Benchmark: Chunk - lodash vs javascript vs for loop vs reduce

HTML Preparation code:

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

Script Preparation code:

var values = new Array(200 * 200 * 4)

Tests:

lodash
var chunks = _.chunk(values, 4)
native new
const chunk = (input, size) => { return input.reduce((arr, item, idx) => { if (idx % size === 0) { arr.push([item]); } else { arr[arr.length - 1].push(item); } return arr; }, []); } var chunks = chunk(values, 4)
native old
const chunk = (input, size) => { return input.reduce((arr, item, idx) => { return idx % size === 0 ? [...arr, [item]] : [...arr.slice(0, -1), [...arr.slice(-1)[0], item]]; }, []); } var chunks = chunk(values, 4)
native for loop
const chunk = (input, size) => { const arr = []; for (let i = 0; i < input.length; i += size) { arr.push(input.slice(i, i + size)); } return arr; }; var chunks = chunk(values, 4)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash
native new
native old
native for loop

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36

Browser/OS: Chrome 136 on Linux

View result in a separate tab

native old

2K/s

native new

2K/s

lodash

760/s

native for loop

500/s

View exact numbers

Test name	Executions per second
✓ native old	1,955 Ops/sec
native new	1,622 Ops/sec
lodash	760 Ops/sec
native for loop	500 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its test cases.

**Benchmark Overview**

The benchmark measures the performance of three different approaches to chunking an array: using Lodash (`lodash`), a custom `native new` function, and a custom `native for loop` function. The benchmark tests how fast each approach can divide an array of 200x200 elements into chunks of size 4.

**Test Cases**

1. **Lodash**: The test case uses the Lodash library to chunk the array. Lodash is a utility library that provides a lot of helper functions for common tasks, such as string manipulation, array manipulation, and more.
2. **Native New**: This test case defines a custom function `chunk` that takes an input array and a size as arguments. It uses the `reduce` method to create chunks of the specified size. The `native new` approach is likely optimized for performance, but its implementation details are not shown in the benchmark.
3. **Native Old**: This test case defines another custom function `chunk` that takes an input array and a size as arguments. However, this implementation uses a different logic to create chunks, which may be less efficient than the `native new` approach.

**Options Compared**

The three test cases compare different approaches to chunking an array:

* **Lodash**: Uses Lodash's built-in `chunk` function, which is likely optimized for performance.
* **Native New**: A custom implementation that uses the `reduce` method to create chunks. This approach may be more efficient than using a library function.
* **Native Old**: Another custom implementation that uses a different logic to create chunks.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Lodash**: Pros: Easy to use, optimized for performance. Cons: Additional dependency on Lodash.
* **Native New**: Pros: Customizable, potentially more efficient than using a library function. Cons: Requires manual implementation of chunking logic.
* **Native Old**: Pros: Another custom implementation option. Cons: Less efficient than `native new`, likely less efficient than Lodash.

**Other Considerations**

When choosing an approach to chunking an array, consider the following factors:

* Performance: How fast do you need to process the data?
* Complexity: How complex is your application? If you're working with large datasets or performance-critical code, a custom implementation may be necessary.
* Maintenance: How easy is it to maintain and update your code?

**Alternatives**

If you don't want to use Lodash or implement your own chunking logic, consider the following alternatives:

* Use a different library or framework that provides an efficient chunking function, such as `chunk` in React.
* Use a streaming or parallel processing approach to process large datasets.
* Optimize your code for performance using techniques like caching, memoization, or just-in-time (JIT) compilation.

Keep in mind that the best approach depends on your specific use case and requirements.

Chunk - lodash vs javascript vs for loop vs reduce (version: 0)

Comparing performance of: `lodash vs native new vs native old vs native for loop`

Created: 3 years ago by: Guest

Go to the latest result

lodash

native new

native old

native for loop

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Chunk - lodash vs javascript vs for loop vs reduce (version: 0)

Comparing performance of: lodash vs native new vs native old vs native for loop

Created: 3 years ago by: Guest

Go to the latest result

lodash

native new

native old

native for loop

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `lodash vs native new vs native old vs native for loop`