Benchmark: lodash groupBy vs Array.reduce (1mln)

HTML Preparation code:

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

Script Preparation code:

var max2 = 1000000; // 1,000,000 (1 Million)

var data = [];
for (var i = 0; i <= max2; i++) { data.push({ id: i }); }

Tests:

Lodash
_.groupBy(data, ({ id }) => id)
Native
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
Native

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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 explain what's being tested.

The benchmark is comparing two approaches to group an array of objects by their `id` property:

1. **Lodash**: The first test case uses the `lodash.groupBy()` function to group the data. Lodash is a popular JavaScript library that provides a wide range of utility functions, including grouping.
2. **Native**: The second test case uses the built-in `Array.prototype.reduce()` method to achieve the same result.

Now, let's discuss the pros and cons of each approach:

**Lodash:**

Pros:

* Easier to read and write, as it provides a specific function for grouping data.
* More concise code, which can lead to faster execution times.

Cons:

* Introduces additional overhead due to the need to load and initialize the Lodash library.
* May not be as efficient as native implementation, especially for large datasets.

**Native:**

Pros:

* No additional overhead, as it only requires the use of built-in JavaScript methods.
* Can be optimized by the browser's Just-In-Time (JIT) compiler, leading to better performance.

Cons:

* More verbose code, which can lead to slower execution times due to parsing and compilation overhead.
* Requires a good understanding of the `reduce()` method and its usage.

Other considerations:

* The benchmark results show that the native implementation outperforms Lodash in this specific test case. However, it's essential to note that this result may not hold true for all use cases or datasets.
* The Lodash library provides additional functionality beyond grouping, such as caching and memoization. In some cases, using Lodash might be beneficial even if the native implementation is faster.

Special JS feature/syntax:

* There is no special JavaScript feature or syntax being used in this benchmark. Both approaches rely on standard JavaScript methods and libraries (Lodash).

Alternatives:

* Other grouping algorithms available in JavaScript include `Array.prototype.forEach()` with a custom callback function, or using a library like Moment.js for date-based grouping.
* For more complex data processing tasks, consider using frameworks like React or Angular, which provide built-in tools for data manipulation and optimization.

When preparing the benchmark, it's essential to ensure that:

* The input dataset is large enough to produce meaningful results (in this case, 1 million elements).
* The test code accurately represents the real-world scenario.
* The test environment is consistent across all runs.

By carefully crafting the benchmark, you can gather reliable insights into the performance characteristics of different grouping algorithms in JavaScript.

Related benchmarks:

lodash groupBy vs Array.reduce (1mln) (version: 0)

Comparing performance of: Lodash vs Native

Created: 5 years ago by: Guest

Jump to the latest result

Lodash

Native

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):