Benchmark: Lodash groupBy vs JS reduce

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.20/lodash.min.js'></script>

Script Preparation code:

var max2 = 10000;

var data = [];
for (var i = 0; i <= max2; i++) { data.push({ id: i }); }

Tests:

Lodash
_.groupBy(data, ({ id }) => id)
JS reduce
data.reduce((acc, item) => { acc[item.id] = item; return acc; }, {})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
JS 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 break down the provided benchmark and explain what's being tested, compared, and considered.

**Benchmark Overview**

The test measures the performance of two approaches for grouping data:

1. **Lodash `groupBy`**: The test uses the Lodash library to group an array of objects based on a common property (`id`). The `groupBy` function takes two arguments: the input data (`data`) and a callback function that extracts the grouping key (`({ id }) => id`).
2. **JS `reduce`**: The test uses the built-in `reduce` method in JavaScript to group an array of objects based on a common property (`id`). The `reduce` function takes two arguments: an initial value (`{}`) and a callback function that accumulates the grouped data.

**Comparison**

The benchmark compares the performance of these two approaches:

* **Lodash `groupBy`**: Uses the Lodash library, which provides a convenient and efficient way to group data.
* **JS `reduce`**: Uses the built-in `reduce` method, which is a more lightweight and flexible alternative.

**Pros and Cons**

**Lodash `groupBy`:**

Pros:

* Highly optimized for performance
* Simplifies grouping data in a concise and readable way
* Provides a robust set of features, including support for multiple groupings and filtering

Cons:

* Requires an additional library dependency (Lodash)
* May have higher overhead due to the library's functionality

**JS `reduce`:**

Pros:

* Lightweight and flexible, with more control over the grouping process
* No additional library dependency required
* Can be easily customized or extended for specific use cases

Cons:

* May require more boilerplate code and manual implementation
* Less optimized for performance compared to Lodash's built-in functions

**Other Considerations**

* **Data size**: The benchmark creates a large array of 10,000 objects with varying `id` values. This data size is likely chosen to demonstrate the performance difference between the two approaches.
* **Browser and device specifics**: The benchmark results show data for Chrome 97 on Windows Desktop. It's essential to consider how different browsers, devices, and operating systems might affect performance.

**Library (Lodash)**

The Lodash library provides a comprehensive set of utility functions, including `groupBy`, that simplify common tasks like data manipulation and grouping. In this benchmark, Lodash's `groupBy` function is used to provide an optimized implementation for grouping data.

**Special JS Feature/ Syntax**

There are no special JavaScript features or syntax used in this benchmark. Both approaches rely on standard JavaScript methods (e.g., `reduce`, array iteration).

**Alternatives**

Some alternative approaches for grouping data might include:

* Using other library functions, such as Moment.js for date-based grouping
* Implementing custom grouping logic using vanilla JavaScript or a different library
* Utilizing Web Workers or parallel processing for improved performance on large datasets

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

Related benchmarks:

Lodash groupBy vs JS reduce (version: 0)

Comparing performance of: Lodash vs JS reduce

Created: 5 years ago by: Guest

Jump to the latest result

Lodash

JS 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):