Benchmark: JS array shallow copy and sort

Script Preparation code:

params = 'qwertyuiopasdfghjklzxcvbnm'.repeat(1000).split('');

Tests:

Spread operator - sort
[...params].sort();
slice - sort
params.slice().sort();
toSorted
params.toSorted();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread operator - sort
slice - sort
toSorted

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36

Browser/OS: Chrome 132 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Spread operator - sort	478.7 Ops/sec
slice - sort	482.3 Ops/sec
toSorted	494.7 Ops/sec

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 data to explain what's being tested and compare different approaches.

**Benchmark Definition**
The benchmark is testing the performance of three methods for sorting an array in JavaScript:

1. Shallow copying and sorting using the spread operator (`[...params].sort();`)
2. Using the `slice()` method followed by sorting (`params.slice().sort();`)
3. Using a custom `toSorted()` function (not shown in the provided code)

**Options Compared**
The benchmark is comparing three different approaches:

1. **Shallow copying and sorting using the spread operator**: This approach creates a new array by spreading the original array's elements into a new array (`[...params]`). The sorted array is then returned.
2. **Using the `slice()` method followed by sorting**: This approach creates a shallow copy of the original array using `slice()`, and then sorts the copied array.
3. **Custom `toSorted()` function**: This approach is not shown in the provided code, so we can't analyze it further.

**Pros and Cons**

* **Shallow copying and sorting using the spread operator**:
	+ Pros: Simple and concise code, creates a new array without modifying the original, potentially faster execution due to reduced overhead.
	+ Cons: May create unnecessary copies of large arrays, can be slower than other approaches for very large datasets.
* **Using the `slice()` method followed by sorting**:
	+ Pros: More efficient use of memory since only a shallow copy is created, can be faster for large datasets.
	+ Cons: Can be slower than the spread operator approach for small to medium-sized arrays due to the overhead of creating a slice.
* **Custom `toSorted()` function**: Not shown in the provided code, so we can't analyze its performance or pros/cons.

**Library and Purpose**
None are explicitly mentioned in the provided code. However, it's worth noting that the use of `slice()` suggests that the benchmark is targeting modern JavaScript engines that support this method.

**Special JS Features or Syntax**
None are mentioned in the provided code.

**Other Alternatives**

Some alternative approaches for sorting arrays in JavaScript include:

* Using `Array.prototype.sort()`: This approach modifies the original array and can be slower than other approaches due to the overhead of sorting.
* Using a library like Lodash (`_.sortBy()`): This approach uses an external library, which may have its own performance implications.

**Benchmark Preparation Code**
The script preparation code is a simple string literal that creates a long array by repeating the string `'qwertyuiopasdfghjklzxcvbnm'` 1000 times and splitting it into individual characters:

```javascript
params = 'qwertyuiopasdfghjklzxcvbnm'.repeat(1000).split('');
```

This code is designed to create a large array that can be sorted, allowing the benchmark to focus on the performance of each sorting approach.

Related benchmarks:

JS array shallow copy and sort (version: 0)

Comparing performance of: Spread operator - sort vs slice - sort vs toSorted

Created: 2 years ago by: Guest

Jump to the latest result

Spread operator - sort

slice - sort

toSorted

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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