Benchmark: _.chain then reduce vs Object.entries then reduce

HTML Preparation code:

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

Script Preparation code:

var users = {}

for (var i = 0; i < 1000; i++) {
  if (i % 2) {
 	users[i] = {a: "a", b: "b"}
  } else {
 	users[i] = {b: "b"}
  } 
}

Tests:

Lodash reduce
var res = _.chain(users) .reduce((acc, a, userID) => { if (a === "a") { acc.push(userID) } return acc }, []) .value()
Native reduce
var res = Object.entries(users) .reduce((acc, val) => { if (val[1].a) { acc.push(val[0]) } return acc }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash reduce
Native reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's dive into explaining the benchmark and its options.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmarking test case, which compares two approaches for reducing an object in a specific scenario: using Lodash's `chain` method with `reduce`, and using native JavaScript's `Object.entries` method with `reduce`.

**Script Preparation Code**

The script preparation code generates a dataset of 1000 objects, where every other object has a property `a` set to `"a"`. The purpose of this data is to create a scenario that triggers the reduction logic in the benchmark.

**Html Preparation Code**

The HTML preparation code includes a reference to Lodash's JavaScript library (`lodash.min.js`) via a CDN. This ensures that Lodash is available for use in the benchmark.

**Individual Test Cases**

There are two test cases:

1. **Lodash reduce**: This test case uses Lodash's `chain` method with `reduce`. The benchmark definition code creates an array of user IDs using this approach.
2. **Native reduce**: This test case uses native JavaScript's `Object.entries` method with `reduce`. The benchmark definition code creates an array of user IDs using this approach.

**Comparison**

The two approaches are compared in terms of performance (expressions per second). Lodash's `chain` method and `reduce` is expected to outperform the native JavaScript approach, as it is a more optimized implementation of the reduction logic.

**Pros and Cons**

**Lodash reduce:**

Pros:

* Optimized implementation of reduction logic
* Potential for better performance

Cons:

* Additional overhead due to the use of Lodash library
* May introduce additional memory usage

**Native reduce:**

Pros:

* No additional overhead or memory usage
* Native JavaScript implementation

Cons:

* May be slower than optimized Lodash implementation
* More prone to errors and bugs due to manual implementation

**Other Considerations**

The benchmark is likely using the `Benchmark.js` library, which provides a standardized framework for creating benchmarks. This ensures that the results are consistent across different environments and browsers.

**Library and Special JS Features**

In this case, Lodash is used as a third-party library, providing a convenient and optimized implementation of the reduction logic. There are no special JavaScript features or syntax mentioned in the benchmark definition code.

**Alternatives**

Other alternatives to Lodash's `chain` method with `reduce` could include:

* Using other libraries like Underscore.js or Ramda
* Implementing the reduction logic manually using native JavaScript
* Using a different data structure, such as an array of objects instead of an object with user IDs.

However, these alternatives are not currently being tested in this benchmark.

Related benchmarks:

_.chain then reduce vs Object.entries then reduce (version: 0)

Comparing performance of: Lodash reduce vs Native reduce

Created: 5 years ago by: Registered User

Jump to the latest result

Lodash reduce

Native 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):