Benchmark: ramda-transducer-into

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.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:

No previous run results

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

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**Benchmark Overview**

The provided benchmark measures the performance of different approaches to map, filter, and transform an array in JavaScript. The benchmark uses Ramda, a popular functional programming library for JavaScript.

**Script Preparation Code**

The script preparation code defines two arrays: `ids` and `isPositive`. `ids` is an array of numbers from 1 to 10,000, generated using the `Array.from()` method. `isPositive` is a simple function that checks if a number is even (i.e., it leaves a remainder of 0 when divided by 2).

**Benchmark Definitions**

The benchmark consists of three individual test cases:

1. **"Array(map + filter)"**: This test case uses the native JavaScript `map()` and `filter()` methods to transform the `ids` array.
2. **"ramda(map + filter)"**: This test case uses Ramda's `R.map()` and `R.filter()` functions to transform the `ids` array.
3. **"ramda-transducer(map + filter)"**: This test case uses Ramda's `R.pipe()` function, which creates a chain of transformations, to transform the `ids` array.

**Comparison**

The three approaches differ in how they handle memory allocation:

1. **Native JavaScript (`Array(map + filter)`)**: Creates new arrays for each step of the transformation, using more memory.
2. **Ramda's `R.map()` and `R.filter()`**: Creates a new array with the transformed elements, but reuses the original array as much as possible, using less memory than native JavaScript.
3. **Ramda's `R.pipe()`**: Creates a single array with all transformations applied in sequence, reusing the original array and using even less memory.

**Pros and Cons**

* Native JavaScript:
	+ Pros: Easy to implement, no dependencies required.
	+ Cons: May lead to performance issues due to excessive memory allocation.
* Ramda's `R.map()` and `R.filter()`:
	+ Pros: Efficient use of memory, easy to understand and implement.
	+ Cons: Requires an additional dependency (Ramda), may not be familiar to developers without functional programming experience.
* Ramda's `R.pipe()`:
	+ Pros: Elegant, efficient use of memory, easy to read and maintain.
	+ Cons: May require more upfront understanding of functional programming concepts.

**Library: Ramda**

Ramda is a popular JavaScript library for functional programming. It provides a set of higher-order functions (functions that take other functions as arguments) that can be used to transform and manipulate data in a declarative way.

The `R.map()` function applies a given function to each element of an array, returning a new array with the transformed elements.

The `R.filter()` function filters an array based on a predicate function (a function that returns a boolean value), returning a new array containing only the elements for which the predicate is true.

The `R.pipe()` function creates a chain of transformations by applying multiple functions to an input, returning the final result.

**Special JS Features**

None mentioned in this explanation.

Related benchmarks:

ramda-transducer-into (version: 0)

Using push instead of append

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

Created: 5 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>

Fastest: N/A

Slowest: N/A

No previous run results

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