Benchmark: js methonds - MeasureThat.net

HTML Preparation code:

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

Script Preparation code:

const arr = [{vaults: [1, 2]}, {vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]}];

Tests:

flatten
_.flatten(_.map([{vaults: [1, 2]}, {vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]}], ({ vaults }) => vaults))
reduce
[{vaults: [1, 2]}, {vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]},{vaults: [1, 2]}].reduce((a,{vaults}) => a.concat(vaults), [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatten
reduce

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 provided benchmark definition and test cases.

**Benchmark Definition**

The benchmark is defined by two JSON objects that represent individual test cases:

1. **Script Preparation Code**: This code defines an array of objects, each containing a `vaults` property with an array value. The array has six elements.
2. **Html Preparation Code**: This code includes a script tag that loads the Lodash JavaScript library (version 4.17.5) from a CDN.

**Individual Test Cases**

There are two test cases:

1. **Test Case: "flatten"**
	* **Benchmark Definition**: This definition uses the `_.flatten()` function from the Lodash library, which flattens an array of arrays into a single array.
	* **Input Data**: The input data is generated by mapping over the previously defined array and extracting the `vaults` property from each object. The result is passed to the `_.flatten()` function.
2. **Test Case: "reduce"**
	* **Benchmark Definition**: This definition uses the `Array.prototype.reduce()` method, which applies a callback function to each element in an array and reduces it to a single value (in this case, an array).
	* **Input Data**: The input data is the same as in the previous test case.

**Options Compared**

The two test cases compare different approaches for flattening or reducing the input data:

1. **Lodash's _.flatten() function**
2. **Built-in JavaScript method: Array.prototype.reduce()**

**Pros and Cons of Different Approaches**

* **Lodash's _.flatten() function**:
	+ Pros:
		- Efficient and concise way to flatten arrays.
		- Handles nested arrays of arbitrary depth.
	+ Cons:
		- Requires the Lodash library, which may introduce additional overhead.
		- May not be as familiar or intuitive for some developers.
* **Built-in JavaScript method: Array.prototype.reduce()**:
	+ Pros:
		- No additional dependencies required (native JavaScript).
		- Familiar and intuitive for many developers.
	+ Cons:
		- May be slower or more memory-intensive than the _.flatten() function, especially for large inputs.

**Library Used**

* **Lodash**: A utility library that provides a wide range of functional programming helpers, including `_.flatten()`.

**Special JS Features/Syntax**

None are explicitly mentioned in the benchmark definition. However, it's worth noting that JavaScript supports various features and syntax, such as:

* Arrow functions (`=>`)
* Template literals (`${}`)

These features may impact the performance or behavior of the benchmark, but they are not explicitly used in this example.

**Alternatives**

Other alternatives for flattening or reducing arrays could include:

1. Using `Array.prototype.flat()` instead of `_.flatten()`.
2. Implementing a custom reduction function using JavaScript's built-in `forEach()` method.
3. Utilizing libraries like Underscore.js, which provides similar functionality to Lodash.

Keep in mind that the choice of approach depends on the specific requirements and constraints of your project.

Related benchmarks:

js methonds (version: 0)

Comparing performance of: flatten vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

flatten

reduce

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):