Benchmark: native-slice-vs-chunk-real

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());

var chunk = function(arr, chunkSize) {
    const res = [];
    for (let i = 0; i < arr.length; i += chunkSize) {
        const chunk = arr.slice(i, i + chunkSize);
        res.push(chunk);
    }
    return res;
}

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native slice
lodash chunk

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 break down the provided benchmark definition and test cases to understand what is being tested.

**Benchmark Definition JSON**

The benchmark definition represents two microbenchmarks: `native-slice-vs-chunk-real`. This benchmark compares the performance of two approaches:

1. **Native Slice**: A custom implementation in JavaScript that uses the built-in `slice()` method to chunk the array.
2. **Lodash Chunk**: The `chunk()` function from the popular JavaScript utility library, Lodash.

**Script Preparation Code**

The script preparation code creates a large array of random numbers with 100,000 elements using `Array.from()`. This array will be used for both benchmark tests.

The custom implementation (native slice) defines a `chunk()` function that takes an array and a chunk size as arguments. It then iterates over the array in chunks of the specified size, creating a new array for each chunk.

Lodash's `chunk()` function is imported from the `lodash.min.js` file, which is included via a CDN link.

**Html Preparation Code**

The HTML preparation code includes a script tag that loads Lodash's `lodash.min.js` file. This ensures that the Lodash library is available for use in the benchmark tests.

**Individual Test Cases**

There are two test cases:

1. **Native Slice**: This test case executes the custom implementation of the `chunk()` function on the prepared array, using a chunk size of 5,000.
2. **Lodash Chunk**: This test case executes Lodash's `chunk()` function on the same prepared array, using a chunk size of 5,000.

**Pros and Cons of Approaches**

1. **Native Slice**:
	* Pros: Likely to be highly optimized for performance, as it is implemented in native JavaScript.
	* Cons: May not be as efficient as other libraries due to its custom implementation.
2. **Lodash Chunk**:
	* Pros: Part of a widely-used and well-tested library, which may lead to better performance and reliability.
	* Cons: Requires an additional dependency (Lodash) and may introduce overhead.

**Other Considerations**

The benchmark results are displayed in terms of the number of executions per second (ExecutionsPerSecond). This metric is commonly used to measure performance. The results will likely show that the native slice implementation outperforms Lodash's `chunk()` function, but this can vary depending on the specific hardware and browser configuration.

**Special JavaScript Features or Syntax**

There are no special JavaScript features or syntax used in these benchmark tests.

**Alternatives**

If you were to rewrite or modify this benchmark test, you might consider:

* Using different chunk sizes or array sizes to observe how performance changes.
* Adding more browsers or device platforms to the test.
* Comparing other libraries or implementations for chunking arrays (e.g., `Array.prototype.slice()`).
* Implementing a different algorithm for chunking arrays (e.g., using recursion).

Related benchmarks:

native-slice-vs-chunk-real (version: 0)

Comparing performance of: Native slice vs lodash chunk

Created: 3 years ago by: Guest

Jump to the latest result

Native slice

lodash chunk

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):