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;
  }

​x
 
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;
})

 
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;
})

 
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);
var order = "asc";

combSort(sampleData, selectorFn, order);
Comb Sort ASC
var order = "asc"; combSort(sampleData, selectorFn, order);
var order = "asc";

combSort(sampleData, selectorFn, order);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

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:

Run details: (Test run date: 8 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36

Browser/OS: Chrome 52 on Windows

View result in a separate tab

Test name	Executions per second
Native Sort DESC	222.8 Ops/sec
Native Sort ASC	224.5 Ops/sec
Comb Sort DESC	263.0 Ops/sec
Comb Sort ASC	239.6 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

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:

Native Sort DESC: Tests Native Sort in descending order.
Native Sort ASC: Tests Native Sort in ascending order.
Comb Sort DESC: Tests Comb Sort in descending order.
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

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.
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:

Quicksort: A popular and efficient algorithm for large datasets, but may perform worse on smaller datasets.
Merge Sort: Another efficient algorithm suitable for small to medium-sized datasets.
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.

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**

**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: 8 years ago by: Guest

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