Benchmark: lodash groupBy vs Array.reduce 100k with array push

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 = 100000;

var data = [];
for (var i = 0; i <= max2; i++) { data.push({ id: i }); }

Tests:

Lodash
_.groupBy(data, ({ id }) => id)
Native
data.reduce((acc, item) => { if (acc[item.id]) { acc[item.id].push(item) } else { 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:

Run details: (Test run date: 5 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/142.0.0.0 Safari/537.36

Browser/OS: Chrome 142 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Lodash	395.5 Ops/sec
Native	912.5 Ops/sec

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 components.

**What is being tested?**

MeasureThat.net is testing the performance difference between two approaches for grouping an array of objects based on a specific property:

1. Using the `groupBy` function from the Lodash library, which groups an array by a specified function.
2. Implementing the grouping logic using the native `Array.reduce()` method.

**Options compared**

The benchmark is comparing the performance of these two approaches:

* **Lodash**: The `groupBy` function from Lodash is used to group the data. Lodash is a utility library that provides a set of high-order functions for functional programming.
* **Native**: The grouping logic is implemented using the native `Array.reduce()` method, which applies a function against an accumulator and a series of values in the array.

**Pros and cons**

Here are some pros and cons of each approach:

**Lodash (`groupBy`):**

Pros:

* More concise and readable code
* Less error-prone, as it's a well-tested library with a clear API
* Can handle complex grouping logic

Cons:

* Adds an external dependency (the Lodash library)
* May have overhead due to the library itself
* Performance might be affected by the size of the input data

**Native (`Array.reduce()`):**

Pros:

* No external dependencies, so no potential performance overhead
* Can be more efficient for large datasets, as it avoids the overhead of a library function

Cons:

* More verbose and less readable code
* Requires manual handling of edge cases and error handling

**Other considerations**

The benchmark also considers the following factors:

* The size of the input data (100,000 elements)
* The JavaScript engine and browser used for testing (Chrome 116 on Mac OS X 10.15.7)
* Device platform (Desktop)

**Library: Lodash**

Lodash is a popular utility library for JavaScript that provides a set of high-order functions for functional programming. In this benchmark, the `groupBy` function is used to group an array by a specified function.

The `groupBy` function takes three arguments:

* The input array
* A function that returns the grouping key (in this case, the `id` property)
* An optional accumulator value (which is not used in this benchmark)

**Special JS feature or syntax**

There are no special JavaScript features or syntaxes being tested in this benchmark. However, it's worth noting that some modern browsers and JavaScript engines support new features like async/await, decorators, and other advanced syntaxes.

Overall, the benchmark provides a useful comparison of two approaches for grouping an array of objects, highlighting the trade-offs between using an external library (Lodash) versus implementing the logic natively.

Related benchmarks:

lodash groupBy vs Array.reduce 100k with array push (version: 0)

Comparing performance of: Lodash vs Native

Created: 2 years ago by: Guest

Jump to the latest result

Lodash

Native

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):