Benchmark: Lodash partition VS native reduce

HTML Preparation code:

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

Script Preparation code:

var arr = [];

for (i = 0; i < 1000; i++) {
  arr[i] = i;
}

Tests:

Lodash
var [met, rest] = _.partition(arr, function(i) { return i % 2 === 0 });
Native reduce
var [met, rest] = arr.reduce(function([p1, p2], i) { return i % 2 === 0 ? [[...p1, i], p2] : [p1, [...p2, i]] }, [[], []])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Native reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 6 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36

Browser/OS: Chrome 141 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Lodash	240499.2 Ops/sec
Native reduce	3738.2 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!

**Benchmark Overview**

The provided JSON represents two test cases for measuring the performance of partitioning an array in JavaScript. The first test case uses Lodash, a popular utility library for functional programming tasks, while the second test case uses native JavaScript methods.

**Options Compared**

In this benchmark, we're comparing the performance of:

1. **Lodash's `partition` method**: This function takes two arguments: the input array and a callback function that returns a boolean value indicating whether an element should be included in the first partition or not.
2. **Native JavaScript's `reduce` method**: This method applies a reduction function to each element of an array, accumulating a value across all elements.

**Pros and Cons**

### Lodash's `partition`

Pros:

* Provides a convenient and readable way to separate elements into two partitions based on a simple condition.
* Abstracts away the underlying implementation details, making it easier to write maintainable code.

Cons:

* Introduces an additional dependency (Lodash) that may impact performance in some cases.
* Can be slower due to the overhead of calling a external function.

### Native JavaScript's `reduce`

Pros:

* Eliminates the need for an external library, reducing dependencies and potential performance impacts.
* Can be faster since it doesn't involve calling a separate function.

Cons:

* Requires more code to achieve the same result, making it less readable for some developers.
* May have performance issues if not implemented correctly (e.g., using `forEach` instead of `reduce`).

**Library: Lodash**

Lodash is a utility library that provides a wide range of functional programming helpers. The `partition` method is one of these helpers, allowing you to split an array into two partitions based on a condition. In this benchmark, Lodash's `partition` method is used to divide the input array into two parts: elements where the index is even and elements where it's odd.

**Special JS Feature/Syntax**

In this benchmark, we're using the **ES6 destructuring assignment** syntax (`var [met, rest] = ...;`) to extract the first element of the reduced array from the `reduce` method. This syntax was introduced in ECMAScript 2015 (ES6) and allows you to destructure arrays into separate variables.

**Other Alternatives**

If you're looking for alternative ways to partition an array, consider using:

* **Array.prototype.filter()**: You can use `filter()` to create a new array with elements that pass a test.
* **Array.prototype.forEach()**: While not as elegant as `reduce`, you can use `forEach()` in combination with `map()` or another function to achieve similar results.

Keep in mind that these alternatives may have different performance characteristics and may require more code.

Related benchmarks:

Lodash partition VS native reduce (version: 0)

Comparing performance of: Lodash vs Native reduce

Created: 6 years ago by: Guest

Jump to the latest result

Lodash

Native reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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