Benchmark: ramda-transducer-into-many-loops

HTML Preparation code:

<script src="https://rawgit.com/ramda/ramda/master/dist/ramda.js"></script>

Script Preparation code:

var ids = Array.from({ length: 1000 }, (_, index) => index + 1)
var isPositive = i => i % 2 === 0

Tests:

Array(map + filter)
ids.map(R.identity).filter(isPositive).map(x => x*x).map(x => x*x).map(x => x*x).map(x => x*x).map(x => x*x).map(x => x*x).map(x => x*x).map(x => x*x).map(x => x*x).map(x => x*x).map(R.inc)
ramda(map + filter)
R.pipe( R.map(R.identity), R.filter(isPositive), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(R.inc), )(ids)
ramda-transducer(map + filter)
const transform = R.pipe( R.map(R.identity), R.filter(isPositive), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), R.map(x => x*x), 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:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Safari/537.36

Browser/OS: Chrome 87 on Windows

View result in a separate tab

Test name	Executions per second
Array(map + filter)	2117.5 Ops/sec
ramda(map + filter)	2853.4 Ops/sec
ramda-transducer(map + filter)	1704.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**What is being tested?**

The provided JSON represents three individual test cases for measuring the performance of different approaches to apply multiple transformations to an array in JavaScript. The arrays are created using the `ids` variable, which is initialized with 1,000 elements from 1 to 1000 (inclusive).

The transformations involve:

1. Mapping over the array with a function that squares each element (`x => x*x`).
2. Filtering out even numbers (`isPositive` function).
3. Applying another mapping operation.

**Options compared:**

There are three approaches being compared:

1. **Array.map + Array.filter**: A traditional JavaScript approach using `map` and `filter` methods to achieve the desired transformations.
2. **Ramda's map + filter**: Ramda is a functional programming library for JavaScript. The `map` and `filter` functions from Ramda are used to apply the same transformations as above, but with a more concise syntax.
3. **Ramda's transducer (map + filter)**: A transducer is a higher-order function that applies transformations to data streams. In this case, a custom transducer is defined using Ramda's `pipe` and `into` functions to achieve the same result as above.

**Pros and Cons of each approach:**

1. **Array.map + Array.filter**: This approach is straightforward and easy to understand. However, it can be less efficient than other approaches due to the overhead of creating new arrays with each mapping operation.
2. **Ramda's map + filter**: Ramda's `map` and `filter` functions are optimized for performance and provide a concise way to apply transformations. However, they may require more setup and understanding of functional programming concepts.
3. **Ramda's transducer (map + filter)**: This approach is often the most efficient and scalable solution, as it avoids creating intermediate arrays and instead applies transformations directly to the data stream.

**Library:**

* Ramda: A functional programming library for JavaScript that provides a concise way to apply transformations and perform data processing tasks.
* `isPositive` function: A custom function that takes an integer `i` and returns `true` if it's even, and `false` otherwise.

**Special JS feature/syntax:**

None mentioned in this specific benchmark. However, Ramda provides a concise syntax for functional programming tasks, which may be unfamiliar to developers without prior experience with functional programming concepts.

**Other alternatives:**

For measuring the performance of different JavaScript approaches, other options include:

* `Lodash` (another popular utility library for JavaScript)
* `Babel` (a transpiler for converting modern JavaScript code to older syntaxes)
* `Benchmark.js` (a dedicated benchmarking library for JavaScript)

These alternatives can be used to create and run different benchmarks with varying configurations and test cases.

Related benchmarks:

ramda-transducer-into-many-loops (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 2 months ago):