boundTwo; } return number; })();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash clamp
Manual Math.min Math.max
Conditional Math clamp

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Mobile Safari/537.36

Browser/OS: Chrome Mobile 135 on Android

View result in a separate tab

Test name	Executions per second
Lodash clamp	16092474.0 Ops/sec
Manual Math.min Math.max	30114890.0 Ops/sec
Conditional Math clamp	29840674.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into explaining the benchmark.

**Overview**

The provided JSON represents a JavaScript microbenchmarking test case for the `clamp` function, which is used to limit a value within a specified range. The test compares three different approaches:

1. Using the Lodash library (`_clamp`)
2. Manual implementation of `Math.min` and `Math.max`
3. Conditional implementation of `Math.clamp`

**Lodash Library**

The Lodash library is a popular JavaScript utility library that provides a wide range of functions for tasks like array manipulation, string processing, and more. In this case, the `_clamp` function is used to limit a value within a specified range.

**Manual Implementation**

The manual implementation of `Math.min` and `Math.max` involves using these built-in functions to find the minimum or maximum value between two numbers. This approach requires explicit function calls and comparisons.

**Conditional Implementation**

The conditional implementation of `Math.clamp` is similar to the Lodash `_clamp` function but uses a more concise syntax. It checks for different conditions to determine which bound to return.

**Pros and Cons**

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

* **Lodash `_clamp`**: Pros:
	+ Shorter code
	+ Easier to read and maintain
	+ Reduces boilerplate code
* Cons:
	+ Adds external library dependency
	+ May incur overhead due to function call and object lookup
* **Manual Implementation**:
	+ Pros:
		- No external dependencies
		- Can be optimized for specific use cases
		- Understood by all developers familiar with `Math.min` and `Math.max`
	+ Cons:
		- Longer code
		- More prone to errors due to increased complexity
		- May require more manual optimization efforts
* **Conditional Implementation**:
	+ Pros:
		- Similar syntax to Lodash `_clamp`
		- Easy to understand for developers familiar with conditional statements
	+ Cons:
		- May not be as concise as Lodash `_clamp`
		- Limited readability for developers unfamiliar with the syntax

**Special JS Feature**

The provided benchmark uses a feature called "arrow functions" (introduced in ECMAScript 2015). An arrow function is a shorthand way of defining small functions. In this case, the conditional implementation uses an arrow function to define the `clamp` function.

**Other Alternatives**

Some other alternatives for implementing `clamp` functions include:

* Using the `Math.floor` and `Math.ceil` functions to achieve similar results
* Implementing a custom binary search algorithm to find the closest bound
* Utilizing GPU acceleration using WebGPU or WebGL

It's essential to note that the choice of implementation depends on specific requirements, such as performance, code readability, and maintainability.

Related benchmarks:

Lodash clamp VS Math.min, Math.max (Conditional) (version: 0)

Comparing performance of: Lodash clamp vs Manual Math.min Math.max vs Conditional Math clamp

Created: 3 years ago by: Guest

Jump to the latest result

Lodash clamp

Manual Math.min Math.max

Conditional Math clamp

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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