Benchmark: EGO set vs lodash vs underscore

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/underscore@1.13.1/underscore-min.js"></script>

Script Preparation code:

var underscore = _.noConflict();
var array = [1, 1, 2, 2, 2, 2, 5, 5, 1, 1, 2, 2, 2, 2, 5, 5, 1, 1, 2, 2, 2, 2, 5, 5, 1, 1, 2, 2, 2, 2, 5, 5, 1, 1, 2, 2, 2, 2, 5, 5, 1, 1, 2, 2, 2, 2, 5, 5, 1, 1, 2, 2, 2, 2, 5, 5, 1, 1, 2, 2, 2, 2, 5, 5]

Tests:

Set
var set = new Set(array); var uniqArraySet = [...set];
Lodash
var uniqArrayLodash = _.uniq(array);
Underscore
var uniqArrayUnderscore = underscore.uniq(array);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Set
Lodash
Underscore

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

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Let's dive into the world of MeasureThat.net and explore what's being tested in this benchmark.

The provided JSON represents a comparison between three JavaScript libraries: EGO Set, Lodash, and Underscore. The purpose of this benchmark is to measure how fast each library can create an array with unique elements from a given input array.

**What are the options being compared?**

1. **Ego Set**: A simple set data structure implementation.
2. **Lodash**: A popular utility library that provides a `uniq` function for removing duplicates from arrays.
3. **Underscore**: Another widely-used utility library that provides an `uniq` function for removing duplicates from arrays.

**Pros and Cons of each approach:**

1. **Ego Set**:
	* Pros: Simple, efficient, and lightweight implementation of a set data structure.
	* Cons: May not be as performant as specialized libraries like Lodash or Underscore, especially for large datasets.
2. **Lodash**:
	* Pros: Well-tested, widely-used library with a strong community backing.
	* Cons: Adds overhead due to its extensive feature set and dependencies.
3. **Underscore**:
	* Pros: Similar to Lodash, well-established library with a dedicated user base.
	* Cons: Also adds some overhead due to its features and dependencies.

In general, Ego Set is likely the fastest option because it's a simple, lightweight implementation of a set data structure. However, Lodash and Underscore are well-tested and widely-used libraries that may provide better performance for certain use cases.

**Library usage:**

* `_.uniq(array)`: Lodash uses its own implementation of uniqueness, which likely includes some overhead due to the library's features.
* `underscore.uniq(array)`: Underscore also has its own implementation of uniqueness, similar to Lodash.
* `var set = new Set(array);`: Ego Set creates a plain JavaScript Set object, which is a lightweight and efficient data structure.

**Special JS feature or syntax:**

There are no specific special features or syntax used in this benchmark. It's purely focused on comparing the performance of different library implementations.

**Other alternatives:**

* For creating unique arrays, other libraries like **Ramda** (a functional programming library) or **Papaparse** (a data parser and processor) might also be considered.
* For simple set operations, libraries like **JS-Set** (a lightweight set implementation) or **fast-set** (a fast and efficient set implementation) could be alternatives.

In summary, this benchmark compares the performance of Ego Set, Lodash, and Underscore in creating unique arrays from input data. While Ego Set is likely the fastest option due to its simplicity, Lodash and Underscore are well-established libraries with dedicated user bases that may provide better performance for certain use cases.

Related benchmarks:

EGO set vs lodash vs underscore (version: 0)

Comparing performance of: Set vs Lodash vs Underscore

Created: 4 years ago by: Registered User

Jump to the latest result

Set

Lodash

Underscore

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):