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array find vs _.find more items
(version: 0)
Compare the new ES6 spread operator with the traditional concat() method
Comparing performance of:
array find vs _.find
Created:
7 years ago
by:
Guest
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HTML Preparation code:
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.core.js"></script>
Tests:
array find
var a = ['hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello']; var b = a.find(item => item === 'bc');
_.find
var a = ['hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello', 'a', 'bc', 'hello']; var b = _.find(a, item => item === 'bc');
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
array find
_.find
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 provided benchmark for you. **Benchmark Definition** The benchmark is to compare two methods for finding an item in an array: the traditional `concat()` method and the new ES6 spread operator (`...`). The test case uses Lodash, a popular JavaScript utility library. **Script Preparation Code** There is no script preparation code provided, which means that the benchmark requires the necessary libraries to be loaded externally. In this case, the HTML preparation code loads Lodash version 4.17.5. **Individual Test Cases** The benchmark consists of two test cases: 1. `array find`: This test case uses the traditional `concat()` method to find an item in the array. 2. `_.find`: This test case uses the `find` function from Lodash to find an item in the array. Both test cases use a similar array, `a`, which contains repeated values of `'bc'`. **Comparison** The benchmark is comparing two approaches: 1. **Traditional concat() method**: This approach uses the `concat()` method to create a new array with the item to be found at the end. 2. **New ES6 spread operator (`...`)**: This approach uses the spread operator (`...`) to create a new array with the item to be found at the end. **Pros and Cons** Here's a brief summary of the pros and cons of each approach: * **Traditional concat() method**: + Pros: Simple, widely supported. + Cons: Creates a new array, which can lead to performance issues for large datasets. * **New ES6 spread operator (`...`)**: + Pros: More efficient than `concat()`, as it only creates a new array reference without copying the data. + Cons: May not work in older browsers or environments that don't support the spread operator. **Lodash Library** The `_.find` function is part of the Lodash library, which provides a set of functional programming helpers and utilities. The `find` function takes an array and a callback function as arguments, returning the first item in the array that satisfies the callback condition. **ES6 Spread Operator** The ES6 spread operator (`...`) was introduced in 2015 to create new arrays or objects from existing ones. It's used to "spread out" elements of an iterable object (like an array) into separate arguments for a function. In this benchmark, it's used to create a new array with the item to be found at the end. **Other Considerations** When writing benchmarks like this one, it's essential to consider factors such as: * **Browser support**: The spread operator is supported in modern browsers, but may not work in older ones. * **Performance**: Both approaches have their performance characteristics. The spread operator may be faster for large datasets, while `concat()` might be better suited for smaller arrays. **Alternatives** If you were to rewrite this benchmark or write a similar one, consider the following alternatives: * Use other libraries or frameworks that provide similar functionality, such as Ramda or Immutable.js. * Experiment with different array methods, like `indexOf()`, `filter()`, or `every()`. * Consider using in-place algorithms instead of creating new arrays. Keep in mind that benchmarking is an art, and the best approach depends on the specific use case, dataset size, and target audience.
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