Benchmark: Lodash isEqual test222333

HTML Preparation code:

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

Script Preparation code:


window.foo1 = new Set(_.range(1000));
window.bar1 = new Set(_.range(1000));
window.isSetsEqual = (a, b) => a.size === b.size && [...a].every(value => b.has(value));

Tests:

lodash
_.isEqual(window.foo1, window.bar1)
manual
window.isSetsEqual(window.foo1, window.bar1);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash
manual

Fastest: N/A

Slowest: N/A

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

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Let's break down the provided JSON to understand what is being tested and the pros/cons of each approach.

**Benchmark Definition**

The benchmark consists of two test cases: one using Lodash (`_isEqual` function) and another implementing it manually (`window.isSetsEqual` function).

**Lodash Implementation**

* **Purpose**: The `_isEqual` function from Lodash checks whether two objects are equal. In this case, it's used to compare two sets (`window.foo1` and `window.bar1`) created using the `_.range` function.
* **Library**: Lodash is a popular JavaScript library that provides a set of functions for functional programming tasks. The `_isEqual` function is part of its utility module.

**Manual Implementation**

* **Purpose**: This implementation also checks whether two sets are equal, but it does so manually without using any external libraries like Lodash.
* **Pros**:
	+ No additional dependencies: Since this implementation doesn't rely on an external library, it's a good option if you want to test performance without introducing extra dependencies.
	+ Control over optimization: By implementing the comparison algorithm yourself, you can optimize it for specific use cases or platforms.
* **Cons**:
	+ Additional code overhead: Implementing the comparison manually requires more code than using an existing library like Lodash.
	+ Potential performance issues: Without proper optimization, manual implementations might be slower than optimized libraries.

**Comparison of Approaches**

Both approaches compare two sets for equality. However, the Lodash implementation is likely to be faster and more efficient due to its:

* **Optimized algorithms**: Lodash's `_isEqual` function is designed to handle various data structures and edge cases efficiently.
* **Library optimizations**: As a mature library, Lodash has been optimized for performance by developers who have studied common pitfalls and implemented workarounds.

In contrast, the manual implementation might be slower due to:

* **Code overhead**: Additional code lines can lead to slower execution times, especially if not optimized carefully.
* **Lack of expertise**: Without knowledge of optimization techniques or common pitfalls in set comparison algorithms, the manual implementation might not achieve the same level of performance as Lodash.

**Other Alternatives**

If you're interested in testing other approaches or alternatives, here are a few options:

1. **Built-in JavaScript methods**: Depending on the browser and platform, you can use built-in methods like `Set.prototype.size` and `Array.prototype.includes()` to compare sets.
2. **Custom implementations**: You could implement your own set comparison algorithm using bitwise operations or other techniques specific to your needs.
3. **Other libraries**: There are other JavaScript libraries that provide set comparison functionality, such as the `lodash-es` version (which is a subset of Lodash) or specialized libraries like `fast-set`.

Keep in mind that each alternative will have its pros and cons, which you'll need to consider based on your specific use case and requirements.

Related benchmarks:

Lodash isEqual test222333 (version: 0)

Test on isEqual performance

Comparing performance of: lodash vs manual

Created: 5 years ago by: Guest

Jump to the latest result

lodash

manual

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):