Benchmark: ramda-transducer

HTML Preparation code:

<script src="https://rawgit.com/ramda/ramda/master/dist/ramda.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(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.transduce( transform, R.flip(R.append), [], 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

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks and analyze what's being tested on MeasureThat.net.

**Benchmark Definition**

The benchmark is based on the Ramda library, which provides functional programming utilities for JavaScript. The script preparation code includes a simple array of 10,000 numbers from 1 to 10000, along with two functions: `isPositive` that checks if a number is even, and `R.identity`, `R.map`, `R.filter`, and `R.inc` which are Ramda's functional programming helpers.

**Test Cases**

There are three test cases:

1. **"Array(map + filter)"**: This test case uses the `map` and `filter` methods directly on the array, without any additional context or transformation.
2. **"ramda(map + filter)"**: This test case uses Ramda's `map` and `filter` functions to perform the same operation as the previous one, but with the added benefit of functional programming abstractions.
3. **"ramda-transducer(map + filter)"**: This test case uses Ramda's `transduce` function, which applies a transformation (in this case, the same `map` and `filter` operations) to an array while accumulating results in a separate data structure.

**Options Compared**

The three test cases compare different approaches to performing the same operation:

* **Direct Array Method**: Uses native JavaScript methods (`map` and `filter`) on the original array.
* **Ramda Map + Filter**: Uses Ramda's functional programming helpers (`R.map`, `R.filter`, etc.) to perform the same operation, with added abstraction and potential performance benefits.
* **Ramda Transducer**: Uses Ramda's `transduce` function to apply a transformation to an array while accumulating results in a separate data structure. This approach can provide better performance and handling of large datasets.

**Pros and Cons**

Here are some pros and cons for each approach:

1. **Direct Array Method**:
	* Pros: Simple, easy to understand, and widely supported.
	* Cons: May be slower due to the overhead of native JavaScript methods, and may not handle large datasets well.
2. **Ramda Map + Filter**:
	* Pros: Provides functional programming abstractions that can simplify code and reduce errors, may perform better than direct array method due to optimized C++ implementation.
	* Cons: Requires additional dependencies (Ramda library) and may have a steeper learning curve for developers unfamiliar with functional programming concepts.
3. **Ramda Transducer**:
	* Pros: Can provide better performance and handling of large datasets compared to the other approaches, can be more efficient when dealing with complex data transformations.
	* Cons: Requires additional dependencies (Ramda library) and may have a steeper learning curve for developers unfamiliar with functional programming concepts.

**Library and Syntax**

The `R` prefix in Ramda's functions (`R.map`, `R.filter`, etc.) refers to the Ramda namespace. The Ramda library provides a set of functional programming helpers that can simplify code and improve performance.

There are no special JavaScript features or syntax used in these test cases, just standard JavaScript with native methods and Ramda's functional programming helpers.

**Alternatives**

Other alternatives for performing similar operations include:

* Using native JavaScript methods (`map`, `filter`, etc.) without any additional libraries.
* Using other functional programming libraries like Lodash or Underscore.js.
* Implementing custom iterative solutions to perform the same operation.

However, it's worth noting that Ramda's transducer approach can provide better performance and handling of large datasets compared to other alternatives, especially when dealing with complex data transformations.

Latest run results:

Run details: (Test run date: 7 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:124.0) Gecko/20100101 Firefox/124.0

Browser/OS: Firefox 124 on Windows

View result in a separate tab

Test name	Executions per second
Array(map + filter)	462.5 Ops/sec
ramda(map + filter)	450.4 Ops/sec
ramda-transducer(map + filter)	11.0 Ops/sec

Related benchmarks:

ramda-transducer (version: 0)

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

Created: 4 years ago by: Registered User

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 26 days ago):