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Native map & filter vs reduce with push
(version: 0)
Lodash map & filter vs reduce with push
Comparing performance of:
map & filter vs reduce
Created:
4 years ago
by:
Registered User
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HTML Preparation code:
<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>
Script Preparation code:
var arr = []; for (var i = 0; i < 1000000; i++) { arr[i] = { num: i }; }
Tests:
map & filter
arr.map(item => item.num).filter(num => num > 15 && num < 30);
reduce
arr.reduce((newArr, item) => { if (item.num > 15 && item.num < 30) { newArr.push(item.num); } return newArr; }, []);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
map & filter
reduce
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
**Overview of the Benchmark** The provided benchmark measures the performance difference between two approaches for filtering data in JavaScript: using `Array.prototype.map` and `Array.prototype.filter`, versus using `Array.prototype.reduce`. The test case uses the Lodash library for the `map` and `filter` approach. **Options Compared** Two options are compared: 1. **Native map & filter**: Using the native `Array.prototype.map` and `Array.prototype.filter` methods. 2. **Reduce with push**: Using the `Array.prototype.reduce` method to build a new array, pushing elements that meet certain conditions. **Pros and Cons of Each Approach** ### Native Map & Filter Pros: * Native implementation, optimized for performance * Easy to read and understand code * No additional library dependencies required Cons: * Can be slower due to the overhead of multiple method calls * May not perform well on large datasets ### Reduce with Push Pros: * Can be faster for large datasets since it only requires a single pass through the array * Allows for more control over the filtering process using the reducer function Cons: * Requires more complex code, potentially harder to read and understand * Additional library dependencies (Lodash) required * May not perform well on smaller datasets due to overhead of creating an empty array **Library: Lodash** Lodash is a popular JavaScript utility library that provides a lot of useful functions for common tasks. In this case, the `map` and `filter` methods are used from Lodash. The `reduce` method is also part of the native Array API. **Special JS Feature or Syntax** There is no special JavaScript feature or syntax mentioned in the benchmark definition. **Other Alternatives** Other alternatives for filtering data in JavaScript include: * Using `Array.prototype.filter` with a callback function * Using a library like jQuery, which provides similar functionality to Lodash * Implementing custom filtering logic using ES6+ features like arrow functions and destructuring It's worth noting that the performance difference between these approaches can be significant, especially for large datasets. The benchmark results show that Chrome 107 can execute `reduce` significantly faster than `map & filter`.
Related benchmarks:
Native map & filter vs reduce with spread
Lodash map & filter vs reduce with push
Lodash map & filter vs reduce with push and desctructuring (10 000 samples )
Native map & filter vs reduce with push and desctructuring (10 000 samples )
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