Benchmark: set.add vs array.push Fabien2

Script Preparation code:

var array = [];
var set = new Set();

Tests:

array.push
for (let i=0; i<100; i++) { array.push(i); }
set.add
for (let i=0; i<100; i++) { set.add(i); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array.push
set.add

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36

Browser/OS: Chrome 96 on Windows

View result in a separate tab

Test name	Executions per second
array.push	84964.9 Ops/sec
set.add	78081.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark definition and test cases.

**Benchmark Definition:**

The provided JSON represents a JavaScript microbenchmark, specifically comparing the performance of two different approaches to adding elements to an array or set data structure:

1. `array.push`
2. `set.add`

**What is being tested?**

In this case, we're testing the execution time of each approach when adding 100 elements to an array or a Set object.

**Options compared:**

We have two options being compared:

1. **Array.push**: This method appends one or more elements to the end of an array and returns the length of the array.
2. **Set.add**: This method adds one or more elements to a set data structure and returns true if the element was added, or false if it already exists in the set.

**Pros and Cons:**

Here's a brief overview of each approach:

* **Array.push**:
	+ Pros:
		- Simple and straightforward implementation.
		- Works well for large datasets when the array is sparse (i.e., most elements are at the end).
	+ Cons:
		- Requires extra memory allocation to create a new element, which can be expensive for large arrays.
		- Can lead to slow performance due to the need to traverse the entire array to find the correct insertion point.
* **Set.add**:
	+ Pros:
		- Efficient implementation with an average time complexity of O(1) for adding elements.
		- Uses a hash table internally, which makes it well-suited for large datasets.
	+ Cons:
		- Requires a JavaScript engine that supports Sets (most modern engines do).
		- Can be slower than `array.push` in some cases due to the overhead of the Set's internal data structure.

**Library/Functionality:**

In this case, we're using built-in JavaScript functions and data structures:

* **Array**: A dynamic array data structure that can grow or shrink as elements are added.
* **Set**: An unordered collection of unique values, implemented using a hash table internally.

No external libraries or frameworks are required for these benchmarks.

**Special JS feature/Syntax:**

There is no special JavaScript feature or syntax being used in this benchmark. It's purely focused on testing the performance of two different array/set operations.

**Other alternatives:**

If you were to modify this benchmark, here are some alternative approaches you could consider:

1. **Using `array splice()`**: This method replaces elements at a specified index and returns the number of elements replaced.
2. **Using `array unshift()`**: This method adds one or more elements to the beginning of an array.
3. **Using `map()` with `Array.prototype.forEach()`**: This approach creates a new array by mapping over the original array and then iterating over the resulting array using `forEach()`.
4. **Using `Array.from()` with `Set`**: This approach creates a new Set from an iterable (e.g., an array) and then adds elements to it.

Keep in mind that these alternatives may introduce additional complexity or assumptions about the input data, so be sure to test them thoroughly before using them as benchmarks.

Related benchmarks:

set.add vs array.push Fabien2 (version: 0)

Comparing performance of: array.push vs set.add

Created: 2 years ago by: Guest

Jump to the latest result

array.push

set.add

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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