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Test22
(version: 0)
Comparing performance of:
test1 vs test2
Created:
7 years ago
by:
Guest
Jump to the latest result
HTML Preparation code:
<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>
Script Preparation code:
function double(n) { return n*2; } var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30];
Tests:
test1
R.map(double, data);
test2
data.map(double);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
test1
test2
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'd be happy to help explain the provided benchmark! **Benchmark Overview** MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark consists of two test cases, each testing a different approach to perform a simple transformation on an array of numbers. **Test Case 1: R.map(double, data)** This test case uses the Ramda library, which is a popular functional programming library for JavaScript. The `R.map` function applies a given function (in this case, `double`) to each element of the input array (`data`). The test case measures how many elements are mapped in one second. **Pros and Cons:** * Pros: + Uses Ramda's `map` function, which is optimized for performance. + Allows for functional programming style, making code more concise and predictable. * Cons: + Requires the use of an external library (Ramda), which may introduce overhead or conflicts with other libraries. **Test Case 2: data.map(double)** This test case uses the built-in `map` function of JavaScript arrays. It applies the same transformation as Test Case 1, but without using any external library. Pros and Cons: * Pros: + Does not require an external library. + Native performance since it's implemented in C++ by V8 (the engine used by Google Chrome). * Cons: + May have slower performance compared to Ramda's optimized `map` function. + Less concise code, as the transformation is applied explicitly. **Other Considerations:** When choosing between these two approaches, consider the following factors: * Performance: If performance is critical, using Ramda's optimized `map` function might be a better choice. * Code Readability and Maintainability: Using built-in JavaScript functions can make code more readable, as it avoids relying on external libraries. **Special JS Feature/Syntax:** In this benchmark, there are no special JavaScript features or syntax being tested. The code uses standard JavaScript syntax for creating an array, defining a function, and applying the transformation using `map`. **Other Alternatives:** If you need to perform complex data transformations, you might consider other libraries like: * Lodash: A popular utility library that provides various functions for working with arrays and objects. * Array.prototype.reduce(): Another native JavaScript method that can be used for array transformations. Keep in mind that performance will depend on the specific use case and requirements.
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