Benchmark: Lodash flatten vs nativate flat (depth 1)

HTML Preparation code:

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

Tests:

Lodash flatten
const arr = [1,[2,3,4]]; _.flatten(arr);
Native flat
const arr = [1,[2,3,4]]; arr.flat();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash flatten
Native flat

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36

Browser/OS: Chrome 144 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Lodash flatten	20314402.0 Ops/sec
Native flat	8729444.0 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 JSON data and explain what's being tested on MeasureThat.net.

**Benchmark Definition**

The benchmark is comparing two approaches to flatten an array in JavaScript:

1. **Lodash `flatten`**: This method is part of the popular Lodash library, which provides a comprehensive set of utility functions for functional programming. The `flatten` function takes an array as input and returns a new array with all nested arrays flattened.
2. **Native `flat`**: This is a built-in method introduced in ECMAScript 2019 (ES2019) that allows you to flatten an array in a more concise way.

**Options Compared**

The benchmark compares the performance of these two approaches:

* Lodash `flatten`: A widely used and well-maintained library with a large user base.
* Native `flat`: A built-in method that is part of the ECMAScript standard, which means it's available in all modern JavaScript engines.

**Pros and Cons**

**Lodash `flatten`**

Pros:

* Widely supported and used, ensuring good performance and stability.
* Provides additional functionality beyond just flattening arrays (e.g., recursive flattening).
* Easy to use and understand, especially for developers familiar with functional programming.

Cons:

* Adds an external dependency (the Lodash library), which may increase bundle size and page load times.
* May not be as lightweight or efficient as the native `flat` method.

**Native `flat`**

Pros:

* Built-in and part of the ECMAScript standard, ensuring consistency across all modern JavaScript engines.
* Lightweight and efficient, with minimal overhead compared to Lodash's dependency.
* Concise syntax and easy to use.

Cons:

* Not all older browsers or environments may support this method (although most modern ones do).
* May not provide additional functionality beyond just flattening arrays.

**Library: Lodash**

Lodash is a popular JavaScript library that provides a comprehensive set of utility functions for functional programming, including the `flatten` method. It's widely used and well-maintained, making it a great choice for many developers.

**Special JS Feature/Syntax**

The benchmark doesn't explicitly mention any special JavaScript features or syntax. However, it does rely on the ECMAScript 2019 standard (ES2019) to utilize the `flat` method.

**Other Alternatives**

If you need alternative approaches to flatten arrays in JavaScript, consider:

* Using a different library, such as Ramda or Underscore.js.
* Implementing your own flattening function using recursion or iteration.
* Utilizing other methods, like `Array.prototype.concat()` or `Array.prototype.reduce()`, although these may be less efficient than the native `flat` method.

Keep in mind that performance and efficiency will vary depending on the specific use case and environment.

Related benchmarks:

Lodash flatten vs nativate flat (depth 1) (version: 0)

Comparing performance of: Lodash flatten vs Native flat

Created: 4 years ago by: Guest

Jump to the latest result

Lodash flatten

Native flat

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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