Benchmark: Lodash chunk vs Native chunk algorithm

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/g/lodash@4(lodash.min.js+lodash.fp.min.js)"></script>

Script Preparation code:

var array = Array(100000)

function nativeChunk (array) {
  let newArray = []
  let newSubArray = []
    
  for (element of array){
   	if (newSubArray.length === 10) {
      newArray.push(newSubArray)
      newSubArray = []
    }
    newSubArray.push(element)
  }
  return newArray
}

Tests:

lodash chunk method
_.chunk(array, 10);
my native chunk algorithm
nativeChunk(array)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash chunk method
my native chunk algorithm

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36

Browser/OS: Chrome 111 on Windows

View result in a separate tab

Test name	Executions per second
lodash chunk method	2089.3 Ops/sec
my native chunk algorithm	41.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**What is being tested?**

The benchmark measures the performance difference between two approaches to chunking an array:

1. Lodash's `chunk` method, which takes an array and a size as input and returns an array of sub-arrays.
2. A custom native chunk algorithm implemented by the test user.

**Options compared:**

The benchmark compares two options for chunking an array:

* **Lodash's `chunk` method**: This is a built-in utility function in Lodash that takes an array and a size as input and returns an array of sub-arrays.
* **Custom native chunk algorithm**: This is the custom implementation provided by the test user.

**Pros and Cons:**

* **Lodash's `chunk` method**:
	+ Pros:
		- Wide adoption and testing, which can lead to more reliable results.
		- Well-tested and optimized for performance.
	+ Cons:
		- May have a higher overhead due to the use of a library.
		- May not be optimized for specific use cases or hardware.
* **Custom native chunk algorithm**:
	+ Pros:
		- Can be optimized for specific use cases, hardware, or performance requirements.
		- Does not introduce additional overhead from a library.
	+ Cons:
		- May require more development effort to implement and test.
		- Results may vary depending on the implementation and testing environment.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, string manipulation, and functional programming. The `chunk` method in this benchmark is part of the Lodash library.

The `chunk` method takes two arguments:

* An array to chunk.
* A size to divide the array into chunks.

It returns an array of sub-arrays, where each sub-array has the specified number of elements.

**Special JS feature or syntax**

There is no specific JavaScript feature or syntax mentioned in this benchmark. The focus is on comparing the performance of two different approaches to chunking an array.

**Other alternatives**

If you're looking for alternative ways to chunk arrays, you may consider using other libraries or frameworks that provide similar functionality, such as:

* Array.prototype.chunk() (ES6+): This method provides a built-in way to chunk arrays, but its performance and features may vary depending on the browser or JavaScript engine.
* Other utility libraries like Underscore.js or Ramda.js.

For custom implementations, you can explore other optimization techniques or algorithms for chunking arrays, such as:

* Using SIMD instructions (e.g., for CPU-based chunking).
* Leveraging GPU acceleration (e.g., using WebGL or CUDA).
* Employing data structures like segment trees or k-d trees.

Related benchmarks:

Lodash chunk vs Native chunk algorithm (version: 0)

lodash chunk vs native chunk

Comparing performance of: lodash chunk method vs my native chunk algorithm

Created: one year ago by: Registered User

Jump to the latest result

lodash chunk method

my native chunk algorithm

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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