Benchmark: ramda-transducer-into-million

HTML Preparation code:

<script src="https://rawgit.com/ramda/ramda/master/dist/ramda.js"></script>

Script Preparation code:

var ids = Array.from({ length: 1000000 }, (_, index) => index + 1)
var isPositive = i => i % 2 === 0

Tests:

Array(map + filter)
ids.map(R.identity).filter(isPositive).map(R.inc)
ramda(map + filter)
R.pipe( R.map(R.identity), R.filter(isPositive), R.map(R.inc) )(ids)
ramda-transducer(map + filter)
const transform = R.pipe( R.map(R.identity), R.filter(isPositive), R.map(R.inc) ) R.into( [], transform, ids, );

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array(map + filter)
ramda(map + filter)
ramda-transducer(map + filter)

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Safari/537.36

Browser/OS: Chrome 87 on Windows

View result in a separate tab

Test name	Executions per second
Array(map + filter)	6.7 Ops/sec
ramda(map + filter)	16.6 Ops/sec
ramda-transducer(map + filter)	11.3 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and the pros/cons of different approaches.

**Benchmark Overview**

The benchmark is designed to compare three approaches for mapping over an array while filtering out certain elements:

1. **Array.map + filter**: Using the native JavaScript `map` and `filter` methods.
2. **R.pipe (ramda)**: Utilizing the Ramda library's `pipe` function to compose multiple functions together.
3. **R.into (ramda-transducer)**: Using the ramda-transducer library's `into` function, which is a transducer that accumulates values.

**Benchmark Definitions**

The benchmark definitions are:

1. `ids.map(R.identity).filter(isPositive).map(R.inc)`:
	* This uses native JavaScript `map`, `filter`, and `map` methods.
2. `(R.pipe(R.map(R.identity), R.filter(isPositive), R.map(R.inc)))(ids)`:
	* This uses Ramda's `pipe` function to compose multiple functions together: `identity`, `filter`, and `inc`.
3. `const transform = R.pipe(R.map(R.identity), R.filter(isPositive), R.map(R.inc));\nR.into([], transform, ids, );`:
	* This uses ramda-transducer's `into` function as a transducer to accumulate values while transforming the input array.

**Comparison and Pros/Cons**

Here's a brief comparison of the three approaches:

1. **Array.map + filter**: Native JavaScript methods are typically faster and more efficient since they don't require additional library overhead.
	* Pros: Fast, lightweight, and widely supported.
	* Cons: May not be as concise or expressive as other approaches.
2. **R.pipe (ramda)**: Ramda's `pipe` function provides a functional programming style for composing functions together.
	* Pros: Concise, readable, and can lead to more efficient code due to function composition.
	* Cons: Requires additional library overhead, may not be as performant as native methods.
3. **R.into (ramda-transducer)**: ramda-transducer's `into` function is a transducer that accumulates values while transforming the input array.
	* Pros: Can lead to more efficient code due to accumulator mechanism, concise and readable syntax.
	* Cons: Requires additional library overhead, may not be as performant as native methods.

**Library and Syntax**

* **Ramda**: A functional programming library for JavaScript that provides a set of utility functions. The `pipe` function is used to compose multiple functions together.
* **ramda-transducer**: A transducer library for Ramda that extends its functionality with accumulator mechanisms.
* **JavaScript ES6 syntax**: Used in the benchmark script preparation code, which allows for concise and expressive syntax.

**Device Platform and Browser**

The latest benchmark results are from a Chrome 87 browser on a Windows Desktop device platform. The `ExecutionsPerSecond` value indicates how many executions of each benchmark definition were completed per second.

**Other Alternatives**

For mapping over arrays with filtering, other alternatives to the above approaches include:

* Using native JavaScript's `forEach` method in combination with array methods like `filter` and `map`.
* Utilizing libraries like Lodash or Underscore.js for functional programming utilities.
* Implementing custom accumulator mechanisms using loops or recursive functions.

These alternatives may offer varying levels of performance, readability, and conciseness, depending on the specific use case.

Related benchmarks:

ramda-transducer-into-million (version: 0)

Using push instead of append

Comparing performance of: Array(map + filter) vs ramda(map + filter) vs ramda-transducer(map + filter)

Created: 3 years ago by: Guest

Jump to the latest result

Array(map + filter)

ramda(map + filter)

ramda-transducer(map + filter)

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):