Benchmark: lodash uniq vs filter vs set

HTML Preparation code:


<script src='https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.min.js'></script>

Tests:

Filter
var l = [1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7].filter((item, index, array) => array.indexOf(item) === index); return l;
Array
var l = [1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]; return _.uniq(l);
Set
var l = [1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7] const _set = new Set(l) function setToArray(set) { let index = -1 const result = new Array(set.size) set.forEach((value) => { result[++index] = value }) return result } return setToArray(_set)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Filter
Array
Set

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; rv:109.0) Gecko/20100101 Firefox/115.0

Browser/OS: Firefox 115 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
Filter	2120882.8 Ops/sec
Array	8479716.0 Ops/sec
Set	2356588.2 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark measures the performance of three different approaches to remove duplicates from an array: `filter()`, `uniq()` (from the Lodash library), and using a `Set` data structure.

**Options Compared**

* `Filter()`: Uses the built-in `Array.prototype.filter()` method to create a new array with only unique elements.
* `Uniq()`: Uses the Lodash `_.uniq()` function, which creates an array with unique values by removing duplicates from the input array.
* `Set`: Uses a JavaScript `Set` data structure to remove duplicates and then converts it back to an array.

**Pros and Cons**

* **Filter():**
	+ Pros: Simple, widely supported, and efficient for small arrays. However, its performance can degrade significantly for large datasets due to the need to iterate over the entire array.
	+ Cons: May be slower than `uniq()` or `Set` for large inputs, especially if the input array is sorted.
* **Uniq():**
	+ Pros: Efficient and fast, even for large arrays. This is because Lodash's implementation uses a more optimized algorithm that doesn't require sorting the input array.
	+ Cons: Requires including the Lodash library, which may add overhead to the benchmark.
* **Set:**
	+ Pros: Fast and efficient, especially for large inputs. The `Set` data structure has an average time complexity of O(1) for insertion and lookup operations.
	+ Cons: May require additional memory allocation if the input array is very large, since sets store unique elements in memory.

**Library and Purpose**

* Lodash's `_.uniq()` function is used to remove duplicates from an array. It creates a new array with unique values by iterating over the input array and checking for duplicate elements. This implementation uses a more efficient algorithm than the built-in `Array.prototype.filter()` method, which makes it suitable for large inputs.

**Special JS Feature or Syntax**

None of the individual test cases use any special JavaScript features or syntax that's not widely supported.

**Other Alternatives**

* For removing duplicates from an array, other approaches include using a combination of `map()`, `reduce()`, and a data structure like a `Set` or an object with a unique key.
* For creating a set-like data structure without the built-in `Set` type, you could use a JavaScript object with a unique key, such as `{ [key: string]: value }`.

Overall, this benchmark provides a useful comparison of different approaches to removing duplicates from an array. The results can help developers choose the most efficient method for their specific use case.

Related benchmarks:

lodash uniq vs filter vs set (version: 0)

Comparing performance of: Filter vs Array vs Set

Created: one year ago by: Guest

Jump to the latest result

Filter

Array

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):