Benchmark: Insertion vs Built-in sort

Script Preparation code:

var input = [];
arrayLenght = 1000
for (var i = 0; i < 1000; i++) {
  input[i] = Math.round(Math.random() * 1000000);
}

Tests:

Built in
var arr1 = [] for (var i = 0; i < arrayLenght; i++) { arr1.push(input[i]) arr1.sort((a, b) => a - b) }
Insertion Sort
var arr2 = [] for (var i = 0; i < arrayLenght; i++) { var addedElement = false for (var j = 0; j < arr2.length; j++) { if (arr2[j] >= input[i]) { arr2.splice(j, 0, input[i]); addedElement = true; break; } } if (!addedElement) { arr2.push(input[i]) } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Built in
Insertion Sort

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in this benchmark.

**Benchmark Description**

The benchmark is testing the performance difference between two sorting algorithms:

1. **Built-in sort**: The built-in sort function, which uses a hybrid sorting algorithm (usually Timsort) that's implemented by the JavaScript engine.
2. **Insertion Sort**: A simple, iterative sorting algorithm that works well for small datasets.

**Options Compared**

The benchmark is comparing two approaches:

* `Built-in sort`: Using the built-in sort function provided by the JavaScript engine.
* `Insertion Sort`: Implementing a custom insertion sort algorithm.

**Pros and Cons of Each Approach**

1. **Built-in sort**:
	* Pros:
		+ Highly optimized for performance.
		+ Can take advantage of multi-threading and caching.
		+ Relatively simple to implement.
	* Cons:
		+ May have complex internal implementation details that are difficult to understand.
		+ May be less efficient for very small datasets or specific edge cases.
2. **Insertion Sort**:
	* Pros:
		+ Simple to understand and implement.
		+ Can be more efficient for small datasets or specific edge cases (e.g., nearly sorted arrays).
	* Cons:
		+ Not as optimized for performance as built-in sort.
		+ May not take advantage of multi-threading or caching.

**Library Used**

None, as the benchmark is implementing its own sorting algorithms instead of using a library.

**Special JS Feature or Syntax**

No special JavaScript features or syntax are being used in this benchmark. The code is written in standard JavaScript and uses only basic data types (numbers, arrays).

**Other Alternatives**

If you're interested in exploring other sorting algorithms or approaches, here are some alternatives:

* **Merge Sort**: A divide-and-conquer algorithm that's similar to built-in sort but can be useful for understanding the basics of comparison-based sorting.
* **QuickSort**: Another divide-and-conquer algorithm that's often used in practice, but may not be as efficient as built-in sort in all cases.
* **Radix Sort**: A non-comparison sorting algorithm that's well-suited for sorting large arrays of integers or strings.
* **External Sorting**: If the dataset is too large to fit into memory, you can use external sorting algorithms like heap sort or merge sort that work with disk-based data.

Related benchmarks:

Insertion vs Built-in sort (version: 0)

Comparing performance of: Built in vs Insertion Sort

Created: 3 years ago by: Guest

Jump to the latest result

Built in

Insertion Sort

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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