Benchmark: Array.sort vs Math.min&Math.max (random)

Script Preparation code:

var v1 = Math.random() * 100000;
var v2 = Math.random() * 100000;
var v3 = Math.random() * 100000;
var v4 = Math.random() * 100000;
var v5 = Math.random() * 100000;
var v6 = Math.random() * 100000;

var arr = [v1, v2, v3, v4, v6, v6]

Tests:

Array.sort
var a = arr.sort(); var min = a[0]; var max = a[1];
Math min and max
var min = Math.min(v2, v1); var max = Math.max(v2, v1);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.sort
Math min and max

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 11_1_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.60 YaBrowser/20.12.0.1065 Yowser/2.5 Safari/537.36

Browser/OS: Yandex Browser 20 on Mac OS X 11.1.0

View result in a separate tab

Test name	Executions per second
Array.sort	2025203.8 Ops/sec
Math min and max	1479235.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmarking test cases.

**Benchmark Definition JSON**

The `Script Preparation Code` section defines six random numbers, which will be used as elements of an array. The `Html Preparation Code` is empty, indicating that no HTML code is necessary for this benchmark.

**Test Cases**

There are two test cases:

1. **Array.sort**
	* Benchmark Definition: This line sorts the array in ascending order and assigns it to a variable named "a".
	* Purpose: To measure the performance of the `sort()` method on an array.
2. **Math min and max**
	* Benchmark Definition: This code finds the minimum and maximum values between two random numbers (`v1` and `v2`) using the `Math.min()` and `Math.max()` functions, respectively.
	* Purpose: To measure the performance of using `Math.min()` and `Math.max()` to find minimum and maximum values.

**Options Compared**

The test cases are comparing the performance of two approaches:

1. Using the `sort()` method on an array (Array.sort).
2. Using `Math.min()` and `Math.max()` functions to find minimum and maximum values.

**Pros and Cons**

* **Array.sort**: Pros:
	+ Simple and straightforward approach.
	+ Can be used when sorting an entire array is necessary.
* Cons:
	+ May have a higher overhead due to the sorting algorithm, which can affect performance for large datasets.
	+ May not be suitable for finding minimum or maximum values in a dataset.
* **Math min and max**: Pros:
	+ Typically faster than using `sort()` because it only finds the two extreme values, rather than sorting an entire array.
	+ Can be used when only finding minimum or maximum values is necessary.
* Cons:
	+ May not work correctly if there are duplicate values in the dataset (since only one minimum and maximum value can be returned).
	+ May not handle edge cases correctly (e.g., what happens if all values are equal).

**Library Usage**

In this benchmark, no external libraries are used.

**Special JS Feature/Syntax**

No special JavaScript features or syntax are used in these test cases. They only rely on basic JavaScript operations and functions like `Math.min()`, `Math.max()`, and the `sort()` method.

**Alternatives**

Other alternatives to compare with Array.sort and Math min/max could be:

1. Using a sorting algorithm from scratch (e.g., bubble sort, quicksort).
2. Using a different function to find minimum or maximum values, such as `Array.prototype.reduce()`.
3. Measuring the performance of sorting an array using other methods, like radix sort or counting sort.

Keep in mind that these alternatives would require significant changes to the benchmark code and may not be directly comparable to the original test cases.

Related benchmarks:

Array.sort vs Math.min&Math.max (random) (version: 0)

Comparing performance of: Array.sort vs Math min and max

Created: 3 years ago by: Guest

Jump to the latest result

Array.sort

Math min and max

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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