Benchmark: Max value: Lodash vs native array functions

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.10/lodash.min.js'></script>

Script Preparation code:

var values = Array(10000).fill(null).map((x, a) => ({number: a}));
var filterFn = x => !(x.number % 5);
var mapFn = x => x.number;
var reduceFn = (a, b) => a > b ? a : b;

Tests:

Native array functions
const result1 = values.filter(filterFn).map(mapFn).reduce(reduceFn); console.log(result1);
Lodash
const result2 = _(values).filter(filterFn).map(mapFn).max(); console.log(result2);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native array functions
Lodash

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 24 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is comparing two approaches:

1. **Native Array Functions**: Using built-in JavaScript array methods to filter, map, and reduce the `values` array.
2. **Lodash**: Utilizing the Lodash library to perform the same operations on the `values` array.

**Options Compared**

The options being compared are:

* **Filtering**: Using a callback function (`filterFn`) to exclude elements from the filtered array (native) vs using the `filter()` method provided by Lodash (`_(values).filter(filterFn)`).
* **Mapping**: Applying a transformation to each element of the filtered array using a callback function (`mapFn`) (native) vs using the `map()` method provided by Lodash (`_(values).map(mapFn)`).
* **Reducing**: Finding the maximum value in the mapped array using a callback function (`reduceFn`) (native) vs using the `max()` method provided by Lodash (`_(values).max()`).

**Pros and Cons of Each Approach**

**Native Array Functions**

Pros:

* Built-in, so no additional library is required.
* May be faster since it's native code.
* Can be more efficient for large datasets.

Cons:

* Requires manual implementation of the filtering, mapping, and reducing operations.
* Can be less readable and maintainable compared to using a library.

**Lodash**

Pros:

* Provides pre-built functions for common operations like filtering, mapping, and reducing.
* Can simplify code and make it more readable.
* Often faster than native implementations due to optimized C++ code.

Cons:

* Requires an additional library to be included in the project.
* May introduce overhead due to the need to import the library.
* Can be slower compared to native implementations for very large datasets.

**Other Considerations**

* **Caching**: Both approaches can benefit from caching the results of intermediate operations. However, Lodash provides a `memoize()` function that can help with this.
* **Parallelization**: If you need to measure performance across multiple threads or processes, you'll want to consider using Web Workers or a similar approach.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a collection of functions for common tasks like filtering, mapping, reducing, and more. The `filter()`, `map()`, and `max()` methods mentioned in the benchmark are part of Lodash's API. By using these methods, developers can simplify their code and focus on the logic of their application rather than rewriting manual implementations.

**Special JS Feature or Syntax**

There is no special JavaScript feature or syntax used in this benchmark. However, note that some modern browsers support features like `async/await` and `class` for handling promises, which could be relevant when writing tests for other benchmarks.

**Other Alternatives**

If you need to compare performance of native array methods against Lodash's implementation, here are a few alternatives:

* **Underscore.js**: Another popular utility library that provides similar functions as Lodash.
* **Array.prototype.forEach()`: Using the `forEach()` method instead of `filter()`, `map()`, or `reduce()` for the operations being compared.
* **Custom implementations**: Writing your own custom implementations using vanilla JavaScript, which can help to understand the underlying performance characteristics of each approach.

Latest run results:

Run details: (Test run date: 9 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Windows

View result in a separate tab

Test name	Executions per second
Native array functions	12283.7 Ops/sec
Lodash	16563.9 Ops/sec

Related benchmarks:

Max value: Lodash vs native array functions (version: 0)

Compare native

Comparing performance of: Native array functions vs Lodash

Created: 4 years ago by: Registered User

Jump to the latest result

Native array functions

Lodash

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 24 days ago):