Benchmark: proper sort vs 2 arrays

Script Preparation code:

var notSelectedArray = Array(200).fill({ value: 'notSelected' });
var selectedArray = Array(200).fill({ value: 'selected', selected: true });
var array = [];
for(var i = 0; i < 200; i++) { 
  array.push(notSelectedArray[i]);
  array.push(selectedArray[i]);
}

Tests:

sort
var result = [...array].sort((a, b) => a.isSelected - b.isSelected);
2 arrays
var selectedInterests = []; var notSelectedInterests = []; array.forEach(interest => { const isSelected = interest.selected; if (isSelected) { selectedInterests.push(interest); } else { notSelectedInterests.push(interest); } }); return [...selectedInterests, ...notSelectedInterests];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
sort
2 arrays

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the world of JavaScript microbenchmarks!

**What is being tested?**

The provided JSON represents two individual test cases for a JavaScript benchmark:

1. **"2 arrays"**: This test case involves creating two arrays, `notSelectedArray` and `selectedArray`, each containing 200 elements with different properties (e.g., `value` and `selected`). The test then iterates through the original array and pushes elements from both `notSelectedArray` and `selectedArray` into a new array. Finally, it returns this new array.
2. **"sort"**: This test case uses the `sort()` method to sort an array based on a custom comparison function that compares the `selected` property of each element.

**Options compared**

In both cases, we have two main approaches:

**Approach 1: Using `sort()`**

* **Pros**:
	+ Efficient use of built-in sorting algorithm.
	+ Easy to implement and maintain.
* **Cons**:
	+ May not be suitable for large datasets or complex data structures.
	+ Can be slower than custom solutions for small arrays.

**Approach 2: Manual iteration**

* **Pros**:
	+ Customizable solution that can handle complex data structures.
	+ May be faster than `sort()` for small to medium-sized arrays.
* **Cons**:
	+ More prone to errors and bugs due to manual implementation.
	+ Less efficient than built-in sorting algorithms.

**Other considerations**

When choosing between these approaches, consider the following factors:

* **Data size**: For large datasets, custom iteration may be faster. However, for small datasets, `sort()` might be sufficient.
* **Complexity of data structure**: If your array contains complex data structures or nested objects, manual iteration might be a better choice.
* **Performance-critical code**: In performance-critical code, using built-in sorting algorithms like `sort()` can provide better scalability.

**Library usage**

In the provided JSON, no libraries are explicitly mentioned. However, if we assume that some JavaScript features or syntax were used (which is not the case here), it's essential to be aware of potential library dependencies or limitations.

**Special JS feature/syntax**

There are no special JS features or syntax used in these test cases. Both examples rely on standard JavaScript language constructs.

Now, let's discuss alternative approaches:

If you're interested in exploring other benchmarking options, consider the following alternatives:

* **Benchmarking frameworks**: Frameworks like Benchmark.js, Speedtest.net, or Microbenchmark can help you create more comprehensive benchmarks.
* **Custom testing libraries**: Libraries like Jest, Mocha, or AVA provide robust testing tools and can be used to write custom benchmarks.

Keep in mind that each approach has its strengths and weaknesses. The choice ultimately depends on the specific requirements of your project and the goals of your benchmarking exercise.

Related benchmarks:

proper sort vs 2 arrays (version: 0)

Comparing performance of: sort vs 2 arrays

Created: 3 years ago by: Guest

Jump to the latest result

sort

2 arrays

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):