Benchmark: lodash flatten vs reduce

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.4/lodash.min.js"></script>

Script Preparation code:

window.obj = {};

for (var i = 0, len = 10; i < len; i++) {
  var arr = []
  obj['key' + i] = arr
  for (var n = 0, len = 10; n < len; n++) {
    arr.push({ ['key' + n]: n });
  }
}

Tests:

flatten
_.flatten(obj, (acc, val, key) => { acc[key] = val.map(d => ({ label: key, value: val })); return acc; }, {})
reduce
Object.entries(obj).reduce((acc, [key, val]) => { acc[key] = val.map(d => ({ label: key, value: val })); return acc; }, {})

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):

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Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Overview**

The benchmark compares two approaches to flatten an object: `lodash.flatten` and `Array.prototype.reduce`. The benchmark is designed to test how fast each approach can process a nested array within an object.

**Benchmark Definition JSON**

The benchmark definition is a JSON object that contains the following information:

* `Name`: The name of the benchmark, which is "lodash flatten vs reduce".
* `Description`: A brief description of the benchmark (null in this case).
* `Script Preparation Code`: A JavaScript code snippet that creates an object with nested arrays and assigns them to properties. This code is used to prepare the test data.
* `Html Preparation Code`: The URL of a JavaScript library, `lodash.min.js`, which is loaded into the browser.

**Individual Test Cases**

There are two individual test cases:

1. **flatten**: This test case uses `lodash.flatten` to flatten the object and process the nested array.
2. **reduce**: This test case uses `Array.prototype.reduce` to flatten the object and process the nested array.

**Library: Lodash**

Lodash is a popular JavaScript library that provides various utility functions, including `flatten`. In this benchmark, Lodash is used to provide the implementation for the `flatten` function.

The purpose of Lodash is to simplify common tasks in JavaScript programming by providing a set of reusable functions. In this case, the `flatten` function takes an object and an array as input, flattens the array into an object, and returns the result.

**Special JS Feature: Arrow Functions**

Both benchmark test cases use arrow functions (the syntax `() => { code }`) to define callback functions for the `flatten` and `reduce` functions. This feature is used to create concise and expressive function definitions.

**Pros and Cons of Each Approach**

Here are some pros and cons of each approach:

* **Lodash Flatten**: Pros:
	+ Well-tested and optimized implementation.
	+ Handles nested arrays with ease.
* Cons:
	+ Requires the inclusion of Lodash library in the test code.
	+ May be slower due to the overhead of loading an external library.
* **Array.prototype.reduce**: Pros:
	+ Built-in JavaScript function, so no additional libraries are required.
	+ Can be more efficient than using a custom implementation.

Cons:
	+ Requires manual handling of nested arrays and array indices.
	+ May be less readable and maintainable due to the complexity of the implementation.

**Other Considerations**

When comparing these two approaches, it's essential to consider the following factors:

* **Performance**: How fast is each approach in processing the test data?
* **Readability**: Which approach is easier to understand and maintain?
* **Maintainability**: How easy is it to modify or extend each implementation?

**Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. **Manual Implementation**: Implementing the `flatten` function from scratch using basic JavaScript features (e.g., loops, conditional statements).
2. **Other Lodash Functions**: Using other Lodash functions, such as `map` or `forEach`, to flatten the data.
3. **Custom Library**: Creating a custom library that provides a similar functionality to Lodash.

These alternatives can provide insight into how different approaches handle nested arrays and objects, but they may not be as efficient or maintainable as using established libraries like Lodash.

Related benchmarks:

lodash flatten vs reduce (version: 0)

Comparing performance of: flatten vs reduce

Created: 5 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):