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Lodash intersection vs plain js
(version: 0)
Comparing performance of:
Intersection - Full vs aaaaa
Created:
5 years ago
by:
Guest
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HTML Preparation code:
<script src="https://cdn.jsdelivr.net/lodash/4.16.0/lodash.min.js"></script>
Script Preparation code:
var arrFull = ['past','present','future'] var arrOne = ['past']
Tests:
Intersection - Full
_.intersection(arrFull, arrFull)
aaaaa
arrFull.filter(x => arrFull.includes(x))
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
Intersection - Full
aaaaa
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'd be happy to explain the benchmark and its components. **What is being tested?** The provided JSON represents a JavaScript microbenchmark test case on MeasureThat.net. The test compares two approaches: using the Lodash library's `intersection` function versus writing a plain JavaScript implementation. In this specific test case, we have three main entities: 1. **Benchmark Definition**: This is the code snippet that defines the benchmark. In this case, it contains two benchmark definitions: * `_.intersection(arrFull, arrFull)`: This uses the Lodash library's `intersection` function to find the common elements between two arrays (`arrFull`) and returns an array of those common elements. * `arrFull.filter(x => arrFull.includes(x))`: This is a plain JavaScript implementation that filters the original array `arrFull` using the `includes` method to check if each element exists in the array. 2. **Script Preparation Code**: This is the code snippet that prepares the environment for the benchmark test. In this case, it defines two arrays: `arrFull` and `arrOne`. The latter is not used in the benchmark but can be considered a setup for the first benchmark definition (`_.intersection(arrFull, arrFull)`). 3. **Html Preparation Code**: This is an external JavaScript library that provides the Lodash functionality, including the `intersection` function. In this case, it's included via a CDN (Content Delivery Network). **Options compared** The two options being compared are: 1. Using the Lodash library's `intersection` function 2. Writing a plain JavaScript implementation using the `filter` method **Pros and Cons of each approach:** **Lodash Library (`_.intersection(arrFull, arrFull)`)** Pros: * Faster execution time due to optimized native code * Reduced memory allocation and garbage collection * Easier to read and maintain, as it's a well-tested library function Cons: * Dependence on an external library (Lodash) * Potential overhead of including the library via CDN **Plain JavaScript Implementation (`arrFull.filter(x => arrFull.includes(x))`)** Pros: * No dependence on an external library * Simple and easy to understand for developers familiar with JavaScript basics Cons: * Slower execution time due to manual array manipulation * More memory allocation and garbage collection overhead * May be less efficient than the optimized Lodash implementation **Other considerations:** * The `filter` method has a higher number of iterations compared to the Lodash `intersection` function, which may impact performance. * The plain JavaScript implementation requires more careful handling of edge cases (e.g., empty arrays, duplicate elements) that might not be accounted for by the Lodash library. **Special JS features or syntax:** The benchmark uses the ES6 `includes` method and arrow functions (`x => arrFull.includes(x)`). These features are relatively recent additions to JavaScript and may not be supported in older browsers or environments. **Library descriptions:** * **Lodash**: A popular utility library for JavaScript that provides a wide range of functional programming helpers, including the `intersection` function. Lodash is designed to provide concise and efficient solutions for common problems. In summary, this benchmark tests the performance difference between using an optimized external library (`_.intersection`) versus writing a plain JavaScript implementation. The results will indicate which approach is faster and more suitable for specific use cases.
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