Benchmark: Lodash Chunk vs Native Reduce v2

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.21/lodash.min.js'></script>

Script Preparation code:

var arrays = [[1, 2, 3, 4, 5], [5, 2, 10]];

function chunk(input, size) {
  
  const length= input.length;
  const chunksLenght= length/size;
  
  if(chunksLenght<0){
    return input;
  }
  
  const chunks=[];
  
  let index = 0;
  
  for(let i =0 ;i < chunksLenght ; i++){
    chunks.push(input.slice(index , index + size));
    index += size;
  }
  
  // the remainig 
  if(index < length){
     chunks.push(input.slice(index , length));
  }
  
  
  return chunks;
};

Tests:

Lodash
_.chunk(['a', 'b', 'c', 'd'], 3);
Native
chunk(['a', 'b', 'c', 'd'], 3);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Native

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the benchmark.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark, specifically comparing two approaches to chunking arrays: Lodash's `_.chunk` function and a native implementation using a custom `chunk` function.

**Options Compared**

Two options are compared:

1. **Lodash's `_.chunk` function**: This is a widely-used utility function in the Lodash library that takes an array and a size as input, splitting the array into chunks of the specified size.
2. **Native implementation using custom `chunk` function**: The native implementation uses a simple loop to split the array into chunks.

**Pros and Cons**

Here's a brief overview of each approach:

* **Lodash's `_chunk` function**:
	+ Pros:
		- Well-maintained and widely-used library.
		- Provides additional functionality, such as handling edge cases (e.g., when the input array has fewer elements than the chunk size).
	+ Cons:
		- Adds overhead due to the use of a separate library.
		- May not be optimized for performance.
* **Native implementation using custom `chunk` function**:
	+ Pros:
		- Optimized for performance, as it doesn't require additional libraries or overhead.
		- Customizable and flexible, allowing for fine-grained control over chunking behavior.
	+ Cons:
		- Requires manual implementation and maintenance of the chunking logic.
		- May not handle edge cases as well as Lodash's implementation.

**Library: Lodash**

Lodash is a popular JavaScript library that provides various utility functions, including `_.chunk`, for working with arrays and other data structures. Its purpose is to simplify common tasks and provide a consistent interface across different browsers and environments.

**Special JS Feature/Syntax**

There are no special JS features or syntax used in this benchmark. Both implementations use standard JavaScript features, such as arrays and loops.

**Benchmark Results**

The latest benchmark results show that the native implementation using custom `chunk` function outperforms Lodash's `_chunk` function on the specific test case provided. This suggests that the custom implementation is more efficient than the library-based approach.

**Other Alternatives**

If you need to chunk arrays, other alternatives to consider include:

* **Array.prototype.slice()**: A built-in JavaScript method for splitting an array into chunks.
* **Underscore.js**: Another popular utility library that provides a `chunk` function similar to Lodash's implementation.
* **Custom implementations using specialized libraries**: Libraries like Ramda or Array.prototype.split() may provide optimized chunking functions, but may also introduce additional overhead.

Keep in mind that the choice of implementation ultimately depends on your specific requirements, performance needs, and personal preference.

Related benchmarks:

Lodash Chunk vs Native Reduce v2 (version: 0)

Comparing performance of: Lodash vs Native

Created: 3 years ago by: Guest

Jump to the latest result

Lodash

Native

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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