Benchmark: Lodash uniqBy vs Javascript uniqBy with key

HTML Preparation code:

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

Script Preparation code:

var users = [
  { 'user': 'joey',  'age': 29 },
  { 'user': 'joey',  'age': 32 },
  { 'user': 'ross',    'age': 41 },
  { 'user': 'ross',    'age': 25 },
  { 'user': 'ross',    'age': 48 },
  { 'user': 'chandler', 'age': 39 },
  { 'user': 'chandler', 'age': 12 },
  { 'user': 'chandler', 'age': 41 }
]

Tests:

Native uniqueBy
const uniqueBy = (array, key) => array.filter((value, index, self) => self.findIndex((item) => item[key] === value[key]) === index); let uniqued = uniqueBy(users, 'user');
Lodash uniqBy
let uniqued = _.uniqBy(users, ['user']);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native uniqueBy
Lodash uniqBy

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

**Benchmark Explanation**

The provided JSON represents two microbenchmarks, both testing the uniqueness filtering functionality of arrays. The benchmarks are:

1. `Native uniqueBy`: This test compares the performance of the native JavaScript method `Array.prototype.filter()` used in conjunction with the `findIndex()` and comparison operator (`===`) to find unique elements.
2. `Lodash uniqBy`: This test compares the performance of the popular JavaScript library Lodash's `uniqBy` function, which is designed specifically for uniqueness filtering.

**Comparison Options**

The two tests compare different approaches to achieving uniqueness filtering:

1. **Native `Array.prototype.filter()`**: This approach uses the native JavaScript method to filter the array and find unique elements. It relies on the `findIndex()` method to identify unique values and then filters out duplicates.
2. **Lodash's `uniqBy` function**: This approach uses Lodash's dedicated function for uniqueness filtering, which is optimized for performance.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Native `Array.prototype.filter()`**:
	* Pros: Native implementation, potentially faster since it avoids a library overhead.
	* Cons: Requires manual implementation of the filtering logic, might not be as efficient for large datasets due to its iterative nature.
2. **Lodash's `uniqBy` function**:
	* Pros: Optimized and tested for performance, provides a simple and concise API.
	* Cons: Introduces an external library dependency, which may impact performance or lead to additional overhead.

**Library Description**

In this benchmark, Lodash is used as the utility library for the `uniqBy` function. Lodash is a popular JavaScript library that provides a collection of reusable functions for various tasks, including array manipulation and filtering.

**Special JS Features/Syntax**

There are no specific JavaScript features or syntaxes being tested in this benchmark beyond the standard `Array.prototype.filter()` method and Lodash's `uniqBy` function.

**Other Alternatives**

If you were to implement a custom uniqueness filtering solution without relying on Lodash, you might consider using:

1. **`Set` data structure**: Using a `Set` to store unique values could provide an efficient way to filter out duplicates.
2. **`Map` data structure**: Similar to `Set`, a `Map` could be used to keep track of unique values and filter out duplicates.

Keep in mind that these alternatives might not be as optimized or concise as Lodash's `uniqBy` function, but they can provide an interesting alternative implementation perspective.

Related benchmarks:

Lodash uniqBy vs Javascript uniqBy with key (version: 0)

Comparing performance of: Native uniqueBy vs Lodash uniqBy

Created: 2 years ago by: Registered User

Jump to the latest result

Native uniqueBy

Lodash uniqBy

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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