Benchmark: Test 2 different chunking approaches

Script Preparation code:

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

Tests:

test1
const chunkArray = (items, size) => { const groups = [] let i = 0 while (i < items.length) { groups.push(items.slice(i, (i += size))) } return groups } chunkArray(test, 25)
test2
const chunkArray2 = (items, size) => { items.reduce((chunks, val, index) => { const chunk = Math.floor(index / size) chunks[chunk] = ([]).concat(chunks[chunk] || [], val) return chunks }, []) } chunkArray2(test, 25)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
test1
test2

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 explanation of the provided JavaScript microbenchmark.

**Benchmark Overview**

The benchmark compares two different approaches to chunking an array: `chunkArray` and `chunkArray2`. Chunking is the process of dividing a large array into smaller, more manageable chunks.

**Approach 1: `chunkArray`**

This approach uses a traditional loop-based method to create chunks. It iterates through the array, slicing it into groups of a specified size (25 in this case) and adding each group to an array called `groups`. The function returns the `groups` array.

```javascript
const chunkArray = (items, size) => {
  const groups = []
  let i = 0
  while (i < items.length) {
    groups.push(items.slice(i, (i += size)))
  }
  return groups
}
```

Pros:

* Easy to understand and implement
* No external dependencies

Cons:

* May be slower due to the explicit loop
* May not scale well for very large arrays

**Approach 2: `chunkArray2`**

This approach uses the `reduce()` method to create chunks. It iterates through the array, assigning each element to a chunk based on its index divided by the chunk size (25 in this case). The resulting chunks are accumulated into an object called `chunks`.

```javascript
const chunkArray2 = (items, size) => {
  items.reduce((chunks, val, index) => {
    const chunk = Math.floor(index / size)
    chunks[chunk] = ([]).concat(chunks[chunk] || [], val)
    return chunks
  }, [])
}
```

Pros:

* May be faster due to the use of `reduce()`
* Can scale well for very large arrays

Cons:

* Requires a basic understanding of `reduce()` and object manipulation
* May have overhead due to the creation of an object

**Library: `Array.prototype.slice()`**

The `slice()` method is used in both approaches to extract groups from the original array. It's a built-in JavaScript method that returns a shallow copy of a portion of an array.

**Special JS Feature/Syntax: None**

There are no special features or syntaxes being tested in this benchmark.

**Other Alternatives**

If you wanted to implement chunking differently, some alternative approaches could include:

* Using `Array.prototype.every()` and `Array.prototype.findIndex()` to create chunks
* Utilizing a library like Lodash's `chunk` function
* Implementing a custom chunking algorithm using recursion or iteration

Keep in mind that the choice of approach depends on the specific use case, performance requirements, and personal preference.

**Benchmark Results**

The provided benchmark results show the execution time per second for each test case. The first test case (`test1`) takes approximately 7852 seconds per second, while the second test case (`test2`) takes approximately 52 seconds per second. This suggests that `chunkArray` is significantly slower than `chunkArray2`.

Related benchmarks:

Test 2 different chunking approaches (version: 0)

Comparing performance of: test1 vs test2

Created: 3 years ago by: Guest

Jump to the latest result

test1

test2

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):