Benchmark: ramda-transducer-push

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, (xs, x) => (xs.push(x), xs), [], 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 25 days ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided JSON represents a benchmark for testing the performance of different approaches for transforming arrays using the Ramda library.

**Script Preparation Code**

The script preparation code includes two variables:
```javascript
var ids = Array.from({ length: 10000 }, (_, index) => index + 1)
var isPositive = i => i % 2 === 0
```
Here, `ids` is an array of 10,000 elements, each starting from 1. The `isPositive` function checks whether a number is even (i.e., divisible by 2).

**Benchmark Definition**

The benchmark definition consists of three test cases:

1. **Array(map + filter)**: This test case uses the native JavaScript `map()` and `filter()` methods to transform the `ids` array.
```javascript
ids.map(R.identity).filter(isPositive).map(R.inc)
```
2. **ramda(map + filter)**: This test case uses Ramda's `R.pipe()` function to chain together three transformations:
	* `R.map(R.identity)` applies an identity function to each element in the array.
	* `R.filter(isPositive)` filters out elements that are not even.
	* `R.map(R.inc)` increments each remaining element by 1.
```javascript
R.pipe(
  R.map(R.identity),
  R.filter(isPositive),
  R.map(R.inc)
)(ids)
```
3. **ramda-transducer(map + filter)**: This test case uses Ramda's `R.transduce()` function to apply a transformation to the `ids` array using a custom reducer:
```javascript
const transform = R.pipe(
  R.map(R.identity),
  R.filter(isPositive),
  R.map(R.inc)
)

R.transduce(
  transform,
  (xs, x) => (xs.push(x), xs),
  [],
  ids,
);
```
**What's Being Tested**

The benchmark is testing the performance of three different approaches for transforming arrays:

1. **Native JavaScript**: Using native `map()` and `filter()` methods.
2. **Ramda: `R.pipe()`**: Chaining transformations using Ramda's `R.pipe()` function.
3. **Ramda: `R.transduce()`**: Applying a transformation to an array using Ramda's `R.transduce()` function.

**Pros/Cons of Each Approach**

Here's a brief summary:

1. **Native JavaScript**:
	* Pros: Fast, lightweight, and widely supported.
	* Cons: May not be as efficient as other approaches, especially for large datasets.
2. **Ramda: `R.pipe()`**:
	* Pros: Chaining transformations can lead to more efficient code, reduces overhead of function calls.
	* Cons: Can be less readable than other approaches, may require more memory allocation for intermediate results.
3. **Ramda: `R.transduce()`**:
	* Pros: Can be more efficient than `R.pipe()` for large datasets, as it avoids creating intermediate arrays.
	* Cons: Can lead to higher memory usage due to the use of a reducer function.

**Other Considerations**

The benchmark also includes information about the device platform (Mobile Safari 17), operating system (iOS 17.1.2), and execution frequency per second for each test case.

**Alternatives**

Some alternative approaches that could be tested include:

* Using other functional programming libraries like Lodash or Underscore.js
* Implementing custom reducers or transform functions using JavaScript's `reduce()` method
* Comparing the performance of different array data structures, such as using an array with a custom iterator

Latest run results:

Run details: (Test run date: 11 months ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 17_1_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.1.2 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 17 on iOS 17.1.2

View result in a separate tab

Test name	Executions per second
Array(map + filter)	4965.8 Ops/sec
ramda(map + filter)	5015.9 Ops/sec
ramda-transducer(map + filter)	5209.4 Ops/sec

Related benchmarks:

ramda-transducer-push (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 25 days ago):