Benchmark: Lodash.js vs Native - plz work2

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Script Preparation code:

var max1 = 1000; // 100,000 (100 Thousand)
var max2 = 100; // 10,000,000 (10 Million)
var max3 = 100; // 100,000,000 (100 Million)

var items = [];
for (var i = 0; i <= max1; i++) { items.push(i); }

var cells = [];
for (var i = 0; i <= max2; i++) { cells.push({ items : [...items ] }); }

var columns = [];
for (var i = 0; i <= max3; i++) { columns.push({ cells : [...cells] }); }

Tests:

Native
let allItems = []; for (const column of columns) { for (let rowIndex = 0; rowIndex < column.cells.length; rowIndex++) { allItems.push(...column.cells[rowIndex].items); } }
Lodash.js filter
_(columns).map('cells').flatten().map('items').flatten().valueOf()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native
Lodash.js filter

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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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 components.

**Benchmark Definition**

The benchmark is comparing two approaches:

1. **Native**: This test case uses a simple for loop to iterate through each column, row index, and then push each item into an array called `allItems`.
2. **Lodash.js filter**: This test case uses the Lodash library to perform the same operation. Specifically, it uses the `map`, `flatten`, and `valueOf` methods to transform the data.

**Options compared**

The benchmark is comparing the performance of these two approaches:

* Native code (using JavaScript built-in features)
* Lodash.js library (a popular utility library for JavaScript)

**Pros and Cons of each approach:**

1. **Native**
	* Pros:
		+ Typically faster, as it doesn't involve a library overhead.
		+ Can be more efficient in terms of memory usage.
	* Cons:
		+ Requires manual iteration and indexing, which can lead to slower performance for large datasets.
		+ May require more CPU cycles due to the lack of optimization.
2. **Lodash.js filter**
	* Pros:
		+ Often faster for large datasets, as it leverages optimized library implementations.
		+ Can be more concise and readable, especially for complex transformations.
	* Cons:
		+ Includes a library overhead, which can introduce latency.
		+ May require additional dependencies.

**Lodash.js library**

The Lodash library provides a set of utility functions that can simplify code and improve performance. In this case, the `flatten` method is used to transform the data into a single array, and then the `valueOf` method is used to ensure the result is an array-like object.

**Special JS features or syntax**

There are no special JavaScript features or syntax used in this benchmark that would impact its understanding. The code uses standard JavaScript language constructs, such as loops, arrays, and functions.

**Other alternatives**

In addition to the native approach and Lodash.js library, there may be other libraries or frameworks available for transforming data in JavaScript, such as:

* Ramda: a functional programming library for JavaScript that provides a similar API to Lodash.
* Underscore.js: another utility library for JavaScript that provides many of the same functions as Lodash.

However, the choice of library or framework ultimately depends on the specific requirements and constraints of the project.

Related benchmarks:

Lodash.js vs Native - plz work2 (version: 0)

Comparing performance of: Native vs Lodash.js filter

Created: 3 years ago by: Guest

Jump to the latest result

Native

Lodash.js filter

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):