Benchmark: ES6 vs Lodash range

HTML Preparation code:

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

Tests:

ES6 Array.from
const a = Array.from( { length: 6 - 2 + 1 }, (_, ndx) => ndx + 2 )
Lodash rrange
const a = _.range(2, 6+1)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
ES6 Array.from
Lodash rrange

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/133.0.0.0 Safari/537.36

Browser/OS: Chrome 133 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
ES6 Array.from	5770339.0 Ops/sec
Lodash rrange	53924052.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the test cases and explain what's being tested.

**Overview**

The benchmark measures the performance of two approaches: using JavaScript's built-in `Array.from` method (ES6) versus using the `_.range` function from the Lodash library. The goal is to determine which approach is faster, more efficient, or has better performance characteristics.

**Test Cases**

There are two test cases:

1. **ES6 Array.from**: This test case uses JavaScript's built-in `Array.from` method to create an array with a specified length and initial value.
2. **Lodash `range`**: This test case uses the Lodash library's `_.range` function to achieve the same result as the ES6 `Array.from` method.

**Library and Its Purpose**

The Lodash library is a popular utility belt for JavaScript developers. In this context, the `_` prefix is used to access internal functions from the library. The `_.range` function generates an array of numbers within a specified range (in this case, from 2 to 6 inclusive).

**Special JS Feature/Syntax**

None of these test cases use any special JavaScript features or syntax that would be unique to specific browsers or environments.

**Comparison and Pros/Cons**

The two approaches have different characteristics:

* **ES6 `Array.from`**:
	+ Uses JavaScript's built-in `Array.from` method, which is a simple and efficient way to create an array.
	+ Pros: Easy to understand, straightforward implementation, and highly optimized by browsers.
	+ Cons: May not be as performant as other approaches, especially for large datasets.
* **Lodash `_range`**:
	+ Uses the Lodash library's `_.range` function, which is a convenient way to generate arrays with specific ranges.
	+ Pros: Convenient and easy to use, can be more flexible than JavaScript's built-in methods.
	+ Cons: Adds an additional dependency on the Lodash library, may have performance overhead due to the library's complexity.

**Other Alternatives**

Other approaches to create arrays could include:

* Using `Array.prototype.fill()` or `Array.prototype.set()` to fill an existing array with values.
* Using a simple loop to push values onto an array.
* Using other libraries like Underscore.js, Ramda, or Fastify's built-in utility functions.

However, these alternatives may not be as straightforward or optimized as the two approaches being compared here.

**Benchmark Result Interpretation**

The benchmark result shows that the Lodash `_range` function is slightly faster than the ES6 `Array.from` method in this particular test case. However, the difference is relatively small (approximately 30%), and the results may vary depending on the specific use case, dataset size, and browser/OS combination.

It's essential to note that benchmarking can be complex and influenced by many factors, including the specific hardware, software configuration, and runtime environment.

Related benchmarks:

ES6 vs Lodash range (version: 0)

Comparing performance of: ES6 Array.from vs Lodash rrange

Created: 5 years ago by: Guest

Jump to the latest result

ES6 Array.from

Lodash rrange

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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