Benchmark: filter Lodash vs Vanilla

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js"></script>

Script Preparation code:

var data = [
  {a: 'olo', b: 'trolo'},
  {a: 'trolo', b: 'sdf'},
  {a: 'olo', b: 'ewr'},
  {a: 'olo', b: 'dfgdf'},
  {a: 'trolo', b: 'trolo'},
]

Tests:

Lodash
var result = _.filter(data, (o) => o.a === 'olo')
Vanilla
var result = data.filter( (o) => o.a === 'olo')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Vanilla

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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Let's break down the provided JSON and explain what's being tested.

**Benchmark Definition**

The benchmark is comparing two approaches to filter data in JavaScript:

1. **Vanilla**: Using only built-in JavaScript methods, specifically `filter()` method.
2. **Lodash**: Using the popular utility library Lodash, which provides a wide range of functions for tasks like filtering data.

**Script Preparation Code**

The script preparation code defines an array `data` containing several objects with properties `a` and `b`. The purpose of this code is to create a sample dataset that will be used in the benchmarking process.

**Html Preparation Code**

The HTML preparation code includes a script tag that loads the Lodash library from a CDN (Content Delivery Network). This library is not included by default in every browser, so it's necessary to load it explicitly for the benchmarking process.

**Individual Test Cases**

There are two test cases:

1. **Lodash**: The first test case uses the `filter()` method from Lodash, passing an arrow function as a callback to filter out objects where property `a` is not equal to `'olo'`.
2. **Vanilla**: The second test case uses only built-in JavaScript methods, specifically the `filter()` method, with a similar arrow function callback.

**Comparison**

The benchmark compares the performance of these two approaches on the prepared dataset. The results will show which approach is faster and more efficient for filtering data in JavaScript.

**Pros and Cons**

Here are some pros and cons for each approach:

**Lodash (Vanilla)**

Pros:

* Wide range of utility functions, including `filter()`
* Simplifies code and reduces boilerplate
* Well-maintained and widely used library

Cons:

* Adds external dependency (Lodash)
* May introduce performance overhead due to additional function calls

**Vanilla**

Pros:

* No external dependencies or overhead
* Fast and efficient built-in method
* Simple and straightforward implementation

Cons:

* Requires explicit `filter()` method usage, which can be error-prone
* May not provide the same level of abstraction as Lodash

**Other Considerations**

Other factors that may affect benchmark results include:

* Browser and device compatibility: Different browsers or devices might have varying performance characteristics for each approach.
* Data size and complexity: Larger datasets or more complex filtering conditions can impact performance.
* JavaScript engine optimizations: Some JavaScript engines, like V8 in Chrome, are optimized for specific use cases, which may influence benchmark results.

**Special JS Features/Syntax**

There are no special JS features or syntax used in this benchmarking setup. The focus is on comparing the performance of two simple filtering approaches using built-in methods and a utility library.

**Alternatives**

Other alternatives to Lodash or vanilla `filter()` method include:

* Other JavaScript libraries, such as Ramda or Immutable.js
* In-house implementation of filtering functions
* Browser-specific optimizations or plugins

Keep in mind that the choice of approach depends on the specific use case, performance requirements, and personal preference.

Related benchmarks:

filter Lodash vs Vanilla (version: 0)

Comparing performance of: Lodash vs Vanilla

Created: 6 years ago by: Registered User

Jump to the latest result

Lodash

Vanilla

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):