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lodash map + filter vs vanilla js reduce
(version: 0)
Comparing performance of:
reduce vs lodash
Created:
6 years ago
by:
Guest
Jump to the latest result
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>
Run tests (2)
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Test case name
Result
reduce
lodash
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
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.
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