Benchmark: Array sort & map vs. map & sort

Script Preparation code:

function getRandomInt(max) {
  return Math.floor(Math.random() * Math.floor(max));
}

var arr = [];
for(var i = 0; i < 100000; i++){
  arr.push([-1,getRandomInt(100)]);
}

Tests:

map & sort
arr.map(r => r[1]).sort()
sort & map
arr.sort((a,b) => a[1]-b[1]).map(r => r[1])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map & sort
sort & map

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36 Edg/133.0.0.0

Browser/OS: Chrome 133 on Windows

View result in a separate tab

Test name	Executions per second
map & sort	66.5 Ops/sec
sort & map	188.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in this benchmark.

**Benchmark Definition**

The benchmark is designed to compare two approaches to mapping and sorting an array of arrays:

1. `arr.map(r => r[1]).sort()`
2. `arr.sort((a,b) => a[1]-b[1]).map(r => r[1])`

These two approaches are essentially the same, but in different orders.

**Options Compared**

The benchmark is comparing the performance of these two approaches:

* **Map & Sort**: Mapping the array first and then sorting it
* **Sort & Map**: Sorting the array first and then mapping it

Both approaches have their own pros and cons:

* **Pros of `map & sort`**:
	+ Can be more intuitive for developers who are used to working with arrays in a specific order.
	+ Might be faster since it only requires one pass through the data.
* **Cons of `map & sort`**:
	+ Requires two passes through the data: once for mapping and once for sorting. This can increase memory usage and processing time.
	+ May not take advantage of optimized sorting algorithms that are designed for specific types of data (e.g., arrays of numbers).
* **Pros of `sort & map`**:
	+ Can be faster since it only requires one pass through the data.
	+ Allows for more optimization opportunities, as the sorting step can be optimized using specialized algorithms.
* **Cons of `sort & map`**:
	+ May require additional memory allocation and deallocation, depending on the sorting algorithm used.

**Library**

There is no explicit library mentioned in the benchmark definition or test cases. However, it's likely that some built-in JavaScript functions are being used, such as `map()`, `sort()`, and `Array.prototype.push()`.

**Special JS Feature/Syntax**

No special JavaScript features or syntax are being tested in this benchmark.

**Other Considerations**

When writing benchmarks like this, consider the following:

* **Keep it simple**: Focus on a specific, well-defined problem, rather than trying to test everything.
* **Use meaningful names**: Choose clear and descriptive names for your test cases and benchmark definitions.
* **Consider edge cases**: Think about how your benchmark will behave in unusual or extreme situations.

**Alternatives**

If you're interested in exploring alternative approaches or optimization techniques, consider the following:

* **Use a different sorting algorithm**: Instead of using the built-in `sort()` function, try implementing a custom sorting algorithm that takes advantage of specific properties of your data.
* **Try parallel processing**: If you have a multi-core processor, you can try dividing the work between multiple threads or processes to speed up the benchmark.
* **Use a different data structure**: Instead of using an array, try using a more efficient data structure like a linked list or a tree.

Related benchmarks:

Array sort & map vs. map & sort (version: 2)

The best way to map and sort array of arrays

Comparing performance of: map & sort vs sort & map

Created: 4 years ago by: Registered User

Jump to the latest result

map & sort

sort & map

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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