Benchmark: lodash uniq vs spread new Set()

HTML Preparation code:

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

Tests:

Spread
return new Set([1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7])
use lodash
return _.uniq([1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread
use lodash

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 2 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Definition**

The benchmark definition is provided as a JSON object, which contains information about the test case being run. In this case, there are two test cases: "Spread" and "use lodash". The first test case simply creates a new Set from an array of 16 elements with some duplicates. The second test case uses the Lodash library's `uniq` function to achieve the same result.

**Options Compared**

The benchmark is comparing two approaches:

1. **Direct Set Creation**: This approach creates a new Set using the built-in `Set` constructor, as shown in the first test case: `return new Set([1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7])`.
2. **Lodash uniq Function**: This approach uses the Lodash library's `uniq` function to remove duplicates from an array, as shown in the second test case: `return _.uniq([1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7])`.

**Pros and Cons**

Here are some pros and cons of each approach:

**Direct Set Creation**

Pros:

* Lightweight: Creating a new Set is a lightweight operation that doesn't incur significant overhead.
* Fast: Accessing elements in a Set is generally fast.

Cons:

* Error-prone: If the input array has duplicate values, it may lead to unexpected behavior or errors when trying to access elements.

**Lodash uniq Function**

Pros:

* Robust: The Lodash library provides a robust implementation of the `uniq` function that handles edge cases and duplicates correctly.
* Convenient: Using an existing library can simplify code and reduce development time.

Cons:

* Overhead: Including an external library adds overhead, which may impact performance in some cases.
* Dependence on external library: The benchmark relies on the Lodash library being installed and available, which may not always be the case.

**Other Considerations**

* **Use of a Library**: Using a library like Lodash can make code more readable and maintainable, but it also introduces dependencies that may impact performance.
* **JavaScript Features**: Neither test case uses any special JavaScript features or syntax. The benchmark is focused on comparing two approaches to removing duplicates from an array.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. **Using `filter()` and `includes()`**: You can use the `filter()` method to remove elements that don't match a condition, and then check if each remaining element is included in the original array using the `includes()` method.
2. **Using a custom implementation**: If you want to implement your own duplicate removal algorithm, you can explore various approaches, such as using a hash table or a trie data structure.

Keep in mind that these alternatives may have different performance characteristics and trade-offs compared to the Lodash implementation.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36

Browser/OS: Chrome 116 on Linux

View result in a separate tab

Test name	Executions per second
Spread	976333.7 Ops/sec
use lodash	1754250.0 Ops/sec

Related benchmarks:

lodash uniq vs spread new Set() (version: 0)

Comparing performance of: Spread vs use lodash

Created: one year ago by: Guest

Jump to the latest result

Spread

use lodash

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 2 days ago):