Benchmark: lodash map + filter vs vanilla js reduce

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.15/lodash.min.js"></script>

Tests:

reduce
let someArray = [ { keep: true, given_name: 'first', surname: 'second' }, { keep: false, given_name: 'third', surname: 'fourth' }, { keep: true, given_name: 'fifth', surname: 'sixth' }, ]; let filteredData = someArray.reduce((result, obj) => { if (obj.keep) { result.push({ full_name: `${ obj.given_name } ${ obj.surname }`, some_other_key: null, ...obj, }); } return result; }, []);
lodash
let someArray = [ { keep: true, given_name: 'first', surname: 'second' }, { keep: false, given_name: 'third', surname: 'fourth' }, { keep: true, given_name: 'fifth', surname: 'sixth' }, ]; let data = _.chain(someArray) .filter({keep: true}) .map((o) => { return { full_name: `${ o.given_name } ${ o.surname }`, some_other_key: null, ...o, } }) .value();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
lodash

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):

LLMs can make mistakes. Check important info.

I'll break down the explanation into smaller parts to help software engineers understand what's being tested and compared.

**Benchmark Overview**

The benchmark measures the performance of two approaches: using vanilla JavaScript's `reduce` method versus using Lodash's `filter` and `map` methods.

**Options Compared**

There are two main options:

1. **Vanilla JavaScript's reduce**: This approach uses a custom-built function to filter and map the data.
2. **Lodash's filter and map**: This approach uses the popular utility library, Lodash, which provides pre-built functions for filtering and mapping arrays.

**Pros and Cons**

**Vanilla JavaScript's Reduce:**

Pros:

* Can be optimized for specific use cases (e.g., caching, memoization)
* Can be written in a more concise and expressive way

Cons:

* Requires manual implementation of filtering and mapping logic
* May not work as well with large datasets or complex data structures
* Not tested by Lodash's extensive unit tests

**Lodash's Filter and Map:**

Pros:

* Provides pre-built functions for common use cases, reducing development time
* Extensively tested by Lodash's unit tests
* Works well with large datasets and complex data structures

Cons:

* Requires including the Lodash library in the project (addition overhead)
* May not be as performant as custom-written code (although this is unlikely to be a significant difference)

**Other Considerations**

When deciding between these approaches, consider the following factors:

* Development time: Using pre-built functions like Lodash's `filter` and `map` can save development time.
* Performance: For small datasets, the performance difference may not be noticeable. However, for large datasets or critical performance-critical code paths, custom-written code might provide a slight advantage.
* Code readability: Vanilla JavaScript's reduce method can be more concise and expressive when writing filtering and mapping logic.

**Library Usage (Lodash)**

Lodash is a popular utility library that provides a wide range of functions for common use cases, such as:

* Array manipulation (`map`, `filter`, `reduce`)
* String manipulation (`string.prototype.startsWith`, `string.prototype.endsWith`)
* Object manipulation (`_.pick`, `_omit`, etc.)

The Lodash library includes extensive unit tests to ensure its functions work correctly.

**Special JavaScript Feature/Syntax**

None of the provided benchmark definitions use any special or advanced JavaScript features (e.g., async/await, destructuring, ES6 classes).

**Alternatives**

If you want to explore alternative approaches for similar benchmarks, consider:

* Using other utility libraries like Moment.js, console-loggers, or custom-built functions
* Implementing filtering and mapping logic from scratch without relying on existing libraries
* Using a different programming paradigm (e.g., functional programming with currying)

Keep in mind that these alternatives might require more development time and testing effort.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:96.0) Gecko/20100101 Firefox/96.0

Browser/OS: Firefox 96 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
reduce	3444942.2 Ops/sec
lodash	755101.4 Ops/sec

Related benchmarks:

lodash map + filter vs vanilla js reduce (version: 0)

Comparing performance of: reduce vs lodash

Created: 5 years ago by: Guest

Jump to the latest result

reduce

lodash

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):