Benchmark: lodash uniq vs vanilla set

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.11/lodash.min.js'></script>

Script Preparation code:

var firstEqual = [];
var secondEqual = [];

for (var i=0; i<=1000; i++) {
  firstEqual.push(i);
  secondEqual.push(i);
}

var arrayToDedup = [...firstEqual, ...secondEqual];

Tests:

Uniq
_.uniq(arrayToDedup);
Set
[...new Set(arrayToDedup)]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Uniq
Set

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/142.0.0.0 Safari/537.36

Browser/OS: Chrome 142 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Uniq	19022.8 Ops/sec
Set	17441.0 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, compared, and some of the pros and cons associated with each approach.

**Benchmark Definition**

The benchmark is comparing two approaches to remove duplicates from an array:

1. **Lodash `uniq`**: Lodash is a popular JavaScript utility library that provides a wide range of functions for data manipulation, among other things.
2. **Vanilla `Set`**: This is the built-in JavaScript object type used to store unique values.

**Script Preparation Code**

The script creates two arrays with 1000 elements each and pushes them onto two separate arrays (`firstEqual` and `secondEqual`). Then, it uses the spread operator (`...`) to concatenate these two arrays into a single array (`arrayToDedup`).

**Html Preparation Code**

A reference to the Lodash library is included in the HTML code using a CDN link. This means that the benchmark will use the Lodash library's implementation of `uniq`.

**Individual Test Cases**

There are two test cases:

1. **Uniq**: This tests the performance of Lodash's `uniq` function on the prepared array (`arrayToDedup`).
2. **Set**: This tests the performance of using a vanilla `Set` object to remove duplicates from the same prepared array.

**Pros and Cons**

**Lodash `uniq`:**

* Pros:
	+ Familiar syntax for developers familiar with Lodash.
	+ Optimized implementation by Lodash maintainers.
* Cons:
	+ Adds additional overhead due to the library's existence and initialization time.
	+ May not be as performant as a purely vanilla JavaScript solution.

**Vanilla `Set`:**

* Pros:
	+ Lightweight, built-in implementation with minimal overhead.
	+ Fast and efficient for large datasets.
* Cons:
	+ Requires manual handling of iteration and conversion to/from array.
	+ May have limitations in edge cases (e.g., NaN values).

**Other Considerations**

When choosing between these approaches, consider the following:

* **Development time**: If you need a quick prototype or proof-of-concept, vanilla `Set` might be faster to implement. For production code, Lodash's well-maintained implementation may be more suitable.
* **Readability and maintainability**: If readability and maintainability are crucial for your project, Lodash's `uniq` function might be a better choice due to its familiar syntax.

**Alternative Approaches**

Other alternatives to consider:

* Using a different library or framework (e.g., Immutable.js) that provides similar functionality.
* Implementing the `uniq` function manually using JavaScript.
* Using a custom solution with a specific optimization technique (e.g., hash-based deduplication).

Keep in mind that the choice of approach ultimately depends on your project's requirements, performance needs, and development constraints.

Related benchmarks:

lodash uniq vs vanilla set (version: 0)

Comparing performance of: Uniq vs Set

Created: 7 years ago by: Registered User

Jump to the latest result

Uniq

Set

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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