Benchmark: native slice vs native reduce vs lodash chunk

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.15/lodash.min.js'></script>

Script Preparation code:

var test = Array.from({
    length: 100000
}, () => Math.random())

Tests:

native slice
const chunkSize = 80; const chunks = []; for (let i = 0; i <= test.length; i += chunkSize) { chunks.push(test.slice(i, i + chunkSize)); }
native reduce
const chunkSize = 80; test.reduce((accu, element, idx) => { if (idx % chunkSize === 0) { accu.push([]); } const chunk = accu[accu.length - 1]; chunk.push(element); return accu; }, []);
lodash chunk
_.chunk(test,80)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
native slice
native reduce
lodash chunk

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36

Browser/OS: Chrome 138 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
native slice	32156.1 Ops/sec
native reduce	1199.5 Ops/sec
lodash chunk	7691.9 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 benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is comparing three approaches to chunking an array:

1. **Native Slice**: Using the `slice()` method to extract chunks from the original array.
2. **Native Reduce**: Using the `reduce()` method with a custom callback function to create chunks.
3. **Lodash Chunk**: Using the `chunk()` method from the Lodash library to create chunks.

**Options Compared**

The three approaches are compared in terms of their performance, specifically:

* The number of executions per second
* The number of iterations required to complete each test case

**Pros and Cons**

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

1. **Native Slice**:
	* Pros: Fast, lightweight, and doesn't require any additional library.
	* Cons: May not be as efficient for large arrays or complex chunking scenarios.
2. **Native Reduce**:
	* Pros: Can handle complex chunking scenarios and is often more efficient than native slice.
	* Cons: Requires a custom callback function, which can add overhead.
3. **Lodash Chunk**:
	* Pros: Easy to use, efficient, and provides a flexible way to chunk arrays.
	* Cons: Requires including the Lodash library, which adds extra size.

**Library Used**

In this benchmark, the `lodash` library is used for its `chunk()` method. Lodash is a popular utility library that provides various functions for tasks like array manipulation, string processing, and more.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntaxes being tested in this benchmark. However, it's worth noting that the use of `reduce()` as a chunking approach might be considered an advanced JavaScript concept.

**Other Alternatives**

If you're looking for alternative approaches to chunking arrays, here are some options:

* **Array.prototype.slice()**: Similar to native slice, but may not be as efficient.
* **Array.prototype.reduce()**: Similar to native reduce, but requires a custom callback function.
* **Underscore.js**: Another popular utility library that provides a `chunk()` method similar to Lodash.
* **Native implementation**: Depending on the specific use case, you might be able to implement chunking using only native JavaScript methods, such as looping and array manipulation.

Related benchmarks:

native slice vs native reduce vs lodash chunk (version: 0)

native slice vs lodash chunk

Comparing performance of: native slice vs native reduce vs lodash chunk

Created: 3 years ago by: Guest

Jump to the latest result

native slice

native reduce

lodash chunk

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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