Benchmark: Beast ramda leaves no chance for native vs 2

HTML Preparation code:

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

Script Preparation code:

var ramdaCount = R.pipe(
  R.map(R.o(Math.round, R.multiply(50))),
  R.filter(R.modulo(R.__, 2)),
  R.countBy(R.identity)
);

var nativeCount = vals => 
  vals
  	.map(v => Math.round(v * 50))
  	.filter(v => v % 2)
  	.reduce((acc, cur) => { acc[cur] = (acc[cur] | 0) + 1; return acc; }, {});

var nativeRes ={};    
var nativeCount2 = vals =>     
	vals
  	.map(v => Math.round(v * 50))
  	.filter(v => v % 2)   
    .forEach(item => { nativeRes[item] = (nativeRes[item] | 0) + 1 });   

var arr = Array.from({length: 100000}, Math.random);

Tests:

Ramda
ramdaCount(arr)
Native
nativeCount(arr)
Native2
nativeCount2(arr)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Native
Native2

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.

I'll break down the provided benchmark JSON and explain what's being tested, compared, and discussed.

**Benchmark Definition**

The test cases compare three approaches to calculate the count of even numbers in an array:

1. **Ramda (R)**: The `ramdaCount` function uses Ramda, a functional programming library for JavaScript, to perform the calculation.
2. **Native**: The `nativeCount` function implements the same logic without using any external libraries.
3. **Native 2**: A variation of the native approach, where the even count is calculated incrementally using a `forEach` loop.

**Options Compared**

The three options are compared in terms of performance, specifically:

* Time taken to execute each benchmark definition
* Number of executions per second

**Pros and Cons of Each Approach**

1. **Ramda (R)**:
	* Pros: Fast and concise code, leverages Ramda's optimized data processing capabilities.
	* Cons: Requires including an external library, might be unfamiliar to developers without experience with functional programming.
2. **Native**:
	* Pros: No external dependencies, easy to understand and implement for most developers.
	* Cons: May not be as efficient or concise as the Ramda implementation.
3. **Native 2**:
	* Pros: Incremental calculation might improve performance in certain scenarios, but its impact is unclear without specific testing.
	* Cons: Introduces unnecessary complexity with the `forEach` loop, which can negatively affect performance.

**Library Used (Ramda)**

Ramda is a functional programming library for JavaScript that provides a set of higher-order functions for data processing. The `R.pipe`, `R.map`, `R.filter`, and `R.countBy` functions are used to perform the calculation in a concise and expressive way.

In this benchmark, Ramda's optimized implementation of the even count calculation is being compared to the native approach without external libraries.

**Special JS Feature/Syntax**

None mentioned in the provided benchmark definition. The code relies on standard JavaScript features like `Array.from`, `Math.random`, and basic arithmetic operations.

**Alternatives**

Other alternatives for calculating even counts could include:

* Using a library like Lodash or Underscore.js, which offer similar functionality to Ramda.
* Implementing a custom loop-based solution without external libraries.
* Using a Just-In-Time (JIT) compiler like V8's Ahead-of-Time compilation to optimize the native code.

Keep in mind that these alternatives might not be as performant or concise as the Ramda implementation, but they could provide an alternative approach for developers who prefer not to use external libraries.

Related benchmarks:

Beast ramda leaves no chance for native vs 2 (version: 0)

hm

Comparing performance of: Ramda vs Native vs Native2

Created: 5 years ago by: Guest

Jump to the latest result

Ramda

Native

Native2

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):