Benchmark: Sorting test - MeasureThat.net

Tests:

custom sort
const array1 = [1, 3, 9]; const array2 = [2, 9, 4]; let array = mergeSort(array1, array2); console.log(array); function mergeSort(array1, array2) { //comparing two popular merging techniques spread is slightly faster so that's being used //merge arrays let mergedArray = [...array1, ...array2]; //sort arrays // loop through array for (let i = 0; i < mergedArray.length - 1; i++) { //set first index let min = i; //loop through array again for (let j = i + 1; j < mergedArray.length; j++) { //check if number in first array is smaller if reset index if (mergedArray[j] < mergedArray[min]) { min = j; } } //if index of first for has been reset above it's diffirent which means we can add it to end of the array if (min != i) { let target = mergedArray[i]; mergedArray[i] = mergedArray[min]; mergedArray[min] = target; } } return mergedArray; //return mergedArray.sort((a,b) => a-b); }
normal sort
const array1 = [1, 3, 9]; const array2 = [2, 9, 4]; let array = mergeSort(array1, array2); console.log(array); function mergeSort(array1, array2) { //comparing two popular merging techniques spread is slightly faster so that's being used //merge arrays let mergedArray = [...array1, ...array2]; //sort arrays return mergedArray.sort((a,b) => a-b); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
custom sort
normal sort

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 dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Overview**

The provided benchmark measures the performance of two sorting algorithms: mergeSort (also known as merge and sort) and regular sort. The test cases are identical, except for how they approach merging and sorting the arrays.

**Options Compared**

Two options are compared:

1. **Merge and Sort**: This method merges the two input arrays using the spread operator (`[...array1, ...array2]`) and then sorts the merged array using a custom comparison function (`(a,b) => a-b`).
2. **Regular Sort**: This method uses the built-in `sort()` method of JavaScript arrays, which internally implements a sorting algorithm called Timsort.

**Pros and Cons**

### Merge and Sort

Pros:

* The spread operator is slightly faster than using an array concatenation (`array1.concat(array2)`).
* It avoids the overhead of calling a separate sort function.
* It allows for more control over the merging process.

Cons:

* Requires custom implementation of the sorting logic.
* Can be less efficient if not optimized correctly.
* May not work as expected if the input arrays have different lengths or data types.

### Regular Sort

Pros:

* Built-in and well-tested, so you can trust its performance.
* Handles edge cases (e.g., null or undefined values) more robustly.
* Often faster than custom implementations for large datasets.

Cons:

* May be slower than a custom implementation optimized for your specific use case.
* Less control over the sorting process.

**Other Considerations**

When choosing between these two options, consider the following factors:

* Performance: If speed is critical and you're dealing with very large datasets or performance-critical code, a custom implementation might be faster.
* Complexity: If you want to avoid implementing a sorting algorithm from scratch, regular sort is often the simpler choice.

**Library Used**

In this benchmark, no external libraries are used. The `sort()` method is part of the JavaScript standard library.

**Special JS Feature/Syntax**

The use of template literals (`\r\nconst array = mergeSort(array1, array2);\r\n\r\nconsole.log(array);`) and destructuring assignment (`let [a,b] = mergedArray[0].split(')')`) are not used extensively in this benchmark.

However, it's worth noting that the `mergeSort` function uses a technique called "in-place sorting," which modifies the original array instead of creating a new one. This can be an efficient approach for large datasets but may lead to unexpected behavior if not handled correctly.

**Alternatives**

If you're interested in exploring other sorting algorithms or optimizations, consider the following alternatives:

* **Quicksort**: A popular in-place sorting algorithm with an average time complexity of O(n log n).
* **Heapsort**: Another efficient in-place sorting algorithm with a time complexity of O(n log n) in the worst case.
* **Radix sort**: A non-comparative sorting algorithm suitable for large datasets of integers or strings.

These alternatives can provide different trade-offs between performance, complexity, and memory usage. You may need to experiment and profile your specific use case to determine the most suitable algorithm.

Latest run results:

Run details: (Test run date: 9 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:132.0) Gecko/20100101 Firefox/132.0

Browser/OS: Firefox 132 on Windows

View result in a separate tab

Test name	Executions per second
custom sort	193096.2 Ops/sec
normal sort	188422.4 Ops/sec

Related benchmarks:

Sorting test (version: 0)

Comparing performance of: custom sort vs normal sort

Created: 5 years ago by: Guest

Jump to the latest result

custom sort

normal sort

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):