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Lodash.js vs Native22222yslysl2222
(version: 0)
Comparing performance of:
Lodash.js chunk ysl vs Lodash.js chunk ysl22
Created:
one year 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 max1 = 100000; // 100,000 (100 Thousand) var max2 = 10000000; // 10,000,000 (10 Million) var max3 = 100000000; // 100,000,000 (100 Million) var arr1 = []; //for (var i = 0; i <= max1; i++) { arr1.push(i); } var arr2 = []; for (var i = 0; i <= max2; i++) { arr2.push(i); } var arr3 = []; //for (var i = 0; i <= max3; i++) { arr3.push(i); }
Tests:
Lodash.js chunk ysl
_.chunk(arr3, 4);
Lodash.js chunk ysl22
let i ; i++;
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
Lodash.js chunk ysl
Lodash.js chunk ysl22
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36
Browser/OS:
Chrome 128 on Mac OS X 10.15.7
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Embed Benchmark Result
Test name
Executions per second
Lodash.js chunk ysl
9639823.0 Ops/sec
Lodash.js chunk ysl22
175587584.0 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what's being tested. **Benchmark Overview** The benchmark is designed to compare the performance of two approaches: 1. Lodash.js (a popular JavaScript utility library) 2. Native JavaScript code (without any libraries) The benchmark consists of two test cases: 1. `_.chunk(arr3, 4)` - This test case uses the `chunk` function from Lodash.js to split an array into chunks of size 4. 2. `let i ; i++;` - This is a simple incrementing variable assignment. **Options Compared** The benchmark compares two options: 1. **Lodash.js**: Using the `chunk` function from Lodash.js, which provides a pre-built implementation for splitting arrays into chunks. 2. **Native JavaScript code**: Implementing the same logic using only native JavaScript features. **Pros and Cons of Each Approach** **Lodash.js:** Pros: * Provides a pre-built implementation for chunking arrays, which can be optimized and implemented more efficiently by the library authors. * Can take advantage of browser optimizations and caching. Cons: * Introduces an additional dependency on Lodash.js, which may increase the bundle size and overhead. * May not optimize well for certain edge cases or specific use cases. **Native JavaScript code:** Pros: * Eliminates the need for an external library, reducing the bundle size and potential dependencies. * Can be optimized and customized for specific use cases and performance requirements. Cons: * Requires implementing the chunking logic from scratch, which can be more error-prone and time-consuming. * May not take advantage of browser optimizations and caching as effectively as Lodash.js. **Library Used** In this benchmark, Lodash.js is used in the `_.chunk` function. The purpose of this library is to provide a collection of useful functions for functional programming tasks, such as array manipulation, object transformation, and more. **Special JavaScript Feature or Syntax** There are no special features or syntax mentioned in the benchmark that would require specific knowledge or expertise to understand. However, it's worth noting that Lodash.js uses various JavaScript features, such as closures, higher-order functions, and async/await (in some cases), which may be unfamiliar to developers without a deep understanding of these concepts. **Other Alternatives** If you're interested in exploring alternative approaches for chunking arrays, here are a few options: * Using `Array.prototype.chunk()` or `Array.prototype.slice()`, which can be used to split an array into chunks. * Implementing the chunking logic using a custom function or algorithm. * Using other libraries, such as Moment.js or Underscore.js (if you need more advanced functional programming features). Keep in mind that each of these alternatives may have their own trade-offs and performance characteristics, which should be evaluated based on specific use cases and requirements.
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