Benchmark: Lodash vs Native reduce on object 3

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 = 100; i < len; i++) {
  var arr = []
  obj['key' + i] = arr
  for (var n = 0, len = 100; n < len; n++) {
    arr.push({ ['key' + n]: n });
  }
}

Tests:

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

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.

I'd be happy to help you understand what's being tested in the provided JSON.

**Overview**

The benchmark tests two approaches to reducing an object: using Lodash (a popular JavaScript utility library) versus doing it natively (without any external libraries). The test case creates a large object with nested arrays and then reduces it to a new object with transformed data.

**Options being compared**

There are two options being compared:

1. **Lodash**: Using the `_.reduce` method from Lodash to reduce the object.
2. **Native**: Doing the reduction manually without using any external libraries or methods.

**Pros and Cons of each approach:**

**Lodash:**

Pros:

* Easier to read and write, as it's a higher-level abstraction that handles many details for you.
* Less prone to errors, as it enforces consistency in data transformations.

Cons:

* Adds overhead due to the need to include an external library.
* May not be suitable for very large datasets or performance-critical applications.

**Native:**

Pros:

* No additional overhead from including an external library.
* Can be more efficient and scalable, especially for large datasets.

Cons:

* Requires more manual effort and attention to detail.
* More prone to errors if not implemented correctly.

**Library used: Lodash**

Lodash is a popular JavaScript utility library that provides a collection of functions for tasks such as array manipulation, object transformation, and more. In this case, the `_reduce` method is used to reduce an object to a new one.

**Special JS feature or syntax**

None mentioned in the provided JSON. However, it's worth noting that some modern JavaScript features like `async/await`, `let const` declarations, or newer methods (e.g., `map()`, `forEach()`) might be relevant to optimizing the benchmark code.

**Other alternatives**

If you wanted to reduce the object natively without using Lodash, you could use a simple loop with array methods like `forEach()` or `map()`. However, this would require more manual effort and may not be as efficient as the native approach. Other libraries or frameworks might also provide similar functionality, such as Ramda or Underscore.js.

To improve the benchmarking process, it's essential to consider factors like:

* Data size and structure
* Performance requirements (e.g., real-time applications, data-intensive tasks)
* Code maintainability and readability
* Library overhead and versioning considerations

The benchmark results can provide valuable insights into the performance differences between Lodash and native approaches, helping developers make informed decisions about their code optimizations.

Related benchmarks:

Lodash vs Native reduce on object 3 (version: 0)

Comparing performance of: Lodash vs Native

Created: 6 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):