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Map (Native vs Ramda 0.27.2 vs Lodash 4.17.21) 50k
(version: 2)
measures the speed of ramdajs 0.27.2 map vs Array's native map vs lodash 4.17.21 map
Comparing performance of:
Ramda vs Array (native) vs Lodash
Created:
3 years ago
by:
Registered User
Jump to the latest result
HTML Preparation code:
<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.27.2/ramda.min.js"></script> <script src="//cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.21/lodash.min.js"></script>
Script Preparation code:
function double(n) { return n * 2; } var data = [...Array(50000)].map((v, idx) => idx);
Tests:
Ramda
R.map(double, data);
Array (native)
data.map(double);
Lodash
_.map(data, double);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
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Test case name
Result
Ramda
Array (native)
Lodash
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
2 months ago
)
User agent:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/145.0.0.0 Safari/537.36
Browser/OS:
Chrome 145 on Windows
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Embed Benchmark Result
Test name
Executions per second
Ramda
19808.4 Ops/sec
Array (native)
2687.8 Ops/sec
Lodash
17567.0 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the benchmark and explain what's being tested. **Benchmark Overview** The benchmark is designed to measure the performance of three different approaches for mapping over an array: 1. Native Array.prototype.map() 2. Ramda 0.27.2 map() function 3. Lodash 4.17.21 _.map() function The test case uses a sample dataset generated using `Array(50000)`, which is then mapped to double its values. **Options Compared** The benchmark compares the performance of three different approaches: * Native Array.prototype.map(): This is the built-in map function in JavaScript's native array prototype. * Ramda 0.27.2 map(): This is a functional programming library that provides a map function as part of its API. * Lodash 4.17.21 _.map(): This is another popular functional programming library that provides a map function as part of its API. **Pros and Cons** Here's a brief summary of the pros and cons of each approach: 1. **Native Array.prototype.map()** * Pros: + Fastest execution time due to native compilation. + Simple and widely supported. * Cons: + Not optimized for performance, leading to slower execution times on large datasets. 2. **Ramda 0.27.2 map() function** * Pros: + Provides a consistent and predictable performance profile. + Easy to use and integrate with other Ramda functions. * Cons: + Slower execution time compared to native Array.prototype.map(). 3. **Lodash 4.17.21 _.map() function** * Pros: + Similar performance profile to Ramda map(), but potentially faster in some cases. + Wide range of useful utility functions available. * Cons: + Can be overkill for simple mapping tasks, adding unnecessary overhead. **Library Descriptions** 1. **Ramda**: A functional programming library that provides a set of high-order functions for working with arrays and other data structures. The `map()` function is one of its core components. 2. **Lodash**: A utility library that provides a wide range of functions for working with JavaScript, including array manipulation functions like `_.map()`. **Special JS Features or Syntax** This benchmark does not use any special JavaScript features or syntax beyond the standard language and the two libraries being compared. **Alternatives** Other alternatives to these approaches include: * Using the `forEach()` method instead of `map()`, which can be slightly slower but is also more verbose. * Using a library like **Underscore.js**, which provides a similar set of array manipulation functions to Lodash, but with some differences in implementation and API. Overall, this benchmark provides a useful comparison between three different approaches to mapping over arrays, highlighting the trade-offs between performance, simplicity, and consistency.
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