Benchmark: lodash flatmap vs Vanilla flatmap

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/lodash/4.16.0/lodash.min.js"></script>

Script Preparation code:

var x = [1,2,3]

Tests:

lodash.flatMap
_.flatMap(x,x => [x-1, x, x+1])
Vanilla js flatmap
x.flatMap(x => [x-1, x, x+1])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash.flatMap
Vanilla js flatmap

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 12 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36

Browser/OS: Chrome 126 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
lodash.flatMap	15160377.0 Ops/sec
Vanilla js flatmap	7076815.5 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is comparing two approaches to implementing `flatMap` in JavaScript: using the Lodash library and the vanilla JavaScript implementation.

**Lodash Library**

The Lodash library is a popular utility library for JavaScript that provides a collection of small, reusable functions for various tasks. In this case, the library is used to implement the `flatMap` function, which transforms each element of an array into an array of new values.

In the provided JSON, the library version being used is 4.16.0, which was available on the CDN at the time of benchmarking.

**Vanilla JavaScript Implementation**

The vanilla JavaScript implementation uses a simple recursive approach to implement `flatMap`. The idea is to iterate over each element in the input array and transform it into an array of new values using the provided callback function. The transformed arrays are then flattened together using the spread operator (`...`) or the `concat()` method.

**Comparison**

The benchmark is comparing the performance of both approaches:

* Lodash's `flatMap` implementation
* Vanilla JavaScript's recursive implementation

**Pros and Cons:**

1. **Lodash's `flatMap` implementation:**
	* Pros:
		+ Well-optimized for performance
		+ Reduces boilerplate code
	+ Easy to use and understand
	* Cons:
		+ Adds an additional dependency (the Lodash library)
		+ May not be suitable for projects that don't want to include an external library
2. **Vanilla JavaScript implementation:**
	* Pros:
		+ No dependencies or external libraries required
		+ Can be customized and optimized further if needed
		+ Learning opportunity to understand the inner workings of `flatMap`
	* Cons:
		+ Requires more boilerplate code
		+ May be less efficient due to recursive function calls

**Other Considerations:**

* **Async implementation:** If you need to support asynchronous implementations, the benchmark should also include comparisons with both synchronous and async approaches.
* **Edge cases:** Depending on the use case, you may want to consider edge cases such as empty arrays, null or undefined values, or large array sizes.

**Alternative Implementations:**

1. **Array.prototype.flatMap() method:** Some modern browsers and Node.js versions support a `flatMap()` method for the `Array` prototype. This implementation is likely to be optimized for performance.
2. **Iterators and generators:** Another alternative approach would be to use iterators and generators to implement `flatMap`. This could provide additional flexibility and optimizations.

In summary, the benchmark provides valuable insights into the performance differences between using a well-optimized library like Lodash's `flatMap` implementation versus implementing it yourself in vanilla JavaScript.

Related benchmarks:

lodash flatmap vs Vanilla flatmap (version: 0)

Comparing performance of: lodash.flatMap vs Vanilla js flatmap

Created: 3 years ago by: Guest

Jump to the latest result

lodash.flatMap

Vanilla js flatmap

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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