Benchmark: lodash flatmap vs Vanilla flatmapaa

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])
lodash.flatMapDeep
_.flatMapDeep(x,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
lodash.flatMapDeep

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/148.0.0.0 Safari/537.36

Browser/OS: Chrome 148 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
lodash.flatMap	27829982.0 Ops/sec
Vanilla js flatmap	11919844.0 Ops/sec
lodash.flatMapDeep	12149942.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Overview**

The provided JSON represents a benchmark test case on MeasureThat.net, which compares the performance of different approaches to implement the `flatMap` method in JavaScript.

**Benchmark Definition**

The benchmark definition consists of three test cases:

1. `_.flatMap(x,x => [x-1, x, x+1])`: This test case uses the Lodash library's `_flatMap` function.
2. `x.flatMap(x => [x-1, x, x+1])`: This test case implements the `flatMap` method using vanilla JavaScript.
3. `_.flatMapDeep(x,x => [x-1, x, x+1])`: This test case uses the Lodash library's `_flatMapDeep` function.

**Options Compared**

The benchmark compares three options:

* **Lodash**: The Lodash library provides a convenient implementation of the `flatMap` method, which is used in test cases 1 and 3.
* **Vanilla JavaScript**: Test case 2 implements the `flatMap` method using vanilla JavaScript, without relying on any external libraries.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

* **Lodash:**
	+ Pros:
		- Convenient and efficient implementation of `flatMap`.
		- Reduces boilerplate code.
	+ Cons:
		- Introduces an additional dependency (the Lodash library).
		- May not be optimized for performance, as it's wrapped around by the library.
* **Vanilla JavaScript:**
	+ Pros:
		- No dependencies or overhead.
		- Can be optimized and customized for specific use cases.
	+ Cons:
		- Requires more boilerplate code to implement the `flatMap` method.

**Library Explanation**

In test case 1, `_.flatMap(x,x => [x-1, x, x+1])`, the Lodash library is used. Specifically, it's the `_flatMap` function from the `lodash.array` module.

In test case 3, `_.flatMapDeep(x,x => [x-1, x, x+1])`, the Lodash library is again used, this time with the `_flatMapDeep` function from the `lodash.array` module. The difference between `_flatMap` and `_flatMapDeep` lies in how they handle nested arrays.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in these test cases. They rely on standard JavaScript functionality.

**Other Alternatives**

If you were to implement a different approach, some alternatives could include:

* Using `Array.prototype.map` and then slicing the resulting array to achieve the same effect as `flatMap`.
* Implementing a custom `flatMap` function using recursion or iteration.
* Using a library like jQuery's `map` method (although this would introduce additional dependencies).

Keep in mind that these alternatives might not be as efficient or convenient as the Lodash implementation, but they can provide insights into how different approaches work.

Related benchmarks:

lodash flatmap vs Vanilla flatmapaa (version: 0)

Comparing performance of: lodash.flatMap vs Vanilla js flatmap vs lodash.flatMapDeep

Created: 4 years ago by: Guest

Jump to the latest result

lodash.flatMap

Vanilla js flatmap

lodash.flatMapDeep

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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