Benchmark: Lodash Union vs Spread with smaller arrays

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.20/lodash.min.js'></script>

Script Preparation code:

var arr1 = new Array(1000).fill(0);
var arr2 = new Array(1000).fill(1);

Tests:

Spread Operator
[...arr1, arr2]
Lodash union
_.union(arr1, arr2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread Operator
Lodash union

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Spread Operator	429701.9 Ops/sec
Lodash union	33654.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested, compared, and considered.

**Benchmark Overview**

The benchmark is designed to compare two approaches: using the spread operator (`[...arr1, arr2]`) versus using Lodash's `union` function. The test is creating two arrays, `arr1` and `arr2`, each with 1000 elements filled with consecutive integers starting from 0 and 1, respectively.

**Options Compared**

The benchmark is comparing the performance of two options:

1. **Spread Operator (`[...arr1, arr2]`)**: This approach uses the spread operator to concatenate the two arrays.
2. **Lodash Union Function (`_.union(arr1, arr2)`)**: This approach uses Lodash's `union` function to combine the two arrays.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Spread Operator**:
	+ Pros:
		- Simple and concise syntax.
		- Fast execution, as it only requires a single array concatenation operation.
	+ Cons:
		- May incur additional overhead due to the creation of intermediate arrays during concatenation.
		- Can be less readable for developers unfamiliar with the spread operator.
* **Lodash Union Function**:
	+ Pros:
		- Provides a more explicit and maintainable way of combining arrays.
		- May optimize under the hood, depending on the implementation details.
	+ Cons:
		- Requires an additional dependency on Lodash (the `lodash.min.js` script is included in the HTML preparation code).
		- May introduce additional overhead due to function call and potential optimization limitations.

**Other Considerations**

* **Array Length**: The benchmark creates two arrays with 1000 elements, which may not be representative of real-world scenarios where array lengths vary.
* **Integer Values**: Both arrays are filled with consecutive integers, which might not reflect the typical use cases for combining arrays (e.g., strings or objects).
* **Browser and Environment**: The benchmark is executed on a specific browser and device platform, which may not accurately represent other environments or devices.

**Library and Special JS Features**

The test case uses Lodash's `union` function from the included `lodash.min.js` script. No special JavaScript features or syntax are mentioned in this benchmark.

**Alternatives**

Other alternatives for combining arrays could be considered:

* Using the built-in `concat()` method.
* Utilizing modern JavaScript features like `Array.prototype.flat()` and `Array.prototype.flatMap()`.
* Implementing a custom function to combine arrays, potentially using more efficient algorithms or data structures (e.g., linked lists).

It's worth noting that this benchmark is likely designed to test the performance of specific libraries or implementations rather than general array concatenation techniques.

Related benchmarks:

Lodash Union vs Spread with smaller arrays (version: 0)

Comparing performance of: Spread Operator vs Lodash union

Created: 3 years ago by: Guest

Jump to the latest result

Spread Operator

Lodash union

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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