Benchmark: Lodash.js filter vs Native

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Script Preparation code:

var max1 = 100000; // 100,000 (100 Thousand)
var max2 = 10000000; // 10,000,000 (10 Million)
var max3 = 100000000; // 100,000,000 (100 Million)

var arr1 = [];
//for (var i = 0; i <= max1; i++) { arr1.push(i); }

var arr2 = [];
for (var i = 0; i <= max2; i++) { arr2.push(i); }

var arr3 = [];
//for (var i = 0; i <= max3; i++) { arr3.push(i); }

Tests:

Native
arr2.filter((x) => x % 2 === 0)
Lodash.js filter
_.filter(arr2, (x) => x % 2 === 0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native
Lodash.js filter

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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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 benchmarks!

The provided JSON represents a benchmark test for comparing the performance of native JavaScript filtering against Lodash.js filtering.

**Benchmark Definition:**

The benchmark definition consists of two individual test cases:

1. `arr2.filter((x) => x % 2 === 0)` - This is a native JavaScript filtering function that takes an array `arr2` and returns a new array containing only the even numbers.
2. `_.filter(arr2, (x) => x % 2 === 0)` - This is a Lodash.js filtering function that takes an array `arr2` and returns a new array containing only the even numbers.

**Options Compared:**

The benchmark compares two options:

1. **Native JavaScript Filtering**: The native JavaScript filtering approach uses the built-in `filter()` method, which iterates over the elements of the array and executes the provided callback function for each element.
2. **Lodash.js Filtering**: The Lodash.js filtering approach uses the `_filter()` function from the Lodash library, which provides a higher-level abstraction for filtering arrays.

**Pros and Cons:**

1. **Native JavaScript Filtering**:
	* Pros:
		+ Native performance, as it doesn't require an external library.
		+ Simplistic implementation, making it easy to understand and optimize.
	* Cons:
		+ Can be slower due to the overhead of iterating over elements in the array.
		+ May not perform well for large datasets or complex filtering logic.
2. **Lodash.js Filtering**:
	* Pros:
		+ Provides a higher-level abstraction, making it easier to write concise and readable code.
		+ Often optimized for performance, especially for common filtering patterns.
	* Cons:
		+ Requires an external library, which can introduce overhead and dependencies.
		+ May not be suitable for all use cases or performance-critical applications.

**Library:**

In this benchmark, the Lodash.js library is used. Lodash.js is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, object manipulation, string manipulation, and more.

The `_filter()` function in Lodash.js takes an array as input and returns a new array containing only the elements that pass the provided predicate (function). In this case, the predicate is `(x) => x % 2 === 0`, which filters out odd numbers from the array.

**Special JS Feature/Syntax:**

There are no special JavaScript features or syntax mentioned in the benchmark. The code uses standard JavaScript syntax and does not include any experimental features like async/await, destructuring, or template literals.

**Alternatives:**

If you're interested in exploring alternative filtering approaches, here are a few options:

1. **Array.prototype.map()**: Instead of using `filter()`, you could use the `map()` method to create a new array with only the even numbers. This approach can be faster for large datasets, but it requires creating a new array and may not be suitable for all use cases.
2. **Set data structure**: You could use a Set data structure to store unique elements from the array, which would eliminate duplicates and potentially improve performance.
3. **Native Web Workers**: For extremely performance-critical applications, you could consider using native Web Workers to offload filtering tasks to a separate thread, reducing the load on the main thread.

These alternatives may offer better performance or different trade-offs for specific use cases, but they also introduce additional complexity and considerations.

Related benchmarks:

Lodash.js filter vs Native (version: 0)

Comparing performance of: Native vs Lodash.js filter

Created: 4 years ago by: Guest

Jump to the latest result

Native

Lodash.js filter

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):