Toggle navigation
MeasureThat.net
Create a benchmark
Tools
Feedback
FAQ
Register
Log In
Map (Native vs Ramda vs Lodash)
(version: 4)
measures the speed of ramda's map vs Array's native map vs lodash map
Comparing performance of:
Ramda vs Array (native) vs Lodash
Created:
8 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 Preparation code:
function double(n) { return n*2; } var data = [...Array(20)].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
Fork
Test case name
Result
Ramda
Array (native)
Lodash
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; rv:131.0) Gecko/20100101 Firefox/131.0
Browser/OS:
Firefox 131 on Mac OS X 10.15
View result in a separate tab
Embed
Embed Benchmark Result
Test name
Executions per second
Ramda
5007335.0 Ops/sec
Array (native)
11582874.0 Ops/sec
Lodash
7575312.5 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the benchmark and its options. **Benchmark Definition** The benchmark measures the speed of three different approaches to apply a transformation function to an array: 1. **Array (native)**: The native `map` method of JavaScript arrays, which applies the transformation function to each element in the array. 2. **Ramda**: A functional programming library that provides a `map` function as part of its API. 3. **Lodash**: Another popular functional programming library that provides a `map` function as part of its API. **Options Compared** The benchmark compares the performance of these three approaches: * Ramda's `map` function * The native `map` method of JavaScript arrays * Lodash's `map` function Each approach is tested with a different input: an array of 20 elements, where each element is assigned a value based on its index. **Pros and Cons of Each Approach** 1. **Array (native)**: * Pros: Native implementation, likely to be optimized for performance. * Cons: May not provide the same level of expressiveness as functional programming libraries like Ramda or Lodash. 2. **Ramda**: * Pros: Provides a consistent and expressive API for functional programming, which can lead to more concise and readable code. * Cons: May incur a performance overhead due to the additional function call. 3. **Lodash**: * Pros: Similar to Ramda, provides a consistent and expressive API for functional programming, but with some differences in implementation details. * Cons: May also incur a performance overhead due to the additional function call. **Library and Purpose** 1. **Ramda**: A pure JavaScript functional programming library that aims to provide a comprehensive set of functional programming primitives, including `map`, `filter`, `reduce`, etc. It is designed to be highly composable and expressive. 2. **Lodash**: A more general-purpose utility library that provides a wide range of functions for common tasks, such as `map`, `filter`, `each`, etc. While it does provide a `map` function, its implementation may differ from Ramda's. **Special JS Feature or Syntax** None mentioned in this benchmark. However, note that some libraries like Lodash use techniques like memoization to optimize their performance, which may not be immediately apparent without careful analysis. **Other Alternatives** If you're interested in exploring other approaches, you might consider: 1. **ES6 Array.prototype.map**: A native implementation of `map` for JavaScript arrays. 2. **Other functional programming libraries**: Such as Lo-Dash or Underscore, which provide similar APIs to Ramda and Lodash. 3. **Custom implementations**: Depending on your specific use case, you might find it beneficial to implement a custom mapping function using loops or other approaches. Keep in mind that the choice of library or implementation depends on the specific requirements of your project, such as performance, expressiveness, and maintainability.
Related benchmarks:
Map (Native vs Rambda vs Lodash)
Map (Native vs Ramda vs Lodash)333
Map (Native vs Ramda vs Lodash) latest 2021-01-18
Map (Native vs Ramda vs Lodash) v2
Map (Native vs Ramda 0.27.2 vs Lodash 4.17.21) 50k
Comments
Confirm delete:
Do you really want to delete benchmark?