Benchmark: aa lodash range - MeasureThat.net

HTML Preparation code:

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

Tests:

lodash
const NUMPAD_DATA = [ ..._.range(1, 10).map((el) => ({ child: el.toString(), })), ];
arry
const NUMPAD_DATA = [1, 2, 3, 4, 5, 6, 7, 8, 9];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash
arry

Fastest: N/A

Slowest: N/A

Latest 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 test cases.

**Benchmark Definition and Preparation Code**

The benchmark is defined by a JSON object that provides metadata about the benchmark, including:

* `Name`: a unique name for the benchmark
* `Description`: an optional description of the benchmark (not provided in this case)
* `Script Preparation Code`: an optional code to run before each test (not provided in this case)
* `Html Preparation Code`: an HTML script tag that loads the Lodash library version 4.17.4 from a CDN

The Html Preparation Code is crucial, as it provides the necessary context for the benchmark to execute correctly. By loading the Lodash library, we can ensure that the benchmark has access to its functionality.

**Test Cases**

There are two test cases:

1. **`aa lodash range`**: This test case uses the `Benchmark Definition` code:
```javascript
const NUMPAD_DATA = [
  ..._.range(1, 10).map((el) => ({ child: el.toString() })),
];
```
This code creates an array `NUMPAD_DATA` using Lodash's `_range()` and `_map()` functions. The resulting array is then assigned to the constant `NUMPAD_DATA`.

2. **`arry`**: This test case uses a simpler array creation code:
```javascript
const NUMPAD_DATA = [1, 2, 3, 4, 5, 6, 7, 8, 9];
```
This code creates an array `NUMPAD_DATA` with the specified numbers.

**Options Compared**

The two test cases differ in how they create the `NUMPAD_DATA` array:

* **Lodash vs. native JavaScript**: The first test case uses Lodash's `_range()` and `_map()` functions, while the second test case uses native JavaScript's `Array.prototype.range()` and `Array.prototype.map()` methods.
* **Explicit vs. implicit iteration**: In the first test case, the `_range()` and `_map()` functions explicitly iterate over a range of numbers to create the array, whereas in the second test case, the native JavaScript methods implicitly iterate over the range.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Lodash**:
	+ Pros: provides more readable and maintainable code, uses efficient algorithms under the hood
	+ Cons: requires loading an additional library (Lodash), may have overhead due to function calls
* **Native JavaScript**:
	+ Pros: no additional library required, potentially faster execution due to reduced overhead
	+ Cons: code can be less readable and maintainable, may require more expertise in array manipulation

**Library: Lodash**

The `lodash` library is a popular utility library for JavaScript that provides a wide range of functions for tasks like string manipulation, array manipulation, and more. In this benchmark, Lodash's `_range()` and `_map()` functions are used to create the `NUMPAD_DATA` array in the first test case.

**Special JS Feature: None**

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

**Alternatives**

Some alternative approaches for creating arrays like `NUMPAD_DATA` could include:

* Using a library like `ramda` (another utility library with functional programming principles)
* Implementing an array creation function using a custom algorithm
* Using a different data structure, such as a `Set` or `Map`, to store the data

Keep in mind that these alternatives may have different performance characteristics and use cases compared to the Lodash approach.

Related benchmarks:

aa lodash range (version: 0)

Comparing performance of: lodash vs arry

Created: 5 years ago by: Guest

Jump to the latest result

lodash

arry

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):