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native map vs lodash
(version: 1)
Comparing performance of:
Native map vs Lodash map
Created:
5 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 type="text/javascript"> let arr = []; for (let i = 0; i < 1000000; i++) { arr.push(i) } </script>
Tests:
Native map
const newArr = arr.map((el) => el + 1);
Lodash map
const newArr = _.map(arr, (el) => el + 1);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
Fork
Test case name
Result
Native map
Lodash map
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
6 months ago
)
User agent:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36
Browser/OS:
Chrome 141 on Windows
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Embed Benchmark Result
Test name
Executions per second
Native map
116.1 Ops/sec
Lodash map
582.3 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark JSON and explain what's being tested. **Benchmark Overview** The benchmark is designed to compare the performance of JavaScript's built-in `map` function with the popular library, Lodash's `map` function. **Script Preparation Code** The script preparation code includes two external libraries: 1. **Lodash**: A utility library that provides a wide range of functions for tasks such as string manipulation, array manipulation, and more. 2. **JavaScript native map function**: The built-in `map` function in JavaScript. **Individual Test Cases** There are two test cases: 1. **Native map**: This test case uses the built-in `map` function in JavaScript to create a new array with elements that have been incremented by 1. 2. **Lodash map**: This test case uses Lodash's `map` function, which is an alias for `lodash.map`, to achieve the same result as the native `map` function. **Options Compared** The benchmark compares two options: 1. **Native JavaScript map function** 2. **Lodash's map function** **Pros and Cons of Each Approach:** 1. **Native JavaScript map function:** * Pros: + Built-in, no external library dependency. + Generally faster, as it doesn't involve the overhead of a separate library. * Cons: + May require more manual setup and maintenance. 2. **Lodash's map function:** * Pros: + Easier to use, especially for those familiar with Lodash's API. + Can be faster due to optimized internal implementations. * Cons: + External library dependency, which can add overhead. + May not be as widely supported or well-maintained. **Considerations:** When deciding between these two approaches, consider the following factors: 1. **Performance**: If speed is critical, native JavaScript may have an edge due to its built-in nature and optimized implementation. 2. **Maintenance**: If you prefer a more straightforward API and don't want to worry about external library dependencies, Lodash's map function might be a better choice. 3. **Ecosystem familiarity**: If your team is already familiar with Lodash, using it may be easier and more convenient. **Special Considerations** The benchmark uses the `for` loop to create an array of 1 million elements, which can impact performance due to the overhead of creating and managing large arrays. However, this is not specific to either the native map function or Lodash's map function; it's a common pattern in JavaScript testing. **Alternatives** If you need to compare other mapping functions or libraries, consider alternatives like: 1. **Underscore.js**: Another popular utility library that provides a `map` function. 2. **Array.prototype.map() with custom iteration**: Instead of using Lodash's map function, you can create your own implementation using the native `map()` method and a custom iterator. 3. **Other mapping libraries or frameworks**: There are many other mapping libraries and frameworks available, such as Ramda.js, RxJS, or even custom implementations. Keep in mind that each of these alternatives has its pros and cons, which would need to be considered when choosing an implementation for your specific use case.
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