Benchmark: ramda-rubico-transducer

HTML Preparation code:

<script src="https://rawgit.com/ramda/ramda/master/dist/ramda.js"></script>
<script src="https://unpkg.com/rubico@1.6.21/dist/rubico.min.js"></script>

Script Preparation code:

var ids = Array.from({ length: 10000 }, (_, index) => index + 1)
var isPositive = i => i % 2 === 0

Tests:

Array(map + filter)
ids.map(R.identity).filter(isPositive).map(R.inc)
ramda-transducer(map + filter)
const transform = R.pipe( R.map(R.identity), R.filter(isPositive), R.map(R.inc) ) R.transduce( transform, R.flip(R.append), [], ids, );
ramda (map + filter)
R.pipe( R.map(R.identity), R.filter(isPositive), R.map(R.inc) )(ids)
rubico (map + filter)
rubico.pipe([ rubico.map(R.identity), rubico.filter(isPositive), rubico.map(R.inc), ])(ids)
rubico transducers
rubico.transform(rubico.pipe([ rubico.map(R.identity), rubico.filter(isPositive), rubico.map(R.inc), ]), [])(ids)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 15_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.3 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 15 on iOS 15.3

View result in a separate tab

Test name	Executions per second
Array(map + filter)	3834.9 Ops/sec
ramda-transducer(map + filter)	23.6 Ops/sec
ramda (map + filter)	2351.8 Ops/sec
rubico (map + filter)	1000.0 Ops/sec
rubico transducers	776.9 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Explanation**

The provided JSON represents a JavaScript microbenchmarking test, where users can compare the performance of different approaches to perform array operations (map and filter) on large datasets.

**Options Compared**

The benchmark compares four options:

1. **Native Array Method**: The native way to perform map and filter operations on an array using `Array.prototype.map` and `Array.prototype.filter`.
2. **Ramda Map and Filter**: Ramda is a functional programming library that provides a concise way to perform array operations using its `map`, `filter`, and other functions.
3. **Ramda Transducer with Flip**: Ramda's transducer pipeline, which uses `R.transduce` to apply a series of functions to an array. In this case, the pipeline includes `R.map(R.identity)`, `R.filter(isPositive)`, and `R.map(R.inc)`.
4. **Rubico Map and Filter**: Rubico is another functional programming library that provides a similar API to Ramda for array operations.
5. **Rubico Transducer with No Flip**: Rubico's transducer pipeline, similar to the Ramda version, but without using `R.flip(R.append)` as the initial function.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Native Array Method**:
	* Pros: Native performance, no dependencies.
	* Cons: Verbosity, less readable code.
2. **Ramda Map and Filter**:
	* Pros: Concise code, easy to read, and maintain.
	* Cons: Dependency on Ramda library, potential overhead due to function calls.
3. **Ramda Transducer with Flip**:
	* Pros: Optimized pipeline for performance, easy to understand and maintain.
	* Cons: Dependent on Ramda library, potential complexity if not familiar with transducers.
4. **Rubico Map and Filter**:
	* Pros: Similar API to Ramda, concise code, easy to read.
	* Cons: Dependency on Rubico library, potential overhead due to function calls.
5. **Rubico Transducer with No Flip**:
	* Pros: Optimized pipeline for performance, similar to Ramda's transducer.
	* Cons: Dependent on Rubico library, potential complexity if not familiar with transducers.

**Library Descriptions**

1. **Ramda**: A functional programming library that provides a concise way to perform array operations and other data transformations using its `map`, `filter`, and other functions.
2. **Rubico**: Another functional programming library that provides a similar API to Ramda for array operations, with some performance optimizations.

**Special JS Features**

None mentioned in the provided benchmark definition.

**Benchmark Preparation Code**

The provided JSON includes two scripts:

1. `Script Preparation Code`: Creates an array of 10,000 elements and defines a function `isPositive` that checks if a number is even.
2. `Html Preparation Code`: Includes links to Ramda and Rubico JavaScript libraries.

**Alternatives**

If you're interested in exploring alternative approaches for array operations, consider:

1. **Lodash**: A popular utility library that provides a wide range of functions for working with arrays and objects.
2. **Lo-Dash**: Another functional programming library that offers a similar API to Ramda and Lodash for array operations.
3. **Preact**: A lightweight JavaScript library that aims to improve performance by avoiding unnecessary DOM updates.
4. **Tailwind CSS**: A utility-first CSS framework that can be used to optimize performance in web development.

These alternatives might not offer the same concise code as Ramda or Rubico, but they can provide different trade-offs in terms of performance, readability, and maintainability.

Related benchmarks:

ramda-rubico-transducer (version: 0)

Array(map + filter) vs ramda(map + filter) vs ramda-transducer(map + filter) vs rubico-transducer vs rubico(map + filter)

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

Created: 3 years ago by: Registered User

Jump to the latest result

Array(map + filter)

ramda-transducer(map + filter)

ramda (map + filter)

rubico (map + filter)

rubico transducers

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):