Benchmark: ramda-transducer w/mutations

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, (acc, item) => { acc.push(item); return acc; }, [], 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: 4 years ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:84.0) Gecko/20100101 Firefox/84.0

Browser/OS: Firefox 84 on Ubuntu

View result in a separate tab

Test name	Executions per second
Array(map + filter)	875.2 Ops/sec
ramda(map + filter)	824.5 Ops/sec
ramda-transducer(map + filter)	122.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and options.

**Benchmark Overview**

The benchmark measures performance differences between three approaches:

1.  **Vanilla JavaScript (Array)`:** Using built-in Array methods (`map`, `filter`, and `push`).
2.  **Ramda Library:** Utilizing the Ramda functional programming library, which provides a set of higher-order functions for data processing.
3.  **Ramda Transducer:** Employing the Ramda transducer feature, which allows for more efficient processing of data.

**Vanilla JavaScript (Array)**

This approach uses traditional Array methods:

*   `map` applies a function to each element of the array and returns a new array with the transformed elements.
*   `filter` constructs an array from elements that pass the test implemented by the provided function.
*   `push` adds one or more elements to the end of an array.

**Pros:**

*   Lightweight, as it only relies on built-in JavaScript methods.
*   Easy to understand and implement.

**Cons:**

*   May be slower due to the overhead of method calls and loop iterations.
*   Limited functionality compared to the Ramda library.

**Ramda Library**

The Ramda library provides a comprehensive set of higher-order functions for data processing:

*   `map`: applies a function to each element of an array, returning a new array with transformed elements.
*   `filter`: constructs an array from elements that pass a test implemented by the provided function.
*   `pipe` is used to create more complex transformations by composing multiple functions.

**Pros:**

*   More efficient than vanilla JavaScript due to optimized implementations.
*   Provides a wide range of higher-order functions for data processing.

**Cons:**

*   Requires additional library inclusion and learning curve.
*   May have slower startup times compared to vanilla JavaScript.

**Ramda Transducer**

The Ramda transducer feature allows for more efficient processing of data by:

*   Using an accumulator function (`acc`) to accumulate results during the transformation process.
*   Returning the updated accumulator value in each iteration.

This approach is particularly effective when dealing with large datasets or complex transformations.

**Pros:**

*   Optimized for performance, especially with large datasets.
*   Can handle more complex transformations and data processing tasks efficiently.

**Cons:**

*   Requires understanding of transducer concepts and implementation details.
*   May have a steeper learning curve compared to the Ramda library.

In summary:

*   Vanilla JavaScript (Array) provides a lightweight but potentially slower solution.
*   The Ramda library offers more efficient, comprehensive data processing capabilities at the cost of additional library inclusion.
*   The Ramda transducer feature is an optimized approach for large datasets or complex transformations, but requires specialized knowledge and understanding.

The provided benchmark result shows that:

*   **ramda-transducer(map + filter)** outperforms both vanilla JavaScript and the Ramda library approaches in terms of execution speed.
*   **Ramda (map + filter)** performs better than vanilla JavaScript but may be slower than the transducer implementation.

Related benchmarks:

ramda-transducer w/mutations (version: 0)

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

Created: 4 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):