<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>
var data = Array(1000000).fill({ filtering: true, mapping: 42 });
_.filter(data, 'filtering')
data.filter(({ filtering }) => filtering)
--enable-precise-memory-info
flag.
Test case name | Result |
---|---|
lodash filter | |
native filter |
Test name | Executions per second |
---|---|
lodash filter | 116.4 Ops/sec |
native filter | 116.7 Ops/sec |
Let's break down the provided JSON data and explain what's being tested.
Benchmark Definition
The benchmark is comparing two approaches:
_.filter(data, 'filtering')
data.filter(({ filtering }) => filtering)
Both tests are trying to measure the performance of filtering a large array (data
) that contains 1 million elements.
Options Compared
The benchmark is comparing the speed of two different approaches:
filter
function with a callback that checks for the presence of filtering
in each object.filter
method on the Array
prototype, which uses a similar approach to filter elements based on a predicate function.Pros and Cons
Array.prototype.filter()
, which can make the code harder to read.Library: Lodash
Lodash is a popular JavaScript utility library that provides a set of functional programming helpers, including filtering functions. The _.filter
method is one such helper that allows you to filter arrays based on conditions specified as callback functions.
In this benchmark, Lodash's _.filter
function is used with a simple callback that checks for the presence of filtering
in each object. This approach allows for concise and expressive code but might not be as optimized as native JavaScript code.
Special JS Feature/Syntax
There are no special features or syntaxes being tested here, just standard JavaScript array filtering methods.
Now, let's discuss other alternatives:
Array.prototype.forEach()
with a callback function that pushes elements into a new array, or using Array.prototype.map()
followed by Array.prototype.filter()
. These methods might not be as efficient as the native filter
method but can provide similar results.map
and reduce
functions to filter and process arrays. This would require more code and potentially slower performance compared to native filter
.Keep in mind that the choice of approach ultimately depends on your specific use case, personal preference, and performance requirements.