Benchmark: find smallest element in array sort vs reduce

Script Preparation code:

var arr = [];
for(let i = 0; i < 10000000; i++){
	const num = Math.floor(Math.random() * 10000000);
	arr.push(num);
}

Tests:

reduce
console.log(arr.reduce((acc, curr) => acc < curr ? acc : curr, Infinity))
sort
console.log(arr.sort()[0])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
sort

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/124.0.0.0 Safari/537.36

Browser/OS: Chrome 124 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	20.6 Ops/sec
sort	9.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what's being tested.

The test is designed to compare two approaches for finding the smallest element in an array: using `reduce()` and using `sort()`. The arrays are generated randomly with 10 million elements each.

**Options compared:**

1. **Reduce()**: This method reduces the array by repeatedly applying a callback function, which returns the first value that satisfies the condition. In this case, the callback function checks if the accumulator (`acc`) is less than the current element (`curr`), and if so, returns `acc`. If not, it returns `curr`. The initial value for the accumulator is set to infinity.
2. **Sort()**: This method sorts the array in ascending order and returns the first element of the sorted array.

**Pros and Cons:**

1. **Reduce():**
	* Pros:
		+ More efficient for finding the smallest element, as it only needs to iterate through the array once.
		+ Can be more readable and concise, especially when dealing with complex logic.
	* Cons:
		+ May not work correctly if the input array is empty or has only one element.
		+ Requires a callback function, which can add complexity.
2. **Sort():**
	* Pros:
		+ Works correctly for any sorted array, regardless of its length.
		+ Can be used to sort arrays as well.
	* Cons:
		+ Has a higher time complexity than `reduce()`, especially for large arrays.
		+ May not be suitable if the input array is already partially sorted.

**Library usage:**

There are no external libraries explicitly mentioned in the JSON. However, `Math.random()` and `Infinity` are built-in JavaScript functions.

**Special JS features or syntax:**

There's no mention of special JavaScript features or syntax in the provided code.

**Benchmark considerations:**

When benchmarking these two approaches, consider the following factors:

* Array size: The larger the array, the more significant the difference between the two methods.
* Browser and device performance: Different browsers and devices may exhibit varying degrees of performance, which can affect the results.
* Optimization: Both methods can be optimized to improve performance. For example, using `Array.prototype.reduce()` with a faster callback function or using `sort()` with a custom comparator.

**Alternative approaches:**

If you need an even more efficient solution, consider:

1. **Using a data structure like a min-heap**: This can provide the smallest element in O(log n) time complexity.
2. **Using binary search**: This method can find the smallest element in O(log n) time complexity, but it requires the array to be sorted.
3. **Using a single pass with a bit manipulation trick**: This approach can achieve O(n) time complexity, making it more efficient than `reduce()` for very large arrays.

Keep in mind that the choice of approach depends on your specific use case and performance requirements.

Related benchmarks:

find smallest element in array sort vs reduce (version: 0)

Comparing performance of: reduce vs sort

Created: one year ago by: Guest

Jump to the latest result

reduce

sort

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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