<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>
function double(n) {
return n*2;
}
var data = [Array(20)].map((v, idx) => idx);
R.compose(R.map(double), R.map(double), R.map(double), R.map(double))(data);
data.map(double).map(double).map(double).map(double);
_(data).chain(_.map(double),_.map(double),_.map(double),_.map(double)).value();
--enable-precise-memory-info
flag.
Test case name | Result |
---|---|
Ramda | |
Array (native) | |
Lodash |
Test name | Executions per second |
---|---|
Ramda | 253825.9 Ops/sec |
Array (native) | 1533706.9 Ops/sec |
Lodash | 771794.9 Ops/sec |
Let's break down the benchmark and its components.
Benchmark Definition
The test measures the speed of three different approaches to apply a transformation function (double(n)
) to an array of 20 elements:
map
operations.map
operations directly on the native JavaScript array using the map()
method.map
operations.Options Compared
The benchmark compares three different approaches:
map
function, which is a higher-order function that takes a transformation function and an array as input, returning a new array with the transformed values.map()
method, which applies a given function to each element of an array and returns a new array with the results.map
function, which is similar to Ramda's but with additional features like support for iteratees (functions that take multiple arguments) and a more comprehensive set of utility functions.Pros and Cons
Here are some pros and cons of each approach:
Libraries
The benchmark uses the following libraries:
map
function is used to apply a transformation function to each element of an array.map
function is used to apply a transformation function to each element of an array.JavaScript Features
The benchmark does not use any special JavaScript features or syntax beyond the standard language. It only relies on modern JavaScript capabilities like arrays, function composition, and higher-order functions.
Alternatives
Other alternatives for data transformation and array operations include:
These alternatives may offer different trade-offs in terms of performance, conciseness, and compatibility, depending on the specific use case and requirements.