Benchmark: chunk method - MeasureThat.net

Tests:

chunk1
const arr = [1, 2, 3, 4, 5]; const chunk = (arr = [], value = 0) => { const chunkedArr = []; let acc = []; for (let index = 0; index < arr.length; index += 1) { if ((index + 1) % value === 0 || index === arr.length - 1) { acc.push(arr[index]); chunkedArr.push([...acc]); acc = []; } else { acc.push(arr[index]); } } return chunkedArr; }; chunk(arr, 2);
chunk2
const arr = [1, 2, 3, 4, 5]; const chunk = (arr, chunkSize = 1, cache = []) => { const tmp = [...arr]; if (chunkSize <= 0) return cache; while (tmp.length) cache.push(tmp.splice(0, chunkSize)); return cache; }; chunk(arr, 2);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
chunk1
chunk2

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and its pros and cons.

**Benchmark Overview**

The benchmark measures the performance of two different chunking algorithms:

1. `chunk1`: This implementation uses a single loop to iterate over the array and chunks it every 2 elements.
2. `chunk2`: This implementation uses a while loop to repeatedly remove chunks of size 2 from the array.

**Comparison**

In `chunk1`, the algorithm uses a simple if-else statement to determine when to chunk the array, while in `chunk2`, it uses a while loop with conditional statements to achieve the same result. Both implementations aim to produce the same output: an array of chunks, where each chunk contains 2 elements.

**Pros and Cons**

**`chunk1`**

Pros:

* Easier to understand and implement, especially for developers without prior experience with chunking algorithms.
* Less memory usage, as it only creates a single temporary array (`acc`) to store the chunked elements.

Cons:

* May have performance issues if the input array is large, due to the repeated push operations on the `chunkedArr` array.
* Requires explicit loop control to handle edge cases (e.g., when the index reaches the end of the array).

**`chunk2`**

Pros:

* More efficient memory usage, as it uses a more compact data structure (`cache`) to store the chunked elements.
* Can handle large input arrays with less overhead.

Cons:

* More complex implementation, which may be harder for developers without prior experience with chunking algorithms.
* May have performance issues if the cache is not properly implemented or if the input array is very large.

**Library Usage**

Neither of the implementations uses a dedicated library to perform chunking. However, both use built-in JavaScript features like arrays and loops to implement the algorithm.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax used in these implementations. They only rely on standard JavaScript data structures and control flow constructs.

**Alternatives**

Other alternatives for chunking algorithms include:

1. Using libraries like Lodash or Ramda, which provide optimized implementation of chunking functions.
2. Implementing a custom chunking algorithm using a different approach (e.g., recursive function, queue-based processing).
3. Using parallel processing techniques, such as Web Workers or worker threads, to take advantage of multiple CPU cores.

Keep in mind that the choice of alternative implementation depends on the specific requirements and constraints of the project.

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36

Browser/OS: Chrome 79 on Mac OS X 10.15.1

View result in a separate tab

Test name	Executions per second
chunk1	5565137.0 Ops/sec
chunk2	4635903.5 Ops/sec

Related benchmarks:

chunk method (version: 0)

chuck testing

Comparing performance of: chunk1 vs chunk2

Created: 4 years ago by: Guest

Jump to the latest result

chunk1

chunk2

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):