Benchmark: Javascript Array sorting performance with sort() and reduce() take2

HTML Preparation code:

<div id='min-data'></div>

Tests:

with reduce()
const div = document.getElementById('min-data'); const newArray = []; for(let i = 0; i < 10000000; i++){ const num = Math.floor(Math.random() * 10000000); newArray.push(num); } newArray.reduce((a, b) => (a < b ? a : b), []); div.innerHTML = newArray[0].toString();
with sort()
const div = document.getElementById('min-data'); const newArray = []; for(let i = 0; i < 10000000; i++){ const num = Math.floor(Math.random() * 10000000); newArray.push(num); } newArray.sort((a, b) => a - b); div.innerHTML = newArray[0].toString();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
with reduce()
with sort()

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided JSON data to understand what's being tested.

**Benchmark Definition**

The benchmark definition provides information about the test, including its name and description. In this case, we have two tests:

1. "Javascript Array sorting performance with sort() and reduce()"
2. The first test case is "with sort()", and the second test case is "with reduce()".

**Script Preparation Code**

There is no script preparation code provided for either test case. This means that the benchmark creator did not specify any custom JavaScript code to be executed before running the actual tests.

**Html Preparation Code**

The html preparation code is a simple HTML snippet that creates an empty `<div>` element with the id "min-data". This HTML element will be used to display the results of each test case.

**Individual Test Cases**

There are two individual test cases:

1. **with reduce()**
This test case creates an array `newArray` with 10 million random numbers and then uses the `reduce()` method to sort the array in ascending order. The sorted array is then displayed on the screen using the HTML element created earlier.
2. **with sort()**
This test case creates an array `newArray` with 10 million random numbers and then uses the `sort()` method to sort the array in ascending order. The sorted array is then displayed on the screen using the HTML element created earlier.

**Library Used**

In both test cases, no specific libraries are used beyond the built-in JavaScript methods (`reduce()`, `sort()`, `Math.random()`).

**Special JS Features/Syntax**

No special JS features or syntax are being tested in these benchmarks.

**Pros and Cons of Different Approaches**

1. **sort() method**: The `sort()` method is a built-in method in JavaScript that sorts the elements of an array in place. It has a time complexity of O(n log n) for large arrays. Pros: widely supported, efficient for large datasets. Cons:
	* Can be slow for very large arrays due to its O(n log n) complexity.
	* May not work as expected if the array contains non-numeric values (e.g., strings).
2. **reduce() method**: The `reduce()` method is a built-in method in JavaScript that applies a function to each element of an array and reduces it to a single output value. In this case, it's used to sort the array in ascending order. Pros: flexible, can be used for other operations beyond sorting.
	* Can be slower than the `sort()` method for large arrays due to its O(n) complexity.

**Other Considerations**

1. **Array size**: The benchmark tests an array with 10 million random numbers. This is a relatively small dataset compared to some of the largest datasets in real-world applications.
2. **Browser and Device Platform**: The benchmark was run on a Windows desktop with Firefox 115. Other browsers, devices, or operating systems may produce different results due to variations in JavaScript implementation.

**Alternative Approaches**

1. **Other sorting algorithms**: Instead of using built-in methods like `sort()` or `reduce()`, alternative sorting algorithms like merge sort, quick sort, or heap sort could be used.
2. **Parallel processing**: If the benchmark is run on a multi-core processor, parallel processing techniques (e.g., using Web Workers) could be employed to speed up the sorting process.
3. **Optimized data structures**: Using optimized data structures like a balanced binary search tree or a skip list could potentially improve performance for large datasets.

In summary, this benchmark tests the performance of two common JavaScript methods for sorting arrays: `sort()` and `reduce()`. The results can provide insight into how these methods perform on real-world datasets, but it's essential to consider factors like array size, browser and device platform, and potential optimizations.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/115.0

Browser/OS: Firefox 115 on Windows

View result in a separate tab

Test name	Executions per second
with reduce()	0.5 Ops/sec
with sort()	0.5 Ops/sec

Related benchmarks:

Javascript Array sorting performance with sort() and reduce() take2 (version: 0)

Javascript Array sorting performance with sort() and reduce()

Comparing performance of: with reduce() vs with sort()

Created: one year ago by: Guest

Jump to the latest result

with reduce()

with sort()

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):