Benchmark: Ramda beats native x10

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.27.1/ramda.min.js"></script>

Script Preparation code:

const MULTIPLIER = 1e5

var ramdaF = R.pipe(
    R.map(R.o(Math.round, R.multiply(MULTIPLIER))),
    R.filter(R.modulo(R.__, 2))
)

var nativeF = vals =>
    vals
    .map(v => Math.round(v * MULTIPLIER))
    .filter(v => v % 2)

var arr = Array.from({
    length: 50000
}, Math.random)

const ramdaRes = ramdaF(arr)
const nativeRes = nativeF(arr)

if (!R.equals(ramdaRes, nativeRes)) {
    throw new Error('Different results!')
}

Tests:

Ramda - Count
ramdaF(arr)
Native - Count
nativeF(arr)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda - Count
Native - Count

Fastest: N/A

Slowest: N/A

Latest 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 break down the provided benchmark and its components.

**Benchmark Definition**

The benchmark is designed to compare the performance of two approaches: Ramda (a functional programming library) and native JavaScript.

In this specific benchmark, we have a simple operation: filtering an array of random numbers and rounding each number to the nearest integer. The goal is to count how many elements pass this filter.

**Script Preparation Code**

The script preparation code defines two functions:

* `ramdaF`: This function uses Ramda's `pipe` function to create a pipeline that:
	+ Maps each element in the array to its rounded value multiplied by a large constant (`MULTIPLIER = 1e5`) using `R.multiply`.
	+ Filters out elements that are not congruent to 2 modulo 3 (i.e., odd numbers) using `R.filter` and `R.modulo`.
* `nativeF`: This function performs the same operation as `ramdaF`, but without using any external libraries. It uses standard JavaScript `map`, `filter`, and arithmetic operations.

**Html Preparation Code**

The HTML preparation code includes a script tag that loads the Ramda library from a CDN. This is necessary because `ramdaF` relies on Ramda's functions, which are not built into native JavaScript.

**Individual Test Cases**

We have two test cases:

1. "Ramda - Count": This test case measures the performance of `ramdaF`.
2. "Native - Count": This test case measures the performance of `nativeF`.

**Options Compared**

The benchmark is comparing two options:

1. Using Ramda (a functional programming library) for array filtering and transformation.
2. Performing the same operation using native JavaScript.

**Pros and Cons**

**Ramda:**

* Pros:
	+ Higher-level abstraction can lead to more concise code, which might reduce development time.
	+ Functional programming paradigm provides a structured approach to data transformations.
* Cons:
	+ Additional overhead due to loading an external library.
	+ Might introduce performance overhead compared to native JavaScript.

**Native JavaScript:**

* Pros:
	+ No additional overhead due to loading an external library.
	+ Can be optimized for specific use cases or environments.
* Cons:
	+ Requires more code and manual manipulation of data structures.
	+ Might lead to more complex error handling and debugging.

**Library Used (Ramda)**

Ramda is a popular JavaScript functional programming library that provides a set of reusable functions for common tasks, such as array transformations, filtering, and mapping. Its purpose is to abstract away low-level details and provide a higher-level abstraction for developers to focus on the logic of their application.

**Special JS Feature/Syntax**

This benchmark does not use any special JavaScript features or syntax. It relies solely on standard JavaScript concepts, such as functions, arrays, and conditional statements.

**Other Alternatives**

If you were to rewrite this benchmark without using Ramda, you might consider alternative libraries like Lodash or other functional programming frameworks. Alternatively, you could focus on optimizing the native JavaScript implementation for better performance.

For a more comprehensive understanding of the benchmark's performance characteristics, you can explore factors such as:

* Cache behavior and optimization
* Loop unrolling and parallelization
* Memory allocation and garbage collection
* Platform-specific optimizations (e.g., SIMD instructions)

Keep in mind that the specific details may vary depending on the version of Ramda and the target JavaScript environment.

Related benchmarks:

Ramda beats native x10 (version: 0)

hm....

Comparing performance of: Ramda - Count vs Native - Count

Created: 5 years ago by: Guest

Jump to the latest result

Ramda - Count

Native - Count

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):