Benchmark: native-chunk-vs-lodash-chunk KE1

HTML Preparation code:

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

Script Preparation code:

var test = Array.from({
    length: 100000
}, () => Math.random())

function nativeChunk (arr, size) {
  const chunks = [];
  for (let i = 0; i < arr.length; i += size) {
    chunks.push(arr.slice(i, i + size))
  }
  return chunks;
}

Tests:

Native chunk
nativeChunk(test,5000)
lodash chunk
_.chunk(test,5000)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native chunk
lodash chunk

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Mobile Safari/537.36

Browser/OS: Chrome Mobile 116 on Android

View result in a separate tab

Test name	Executions per second
Native chunk	19907.9 Ops/sec
lodash chunk	4218.7 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmarking setup and explain what's being tested, compared, and analyzed.

**Overview of the Benchmark**

The benchmark measures the performance difference between two approaches to chunk an array: a native JavaScript implementation (`nativeChunk`) and a version using the popular `lodash` library (`_.chunk`). The test case uses a random array of 100,000 elements, which is split into chunks of size 5,000.

**Library and Its Purpose**

The `lodash` library is a utility-first JavaScript library that provides a wide range of functions for tasks such as functional programming, data manipulation, and more. In this benchmark, `_.chunk` is used to chunk the array into smaller sub-arrays. `_chunk` is a high-order function that takes an array and a size parameter, returning an array of arrays with the original elements grouped into consecutive chunks.

**Native JavaScript Implementation (`nativeChunk`)**

The native implementation `nativeChunk` manually creates an array of chunks by iterating over the input array and using slicing to extract sub-arrays of the specified size. This approach relies on the browser's engine and its internal array manipulation mechanisms, which can be optimized for specific use cases.

**Comparison of Options**

Two options are being compared:

1. **Native JavaScript Implementation (`nativeChunk`)**: This approach uses native JavaScript features and relies on the browser's engine to perform the chunking operation.
2. **Lodash Library (`_.chunk`)**: This approach uses a separate library (`lodash`) to perform the chunking operation.

**Pros and Cons of Each Approach**

* **Native JavaScript Implementation (`nativeChunk`)**:
	+ Pros:
		- Fast and lightweight, as it relies on native JavaScript features.
		- Optimized for specific use cases by the browser's engine.
	+ Cons:
		- May require more code and complexity to implement.
		- Limited by the browser's engine and its internal array manipulation mechanisms.
* **Lodash Library (`_.chunk`)**:
	+ Pros:
		- High-level abstraction, making it easier to understand and maintain.
		- Wide range of functionality provided by the library.
	+ Cons:
		- Adds an external dependency (the `lodash` library).
		- May introduce overhead due to the additional layer of indirection.

**Special JavaScript Features or Syntax**

None are explicitly mentioned in this benchmark.

**Other Alternatives**

If the native implementation is not suitable, other alternatives could be considered:

* **Array.prototype.chunk()**: Some modern browsers support a `chunk()` method on the `Array.prototype`, which can be used as an alternative to the native implementation.
* **Custom array manipulation libraries**: Other libraries or frameworks that provide optimized array manipulation functions, such as `ramda` or `immutable.js`.
* **Just-In-Time (JIT) compilers**: In some cases, using a JIT compiler like V8 (used by Chrome) can improve performance of certain operations.

Keep in mind that the choice of implementation depends on the specific use case and requirements. The native implementation may be the best choice when performance is critical and the overhead of an external library is unacceptable.

Related benchmarks:

native-chunk-vs-lodash-chunk KE1 (version: 0)

Comparing performance of: Native chunk vs lodash chunk

Created: one year ago by: Guest

Jump to the latest result

Native chunk

lodash chunk

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):