Benchmark: sort vs sorted insert

Tests:

sort
var arr = []; var comparator = (a,b)=>{ var length = a.length; for(var i=0; i<length; i++){ if (a[i]<b[i]) {return -1;} else if(a[i]>b[i]) {return 1;} } return 0; }; for (var i=0; i<10000; i++){ var a1 = Math.random(); var a2 = Math.random(); var a3 = Math.random(); var a4 = Math.random(); arr.push([a1,a2,a3,a4]); } arr.sort(comparator); console.log(arr[arr.length-1]);
insert
var arr = []; var comparator = (m,val)=>{ var length = m.length; for(var i=0; i<length; i++){ if (m[i]>val[i]) {return false;} } return true; }; var find_position = (arr,val)=>{ var l=-1; var r=arr.length; while(l<r-1){ var m = Math.floor((l+r)/2); if (comparator(arr[m],val)) { l=m; } else { r=m; } } return l; }; for (var i=0; i<10000; i++){ var a1 = Math.random(); var a2 = Math.random(); var a3 = Math.random(); var a4 = Math.random(); var row = [a1,a2,a3,a4]; var new_position = find_position(arr,row)+1; arr.splice(new_position,null,row); } console.log(arr[arr.length-1]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
sort
insert

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down what is being tested on the provided JSON.

**Benchmark Definition**

The benchmark definition consists of two test cases:

1. **"sort"**: This test case creates an array `arr` and populates it with 10,000 random elements. It then sorts the array using the `sort()` method, which is a built-in JavaScript function that uses the QuickSort algorithm.
2. **"insert"**: This test case also creates an array `arr` and populates it with 10,000 random elements. However, instead of sorting the array, it uses a custom search function `find_position()` to find the position where a specific element should be inserted. The array is then modified by inserting the new element at that position using `splice()`. Finally, the last element of the array is logged to the console.

**Options being compared**

The two test cases are comparing the performance of sorting an array versus searching for and inserting an element into the same array.

**Pros and Cons of each approach:**

1. **Sorting**: This approach has several pros:
	* It ensures that all elements in the array are in a sorted order.
	* It can be faster for large datasets, as it reduces the number of comparisons required to find an element.
	* However, it also has some cons:
		+ It may require more CPU cycles and memory compared to searching and inserting an element individually.
		+ The sorting algorithm used (QuickSort in this case) has a worst-case time complexity of O(n log n), which means that the performance can degrade for very large datasets.
2. **Searching and Inserting**: This approach also has some pros:
	* It can be faster for small to medium-sized datasets, as it only requires searching for and inserting one element at a time.
	* However, it also has some cons:
		+ It may not ensure that all elements in the array are in a sorted order (although this is not a requirement for this test case).
		+ The search function used (binary search) has a worst-case time complexity of O(log n), but it still requires multiple iterations to find the correct position.

**Library and its purpose**

In both test cases, there is no external library being used. However, it's worth noting that the `sort()` method and the `find_position()` function are built-in JavaScript functions that rely on various algorithms (QuickSort for sorting and binary search for finding an element).

**Special JS feature or syntax**

There is one special feature being used in this test case: the use of a callback function in the `comparator` variable. This allows the sorting function to compare elements based on multiple criteria.

**Other alternatives**

If you wanted to write this benchmark differently, here are some alternative approaches:

1. **Use a different sorting algorithm**: Instead of using QuickSort, you could try other sorting algorithms like MergeSort or HeapSort.
2. **Increase/decrease dataset size**: You could increase or decrease the number of elements in the array to see how performance changes.
3. **Use parallel processing**: If you have multiple cores available, you could use parallel processing techniques to speed up both sorting and searching/insering.
4. **Use a different data structure**: Instead of using an array, you could try using a linked list or a tree-based data structure (like a BST) to see how performance changes.

These are just some examples, but I hope this explanation helps!

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36

Browser/OS: Chrome 105 on Windows

View result in a separate tab

Test name	Executions per second
sort	107.5 Ops/sec
insert	30.5 Ops/sec

Related benchmarks:

sort vs sorted insert (version: 0)

Comparing performance of: sort vs insert

Created: 2 years ago by: Guest

Jump to the latest result

sort

insert

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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