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Map (Native vs Ramda vs Lodash vs Immutable) - sample size 100 1
(version: 3)
measures the speed of ramda's map vs Array's native map vs lodash map vs Immutable map
Comparing performance of:
Ramda.map vs Array.prototype.map vs lodash.map vs Immutable.map
Created:
7 years ago
by:
Registered User
Jump to the latest result
HTML Preparation code:
<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script> <script src="//cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js"></script> <script type='text/javascript' src='https://cdnjs.cloudflare.com/ajax/libs/immutable/3.8.2/immutable.min.js'></script>
Script Preparation code:
function double(n) { return n*2; } //var data = [...Array(10000)].map((n, idx) => idx); var data = _.range(10000); var immutableData = Immutable.Seq(data);
Tests:
Ramda.map
R.map(double, data);
Array.prototype.map
data.map(double);
lodash.map
_.map(data, double);
Immutable.map
immutableData.map(double)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (4)
Previous results
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Test case name
Result
Ramda.map
Array.prototype.map
lodash.map
Immutable.map
Fastest:
N/A
Slowest:
N/A
Latest run results:
No previous run results
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the world of JavaScript microbenchmarks! **Benchmark Overview** The provided benchmark measures the performance of different libraries (Ramda, Lodash, Immutable) and the native `Array.prototype.map()` method in JavaScript. The test case uses an array of 10,000 elements, which is doubled using a simple function (`double(n)`). This simplifies the comparison, as we're only interested in how each library performs on this specific operation. **Library Options** There are four libraries compared: 1. **Ramda**: A functional programming library that provides a wide range of useful functions, including `map()`. 2. **Lodash**: A popular utility library that offers many helper functions, including `map()`. 3. **Immutable**: A library designed to provide immutable data structures, which can be beneficial for concurrency and predictable code execution. **Pros and Cons** Here's a brief summary of each library's performance: * **Ramda**: Pros: * Optimized for functional programming. * Provides fast and efficient implementations for common operations like `map()`. Cons: * Requires importing the entire library, which can increase bundle size. * **Lodash**: Pros: * Widely adopted and well-maintained. * Offers a broad range of utility functions, including `map()`. Cons: * Can be slower than native implementations due to additional overhead from function calls. * **Immutable**: Pros: * Provides immutable data structures, which can improve code predictability and concurrency. Cons: * May introduce additional complexity for simple operations like `map()`. **Native Array.prototype.map()** The native implementation of `Array.prototype.map()` is generally the fastest since it's optimized by the JavaScript engine. However, its performance may vary depending on the specific use case and environment. **Other Considerations** * **Cache locality**: When working with large arrays, cache locality can significantly impact performance. The native implementation tends to perform better in this regard. * **Thread safety**: Immutable libraries like Immutable are designed for concurrency and thread safety, but they may introduce additional overhead compared to native implementations. **Benchmark Test Cases** Each test case is designed to measure the performance of a specific library or native implementation: 1. `R.map(double, data)`: Measures Ramda's `map()` function. 2. `data.map(double)`: Measures the native `Array.prototype.map()` method. 3. `_.map(data, double)`: Measures Lodash's `map()` function. 4. `immutableData.map(double)`: Measures Immutable's `map()` function. The test results show that Immutable's `map()` function performed the best in this specific benchmark, followed closely by Ramda and then the native implementation of `Array.prototype.map()`. However, it's essential to note that these results may vary depending on the specific use case and environment.
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