Benchmark: Lodash.filter vs Lodash.without vs array.filter

HTML Preparation code:

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

Script Preparation code:

var arr = Array.from(Array(100000).keys())

Tests:

filter
_.filter(arr, o => o !== 99999);
without
_.without(arr, 99999);
array.filter
arr.filter(o => o !== 99999);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
without
array.filter

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/119.0

Browser/OS: Firefox 119 on Ubuntu

View result in a separate tab

Test name	Executions per second
filter	1153.4 Ops/sec
without	1219.1 Ops/sec
array.filter	1148.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested, compared, and the pros and cons of each approach.

**Benchmark Overview**

The benchmark compares three approaches to filter an array in JavaScript:

1. `_.filter` (Lodash)
2. `_.without` (Lodash)
3. `array.filter` (native JavaScript)

**Comparison of Approaches**

### 1. Lodash _.filter

* Purpose: Filter elements from an array based on a provided function.
* Code example: `_.filter(arr, o => o !== 99999)`
* Library: Lodash is a popular utility library for JavaScript.

Pros:

* Convenient and concise syntax
* Often provides additional features and optimizations out of the box (e.g., handling null/undefined values)

Cons:

* Adds extra overhead due to the library's dependencies and optimizations.
* May not be as efficient as native JavaScript for simple filtering operations.

### 2. Lodash _.without

* Purpose: Remove elements from an array by their presence in another array or value.
* Code example: `_.without(arr, 99999)`
* Library: Lodash is a popular utility library for JavaScript.

Pros:

* Similar to `_.filter`, but with a different approach and syntax.
* May be optimized differently than `_.filter` due to the underlying algorithm used.

Cons:

* Adds extra overhead due to the library's dependencies and optimizations, similar to `_filter`.

### 3. array.filter (native JavaScript)

* Purpose: Filter elements from an array based on a provided function.
* Code example: `arr.filter(o => o !== 99999)`
* No external libraries required.

Pros:

* Lightweight and efficient, with minimal overhead.
* Optimized for performance by being native to JavaScript.

Cons:

* Requires more verbose syntax compared to Lodash functions.
* May not provide additional features or optimizations out of the box.

**Test Results**

The latest benchmark results show that:

* `_.without` performed best with an average execution rate of 1153.4171142578125 executions per second on Firefox 119.
* `array.filter` performed closest to `_.without`, with an average execution rate of 1148.49462890625 executions per second on the same browser and device platform.

The results suggest that both Lodash functions provide similar performance, while the native JavaScript `array.filter` approach is slightly less efficient but still relatively fast.

**Other Alternatives**

For filtering arrays in JavaScript, other approaches exist:

* Using `Array.prototype.filter()` (native JavaScript): Similar to `array.filter`, but with a more concise syntax.
* Using `filter()` from `underscore.js`: Another utility library that provides an alternative implementation of the filtering algorithm.
* Implementing your own custom filtering function using a simple loop or recursive approach.

Keep in mind that performance differences between these approaches may vary depending on specific use cases and requirements.

Related benchmarks:

Lodash.filter vs Lodash.without vs array.filter (version: 0)

Comparing performance of: filter vs without vs array.filter

Created: one year ago by: Guest

Jump to the latest result

filter

without

array.filter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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