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Lodash startCase vs Native
(version: 0)
Comparing performance of:
Native vs lodash
Created:
5 years ago
by:
Guest
Jump to the latest result
HTML Preparation code:
<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>
Script Preparation code:
var text = "some tItlE HeRe"
Tests:
Native
text.replace(/-/g, ' ').toLowerCase().split(' ').map((word) => word.charAt(0).toUpperCase() + word.slice(1)).join(' ');
lodash
_.startCase(text)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
Native
lodash
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36
Browser/OS:
Chrome 135 on Windows
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Embed Benchmark Result
Test name
Executions per second
Native
2946456.2 Ops/sec
lodash
1673736.5 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the world of JavaScript microbenchmarks! **What is being tested?** The provided benchmark compares two approaches to convert a string from camelCase to title case: one using native JavaScript functions and the other using Lodash, a popular utility library for JavaScript. **Options compared:** 1. **Native JavaScript approach**: This method uses built-in JavaScript methods such as `replace()`, `toLowerCase()`, `split()`, `map()`, and `join()` to convert the string. 2. **Lodash `startCase` function**: Lodash is a utility library that provides a wide range of functions for common tasks, including string manipulation. The `startCase` function is specifically designed to convert strings from camelCase to title case. **Pros and Cons:** * **Native JavaScript approach**: + Pros: No external dependencies, faster execution since no additional libraries need to be loaded. + Cons: May not be as efficient or readable for complex string manipulations due to the use of multiple methods. * **Lodash `startCase` function**: + Pros: More concise and readable code, easier to maintain and extend with other Lodash functions. + Cons: Requires an additional library dependency, potentially slower execution since a new module needs to be loaded. **Other considerations:** * The benchmark uses Firefox 131 as the test browser, which may have specific optimizations or quirks that affect the results. Testing on multiple browsers can help ensure broader compatibility and accuracy. * The benchmark is running on a desktop platform (Mac OS X 10.15), which may not represent all possible environments where this code will be deployed. **Library: Lodash** Lodash is a popular JavaScript utility library created by Kris Sutherland. It provides a wide range of functions for common tasks, such as: * String manipulation (e.g., `startCase`, `lowercase`, `uppercase`) * Array and object manipulation (e.g., `map`, `filter`, `reduce`) * Functionality for working with dates and times (e.g., `moment.js` integration) * Miscellaneous functions for handling errors, logging, and more Lodash is widely used in the JavaScript community due to its concise and readable API, as well as its extensive set of features. **Special JS feature or syntax:** This benchmark does not rely on any special JavaScript features or syntax. The test cases only use standard JavaScript methods and Lodash functions, making it accessible to a wide range of developers without specific expertise in these areas. **Other alternatives:** If you need to convert strings from camelCase to title case but don't want to use Lodash, you could: * Implement the conversion function yourself using native JavaScript methods. * Use other utility libraries that provide similar functionality, such as Underscore.js or Moment.js (although these might require additional dependencies). * Consider using a library specifically designed for string manipulation and formatting, such as ICU or String-Manipulation. Keep in mind that the performance differences between these approaches may be minimal unless you're working with very large datasets or high-performance applications.
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