Benchmark: lodash uniq vs Array.from(new Set()) vs spread new Set() 2

HTML Preparation code:

<script src='https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.min.js'></script>
<script>
const arr = (new Array(10000)).fill().map(e => Math.floor(Math.random() * 100));
</script>

Tests:

Spread
return [...new Set(arr)]
use lodash
return _.uniq(arr);
Array.from
return Array.from( new Set(arr) );
just set
new Set(arr)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread
use lodash
Array.from
just set

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON benchmark and explain what is tested, compared, and their pros/cons.

**Benchmark Definition**

The benchmark measures the performance of three approaches to remove duplicates from an array:

1. `Array.from(new Set(arr))`: This approach uses the `Set` object to automatically eliminate duplicate elements.
2. `_uniq(arr)`: This approach uses the Lodash library's `uniq` function, which removes duplicates from an array.
3. `new Set(arr)`: This approach creates a new `Set` object and adds all elements from the original array.

**Options Compared**

The benchmark compares these three approaches to remove duplicates from an array of 10,000 random integers.

**Pros and Cons**

Here's a brief summary of each approach:

1. **Array.from(new Set(arr))**:
	* Pros: Efficient and concise way to eliminate duplicates.
	* Cons: May not be suitable for large arrays or complex data structures, as it uses the `Set` object internally.
2. **_uniq(arr)_ (Lodash)**:
	* Pros: Widely used and well-tested library function that can handle various edge cases.
	* Cons: Adds an extra dependency on Lodash and may have performance overhead due to its complexity.
3. **new Set(arr)**:
	* Pros: Simple and lightweight way to create a new set of unique elements.
	* Cons: May not be as efficient as `Array.from(new Set(arr))` for large arrays, as it uses additional memory to store the set.

**Library: Lodash**

Lodash is a popular JavaScript library that provides a wide range of utility functions. The `_uniq` function in this benchmark removes duplicate elements from an array and returns a new array with unique elements.

**Special JS Features/Syntax**

None mentioned in the provided JSON.

**Other Alternatives**

If you want to explore alternative approaches, here are a few options:

1. **Using `filter()`**: You can use the `filter()` method to remove duplicates from an array by comparing each element with its neighbors.
```javascript
arr.filter((value, index) => arr.indexOf(value) === index)
```
2. **Using `reduce()`**: You can use the `reduce()` method to combine unique elements into a new array.
```javascript
arr.reduce((uniqueArr, value) => {
  if (!uniqueArr.includes(value)) {
    uniqueArr.push(value);
  }
  return uniqueArr;
}, [])
```
3. **Using a simple loop**: You can use a simple loop to iterate over the array and add each element to a new array only if it's not already present.
```javascript
const uniqueArr = [];
for (const value of arr) {
  if (!uniqueArr.includes(value)) {
    uniqueArr.push(value);
  }
}
```
These alternatives may have different performance characteristics compared to the approaches tested in this benchmark.

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/119.0.0.0 Safari/537.36

Browser/OS: Chrome 119 on Windows

View result in a separate tab

Test name	Executions per second
Spread	8115.3 Ops/sec
use lodash	8215.2 Ops/sec
Array.from	8151.4 Ops/sec
just set	8245.4 Ops/sec

Related benchmarks:

lodash uniq vs Array.from(new Set()) vs spread new Set() 2 (version: 1)

Comparing performance of: Spread vs use lodash vs Array.from vs just set

Created: one year ago by: Registered User

Jump to the latest result

Spread

use lodash

Array.from

just set

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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