Benchmark: javascriptTest2403191

Tests:

native
const numbers = [10, 40, 230, 15, 18, 51, 1221] //native numbers.filter(num => num % 3 === 0)
lodash
const numbers = [10, 40, 230, 15, 18, 51, 1221] // lodash _.filter(numbers, num => num % 3 === 0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
native
lodash

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Mobile Safari/537.36

Browser/OS: Chrome Mobile 122 on Android

View result in a separate tab

Test name	Executions per second
native	11362774.0 Ops/sec
lodash	1834273.1 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

MeasureThat.net is a platform for testing JavaScript microbenchmarks. The benchmark you provided involves two test cases, each with a different approach to filter an array of numbers using JavaScript.

**Test Cases**

The two test cases are:

1. **Native**: This test case uses the native `filter()` method without any external libraries or modifications.
2. **Lodash**: This test case uses the Lodash library, specifically the `_filter()` function, to achieve the same result as the native approach.

**Options Compared**

In this benchmark, two options are compared:

* Native: Uses the built-in JavaScript `filter()` method.
* Lodash: Uses the external Lodash library with its `_filter()` function.

**Pros and Cons of Each Approach**

1. **Native (filter())**
	* Pros:
		+ Fastest execution time since it's a native method that doesn't require any external libraries or overhead.
		+ Well-suited for small to medium-sized datasets where the overhead of an external library is negligible.
	* Cons:
		+ May not be as efficient for very large datasets due to the overhead of creating a new array with the filtered elements.
2. **Lodash (_filter())**
	* Pros:
		+ Can handle larger datasets more efficiently since it doesn't require creating a new array, but rather modifies the original array.
		+ Provides additional features and utilities that can be useful in other parts of the codebase.
	* Cons:
		+ Requires an external library (Lodash), which adds overhead and may not be necessary for smaller projects.
		+ May have slower execution times compared to native methods due to the overhead of importing and initializing Lodash.

**Other Considerations**

When choosing between these two approaches, consider the following factors:

* Dataset size: If you're working with very large datasets, the Lodash approach might be more efficient. For smaller datasets, the native approach is likely sufficient.
* Performance-critical code: If your code requires extreme performance and is executed frequently, using native methods like `filter()` might be a better choice.
* Codebase complexity: If you need to use multiple features from Lodash, it might be worth considering the added complexity and overhead.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a collection of functional programming helpers. The `_filter()` function in Lodash takes two arguments: an array and a callback function that specifies the filtering criteria. In this benchmark, Lodash's `_filter()` function is used to filter the `numbers` array.

**Special JS Feature or Syntax**

None mentioned in the provided code snippets.

Related benchmarks:

javascriptTest2403191 (version: 0)

Comparing performance of: native vs lodash

Created: 2 years ago by: Guest

Jump to the latest result

native

lodash

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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