Benchmark: chunk list of objects

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
}, () => ({ key1: 'a', key2: 'b' }))

Tests:

Native slice
test.slice(0,25)
lodash chunk
_.chunk(test,25)
Access values with native
test.slice(0,25)[1]
Access values with lodash
_.chunk(test,25)[1]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native slice
lodash chunk
Access values with native
Access values with lodash

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36

Browser/OS: Chrome 91 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Native slice	10058798.0 Ops/sec
lodash chunk	3278.8 Ops/sec
Access values with native	9626145.0 Ops/sec
Access values with lodash	3296.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

**Benchmark Overview**

The provided JSON represents a benchmark test case created on MeasureThat.net, a website that allows users to create and run JavaScript microbenchmarks. The goal is to compare the performance of different approaches when dealing with chunking a large list of objects.

**Script Preparation Code**

The script preparation code is:
```javascript
var test = Array.from({ length: 100000 }, () => ({ key1: 'a', key2: 'b' }));
```
This creates an array `test` containing 100,000 objects with two properties each. This large dataset will be used to benchmark different chunking approaches.

**Html Preparation Code**

The HTML preparation code includes a script tag that loads the Lodash library version 4.17.5:
```html
<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>
```
Lodash is a popular JavaScript utility library that provides various functions for tasks like array manipulation, string manipulation, and more.

**Benchmark Test Cases**

There are four test cases:

1. **Native slice**: `test.slice(0,25)`
This test case uses the native JavaScript `slice()` method to extract a chunk of 25 elements from the `test` array.
2. **Lodash chunk**: `_chunk(test,25)`
This test case uses the Lodash `_chunk()` function to split the `test` array into chunks of 25 elements.
3. **Access values with native**: `test.slice(0,25)[1]`
This test case extracts a value from the first chunk of 25 elements using the native JavaScript syntax.
4. **Access values with Lodash**: `_chunk(test,25)[1]`
This test case accesses the second element of the first chunk using the Lodash `_chunk()` function.

**Library: Lodash**

Lodash is a utility library that provides various functions for tasks like array manipulation, string manipulation, and more. In this benchmark, it's used to implement the `_chunk()` function, which splits an array into chunks of a specified size.

**Special JavaScript Features/Syntax**

None mentioned in this benchmark.

**Other Considerations**

When dealing with large datasets and performance-critical code, microbenchmarks like this one can help identify optimization opportunities. However, it's essential to note that the results may not generalize to real-world scenarios or edge cases.

In addition to these test cases, you might also consider including other approaches, such as:

* Using a custom implementation of chunking
* Utilizing parallel processing or concurrency
* Experimenting with different array data structures (e.g., linked lists)

**Alternatives**

Some alternative approaches for chunking large datasets include:

* Using `Array.prototype.slice()` and manual iteration
* Implementing a custom chunking algorithm using bitwise operations
* Leveraging libraries like RxJS or Bluebird, which provide observable and promise-based APIs for handling asynchronous data processing

Keep in mind that each approach has its trade-offs in terms of performance, memory usage, and complexity. The choice of method depends on the specific requirements and constraints of your project.

Related benchmarks:

chunk list of objects (version: 0)

Comparing performance of: Native slice vs lodash chunk vs Access values with native vs Access values with lodash

Created: 3 years ago by: Guest

Jump to the latest result

Native slice

lodash chunk

Access values with native

Access values with lodash

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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