Benchmark: Lodash filter VS native filter (with Lodash actually loaded)

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 = [];

for (i = 0; i < 1000; i++) {
  arr[i] = i;
}
_.map([], function() { return false });

Tests:

Lodash
var lodashResult = _.filter(arr, function(i) { return i % 2 === 0 });
Native reduce
var nativeResult = arr.filter(function(i) { return i % 2 === 0 });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Native reduce

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36

Browser/OS: Chrome 128 on Windows

View result in a separate tab

Test name	Executions per second
Lodash	171197.1 Ops/sec
Native reduce	539735.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks and explain what's being tested on MeasureThat.net.

The benchmark is designed to compare the performance of two approaches: using the Lodash library for filtering arrays, versus implementing a native filter function without any external libraries.

**Options compared:**

1. **Lodash Filter**: The first test case uses the `_.filter()` method from the Lodash library to filter an array. This approach relies on the library's implementation and optimizations.
2. **Native Filter**: The second test case implements a native filter function using JavaScript's built-in `Array.prototype.filter()` method.

**Pros and Cons:**

1. **Lodash Filter**:
	* Pros: Easy to use, well-tested, and optimized for performance by Lodash's maintainers.
	* Cons: Requires including an external library, which may lead to slower startup times or additional payload.
2. **Native Filter**:
	* Pros: No external libraries are required, reducing overhead and potential security risks.
	* Cons: May require more manual optimization and tweaking for optimal performance.

When considering these options, software engineers should weigh the trade-offs between ease of use, performance, and library dependencies. If ease of use is crucial, Lodash's optimized implementation might be a better choice. However, if minimizing external libraries and optimizing performance manually is essential, the native filter approach might be preferred.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks like array manipulation, object traversal, and more. In this benchmark, Lodash's `filter()` method is used to implement the filtering logic. The library is designed to be efficient and performant, but it does introduce an external dependency that may affect performance.

**Special JS feature/syntax: None**

There are no specific JavaScript features or syntaxes being tested in this benchmark. Both test cases use standard JavaScript features like array methods and functions.

To achieve better performance with the native filter approach, software engineers can consider optimizing the implementation by:

* Using techniques like loop unrolling or caching to reduce overhead.
* Applying parallel processing or concurrency to take advantage of multi-core processors.
* Experimenting with different data structures or algorithms for filtering.

**Alternatives:**

Other alternatives for implementing filters in JavaScript include:

1. **Array.prototype.every()` and `Array.prototype.some()`: These methods can be used as an alternative to `Array.prototype.filter()`, but they may have slightly different performance characteristics due to their implementation.
2. **Map-based approach**: Some implementations use a Map data structure to store the filtered elements, which can lead to better cache locality and performance.

However, these alternatives might require more complex code and optimizations to achieve comparable performance with Lodash's filter method or the native filter approach.

Related benchmarks:

Lodash filter VS native filter (with Lodash actually loaded) (version: 0)

Comparing performance of: Lodash vs Native reduce

Created: one year ago by: Guest

Jump to the latest result

Lodash

Native reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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