Benchmark: Beast ramda leaves no chance for native - 1

HTML Preparation code:

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

Script Preparation code:

const MULTIPLIER = 5000

var ramdaCount = R.pipe(
  R.map(R.o(Math.round, R.multiply(MULTIPLIER))),
  R.filter(R.modulo(R.__, 2)),
  R.countBy(R.identity)
)

var ramdaConcatTimes = R.map(R.o(R.concat(R.__, ' times'), String))

var nativeCount = vals => 
  vals
  	.map(v => Math.round(v * MULTIPLIER))
  	.filter(v => v % 2)
  	.reduce((acc, cur) => { acc[cur] = (acc[cur] | 0) + 1; return acc }, {})

var nativeConcatTimes = counts => 
	Object
		.keys(counts)
		.reduce((acc, key) => { acc[key] = String(counts[key]) + ' times'; return acc }, {})

var nativeConcatTimesUsingEntries = counts => 
	Object.fromEntries(
		Object
			.entries(counts)
      		.map(([key, val]) => [key, String(val) + ' times'])
    )

var arr = Array.from({length: 10000}, Math.random)

const ramdaRes = ramdaConcatTimes(ramdaCount(arr))
const nativeRes = nativeConcatTimes(nativeCount(arr))
const otherNativeRes = nativeConcatTimesUsingEntries(nativeCount(arr))

if (!R.equals(ramdaRes, nativeRes) || !R.equals(nativeRes, otherNativeRes)) {
  throw new Error('different results')
}

Tests:

Ramda - Count
ramdaCount(arr)
Native - Count
nativeCount(arr)
Ramda - Count and map object
R.o(ramdaConcatTimes, ramdaCount)(arr)
Native - Count and map object
nativeConcatTimes(nativeCount(arr))
Native - Count and map object using entries
nativeConcatTimesUsingEntries(nativeCount(arr))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda - Count
Native - Count
Ramda - Count and map object
Native - Count and map object
Native - Count and map object using entries

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 11_2_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.192 Safari/537.36

Browser/OS: Chrome 88 on Mac OS X 11.2.1

View result in a separate tab

Test name	Executions per second
Ramda - Count	1661.0 Ops/sec
Native - Count	460.9 Ops/sec
Ramda - Count and map object	1226.5 Ops/sec
Native - Count and map object	355.1 Ops/sec
Native - Count and map object using entries	329.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**What is being tested?**

The provided benchmark tests the performance of two approaches for counting elements in an array: Ramda's `R.pipe` and `R.countBy`, versus native JavaScript implementations using `Array.prototype.filter()` and `Object.reduce()`. Additionally, three variations are compared:

1. Ramda's `R.map` with a callback function that concatenates strings to form output.
2. Native JavaScript implementation using `Array.prototype.concat()` and `String`.
3. Native JavaScript implementation using `Object.fromEntries()`.

**Options compared**

The options being compared are:

* **Ramda**: uses the Ramda library for functional programming. Specifically:
	+ `R.pipe`: a higher-order function that composes multiple functions.
	+ `R.map`: applies a function to each element of an array.
	+ `R.countBy`: counts the occurrences of each unique value in an array.
* **Native JavaScript**: uses built-in JavaScript methods for looping and counting:
	+ `Array.prototype.filter()`: filters elements based on a condition.
	+ `Array.prototype.reduce()`: reduces an array to a single value.
	+ `String` concatenation: concatenates strings using the `+` operator.

**Pros and Cons**

Here's a brief summary of each approach:

* **Ramda**:
	+ Pros:
		- Declarative, functional programming style.
		- Composability and reusability of functions.
	+ Cons:
		- May require more boilerplate code.
		- Can be slower due to overhead of function calls.
* **Native JavaScript**:
	+ Pros:
		- Faster execution speed (typically).
		- Fewer dependencies (no external library required).
	+ Cons:
		- Less declarative, imperative programming style.
		- More verbose code.

**Other considerations**

1. **Memory usage**: Ramda's `R.pipe` and `R.countBy` might be more memory-efficient due to lazy evaluation.
2. **Error handling**: Native JavaScript implementations typically require explicit error handling using `try-catch` blocks or other mechanisms.

**Library descriptions**

* **Ramda**: a popular functional programming library for JavaScript, providing a set of reusable functions for common data processing tasks.
* **Object.fromEntries()**: a method introduced in ECMAScript 2015 (ES6) that creates an object from key-value pairs.

Now, regarding special JavaScript features or syntax:

* The benchmark uses ES6+ syntax, including `const`, `let`, `Arrow functions` (`=>`), and template literals (`\r\n`).
* No other special JavaScript features are mentioned.

**Alternatives**

Other libraries or implementations that could be used for this benchmark include:

1. Lodash (a popular utility library with functional programming capabilities)
2. TypeScript (with its type system and compiler optimizations)
3. Other ES6+ compliant JavaScript engines, such as SpiderMonkey or V8

Keep in mind that these alternatives might not necessarily provide different results, but rather demonstrate alternative implementations of the same functionality.

This concludes our in-depth analysis of the provided benchmark!

Related benchmarks:

Beast ramda leaves no chance for native - 1 (version: 0)

hm....

Comparing performance of: Ramda - Count vs Native - Count vs Ramda - Count and map object vs Native - Count and map object vs Native - Count and map object using entries

Created: 3 years ago by: Guest

Jump to the latest result

Ramda - Count

Native - Count

Ramda - Count and map object

Native - Count and map object

Native - Count and map object using entries

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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