Benchmark: Lodash uniqBy vs Set vs custom uniqBy

HTML Preparation code:


<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var MyArr = Array.from({length: 40}, () => Math.floor(Math.random() * 40));

var myCopy = null;

function uniqBy(arr, predicate) {
  const cb = typeof predicate === 'function' ? predicate : (o) => o[predicate];
  
  return [...arr.reduce((map, item) => {
    const key = (item === null || item === undefined) ? 
      item : cb(item);
    
    map.has(key) || map.set(key, item);
    
    return map;
  }, new Map()).values()];
};

Tests:

Lodash uniqBy
myCopy = _.uniqBy(MyArr);
Set
myCopy = [...new Set(MyArr)]
custom uniqBy
myCopy = uniqBy(MyArr, v => v)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash uniqBy
Set
custom uniqBy

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 dive into the benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is testing three approaches for removing duplicates from an array:

1. **Lodash `uniqBy`**: A function that removes duplicate elements from an array based on a provided predicate function.
2. **Set**: A built-in JavaScript data structure that automatically removes duplicates when used as an array.
3. **Custom `uniqBy`**: A custom implementation of the `uniqBy` function.

**Options Compared**

The benchmark is comparing three options:

* Lodash's `uniqBy` function
* The built-in `Set` data structure
* A custom implementation of `uniqBy`

**Pros and Cons**

1. **Lodash `uniqBy`**:
	* Pros: Easy to use, well-documented, and optimized for performance.
	* Cons: Requires including the Lodash library in your codebase, which may add unnecessary size and complexity.
2. **Set**:
	* Pros: Lightweight, easy to understand, and uses built-in JavaScript functionality.
	* Cons: May not be as efficient as custom implementations or libraries like Lodash for large datasets.
3. **Custom `uniqBy`**:
	* Pros: Can be optimized for specific use cases or performance requirements.
	* Cons: Requires manual implementation, which can add complexity and maintainability issues.

**Library: Set**

The built-in `Set` data structure is used in the "Set" benchmark case. A Set is a collection of unique values that automatically removes duplicates when items are added or removed.

**Special JS Feature/Syntax: None**

There are no special JavaScript features or syntax being tested in this benchmark.

**Other Alternatives**

If you're looking for alternative approaches, consider:

* Using the `filter()` method with a predicate function to remove duplicates.
* Implementing a custom sorting and grouping algorithm using array methods like `sort()`, `map()`, and `reduce()`.
* Using a library like Ramda or Immer.js, which offer optimized implementations of functional programming techniques.

When choosing an approach, consider factors such as performance requirements, code complexity, and maintainability. The Lodash `uniqBy` function is often a good default choice due to its ease of use and optimization, but custom implementations may be necessary for specific use cases or performance requirements.

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/117.0

Browser/OS: Firefox 117 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
Lodash uniqBy	971139.9 Ops/sec
Set	798495.4 Ops/sec
custom uniqBy	492697.5 Ops/sec

Related benchmarks:

Lodash uniqBy vs Set vs custom uniqBy (version: 0)

Comparing performance of: Lodash uniqBy vs Set vs custom uniqBy

Created: 2 years ago by: Guest

Jump to the latest result

Lodash uniqBy

Set

custom uniqBy

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):