Benchmark: Comb Sort vs. Native Sort

Script Preparation code:

var sampleData = [];
for (var i=0; i < 1000; i++){ 
  sampleData.push({value: (Math.floor(Math.random() * 1000))});
}

var selectorFn = function(x) { 
  return x.value;  
}

var combSort = function (source, selectorFn, sortDirection) {
    var result = source.slice();
    var count = result.length;
    var gap = count;
    var swapped = true;

    while (gap > 1 || swapped) {

      if (gap > 1) {
        gap = Math.floor(gap / 1.24733);
      }

      var i = 0;
      swapped = false;

      while ((i + gap) < count) {

        var item1 = result[i];
        var item2 = result[i + gap];

        if (shouldBeSwaped(selectorFn(item1), selectorFn(item2), selectorFn, sortDirection)) {
          result[i] = item2;
          result[i + gap] = item1;
          swapped = true;
        }

        i++;
      }
	}
  
    return result;
}

 var shouldBeSwaped = function(value, valueToCompare, selectorFn, sortDirection) {
 	var less = value > valueToCompare;
	return sortDirection === "asc" ? less : !less;
  }

Tests:

Native Sort DESC
var order = "desc"; sampleData.sort(function(a,b){ var x = selectorFn(a); var y = selectorFn(b); var res = ((x < y) ? -1 : ((x > y) ? 1 : 0)); return order === "desc" ? -1*res : res; })
Native Sort ASC
var order = "desc"; sampleData.sort(function(a,b){ var x = selectorFn(a); var y = selectorFn(b); var res = ((x < y) ? -1 : ((x > y) ? 1 : 0)); return order === "desc" ? -1*res : res; })
Comb Sort DESC
var order = "asc"; combSort(sampleData, selectorFn, order);
Comb Sort ASC
var order = "asc"; combSort(sampleData, selectorFn, order);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native Sort DESC
Native Sort ASC
Comb Sort DESC
Comb Sort ASC

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.

I'll break down the provided JSON benchmark definitions and explain what's being tested, compared options, pros and cons, library usage, special JavaScript features, and other considerations.

**Benchmark Definition**

The benchmark is comparing two sorting algorithms: Native Sort (also known as Timsort in modern browsers) and Comb Sort. The goal is to determine which algorithm performs better for a specific dataset size.

**Script Preparation Code**

The script generates an array of 1000 random objects with a `value` property between 0 and 999. This will be used as the input data for both sorting algorithms.

**Benchmark Options**

There are four benchmark options:

1. **Native Sort DESC**: Tests Native Sort in descending order.
2. **Native Sort ASC**: Tests Native Sort in ascending order.
3. **Comb Sort DESC**: Tests Comb Sort in descending order.
4. **Comb Sort ASC**: Tests Comb Sort in ascending order.

**Library Usage**

The `selectorFn` function is used to compare two objects in the array. It's defined as a simple getter function that returns the value property of an object. Both sorting algorithms use this function to compare elements.

**Special JavaScript Features**

None are explicitly mentioned, but note that modern browsers' Timsort implementation uses some advanced techniques like:

* Using the `Array.prototype.sort()` method internally, which involves comparing elements in a more efficient way than a simple callback function.
* Leveraging the cache to reduce comparisons during iteration.

**Pros and Cons of Options**

1. **Native Sort**: Pros:
	* Optimized for large datasets and stable sorting (less swaps).
	* Uses the `Array.prototype.sort()` method, which is optimized by modern browsers.
Cons:
	* Not optimized for small datasets or unsorted data.
2. **Comb Sort**: Pros:
	* Well-suited for small to medium-sized datasets with some order (reduces comparisons).
Cons:
	* Has higher overhead due to the gap reduction mechanism and more swaps compared to Native Sort.

**Other Considerations**

* The dataset size of 1000 elements is likely chosen for its moderate complexity, allowing both algorithms to exhibit their performance characteristics.
* The use of a random `selectorFn` ensures that the comparison logic remains consistent across different sorting implementations.

**Alternatives**

Other sorting algorithms could be used as alternatives in benchmarking, such as:

1. **Quicksort**: A popular and efficient algorithm for large datasets, but may perform worse on smaller datasets.
2. **Merge Sort**: Another efficient algorithm suitable for small to medium-sized datasets.
3. **Heapsort**: Suitable for certain use cases where stability is important, like sorting a priority queue.

Keep in mind that the choice of alternative algorithms depends on the specific requirements and characteristics of the dataset being sorted.

Related benchmarks:

Comb Sort vs. Native Sort (version: 0)

Comparing performance of: Native Sort DESC vs Native Sort ASC vs Comb Sort DESC vs Comb Sort ASC

Created: 9 years ago by: Guest

Jump to the latest result

Native Sort DESC

Native Sort ASC

Comb Sort DESC

Comb Sort ASC

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):