Benchmark: .sort vs manual (in the context of making a res. array)

Tests:

loop
const arr = [10, 2, 5, 110, 1, 9, 11]; let res = [] for (let i = 0; i < arr.length; i++) { for (let j = 0; j < arr.length; j++) { if (arr[i] <= res[j]) { res.splice(j,0,arr[i]); break; } } if (arr[i] > res[res.length-1] || res.length == 0) { res.push(arr[i]); } } console.log(res)
.sort
const arr = [10, 2, 5, 110, 1, 9, 11]; const sortNumbers = (arr) => { console.log([...arr].sort((a, b) => a - b)); };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
loop
.sort

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

**What is being tested?**

On the provided JSON, two individual test cases are defined to compare the performance of sorting an array using JavaScript's built-in `sort()` method versus a manual implementation.

**Options compared:**

1. **Manual Implementation**: A custom loop-based approach that iterates through the input array and manually updates the result array by inserting elements at the correct position.
2. **Built-in sort() method**: The standard JavaScript function used to sort arrays in ascending order.

**Pros and Cons of each approach:**

**Manual Implementation:**

Pros:

* Can be more efficient for small to medium-sized datasets due to its simplicity and lack of overhead.
* Allows for fine-grained control over the sorting process, potentially leading to better results for specific use cases.

Cons:

* Has a higher constant factor due to the need to iterate through the entire array in each iteration, making it slower than the built-in `sort()` method for large datasets.
* May be more prone to errors and edge cases due to its custom implementation.

**Built-in sort() method:**

Pros:

* Optimized for performance, with algorithms and data structures that minimize the number of comparisons needed.
* Supports various sorting modes (e.g., ascending/descending, stable/unstable) and can handle complex datasets efficiently.

Cons:

* May have a higher overhead due to its complexity and use of additional data structures.
* Less control over the sorting process compared to the manual implementation.

**Library usage:**

In both test cases, no external libraries are explicitly mentioned. However, it's worth noting that the `sort()` method is implemented in native code by JavaScript engines, which can provide a significant performance boost compared to interpreted or dynamically generated implementations.

**Special JS features/syntax:**

There are no special JavaScript features or syntax used in these test cases beyond standard ECMAScript 2020 (ES2020) syntax. The use of template literals (`\r\n` escapes and template literal formatting) is also present, but it's not a feature that affects the benchmarking results.

**Other alternatives:**

If you were to modify or extend this benchmark, some alternative approaches could include:

* Using other sorting algorithms (e.g., merge sort, quicksort) for comparison.
* Incorporating additional factors, such as data distribution or clustering patterns.
* Experimenting with different hardware configurations or parallelization techniques.
* Including additional error handling or edge case scenarios.

Keep in mind that the goal of this benchmark is to compare the performance of two specific approaches within a standard JavaScript context.

Latest run results:

Run details: (Test run date: 4 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
loop	378140.2 Ops/sec
.sort	216208300.0 Ops/sec

Related benchmarks:

.sort vs manual (in the context of making a res. array) (version: 0)

Comparing performance of: loop vs .sort

Created: 4 days ago by: Guest

Jump to the latest result

loop

.sort

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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