Benchmark: xprod (custom impl vs ramda)

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>

Script Preparation code:

function xprod(r,n){if(1===arguments.length)return function(n){return xprod(r,n)};for(var t=[],e=r.length,o=n.length,u=0;u<e;u++)for(var f=0;f<o;f++)t.push([r[u],n[f]]);return t}

var data1 = [...Array(100)].map((v, i) => i)
var data2 = [...Array(100)].map((v, i) => i)

Tests:

ramda
R.xprod(data1, data2)
custom impl
xprod(data1, data2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
ramda
custom impl

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36

Browser/OS: Chrome 132 on Mac OS X 10.15.7

View result in a separate tab

custom impl

24K/s

ramda

23K/s

View exact numbers

Test name	Executions per second
✓ custom impl	23,805 Ops/sec
ramda	22,840 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON benchmark definition and test cases.

**Benchmark Definition**

The benchmark defines two different implementations of the `xprod` function: one using Ramda, a functional programming library for JavaScript, and another implementing it custom.

The `xprod` function is designed to produce the Cartesian product of two arrays. The implementation without Ramda uses a simple loop to iterate over both arrays and create an array of tuples.

**Options Compared**

Two options are compared:

1. **Ramda Implementation**: The first option uses the Ramda library, specifically the `R.xprod` function. This implementation likely utilizes functional programming principles and optimized algorithms provided by the library.
2. **Custom Implementation**: The second option implements the `xprod` function from scratch, using a simple loop to create the Cartesian product.

**Pros and Cons**

Here are some pros and cons of each approach:

**Ramda Implementation:**

Pros:

* Likely provides better performance due to optimized algorithms and reduced boilerplate code.
* Reduces maintenance burden, as changes to the implementation can be made in one place (the library).

Cons:

* Adds an external dependency on Ramda, which might not be desired for all use cases.
* May have additional overhead due to the library's functionality.

**Custom Implementation:**

Pros:

* No external dependencies, which can simplify testing and deployment.
* Can provide more control over the implementation details.

Cons:

* Requires more code and maintenance effort, as changes need to be made in multiple places.
* Might result in less optimal performance compared to a well-optimized library implementation.

**Other Considerations**

When evaluating these options, consider the following factors:

* **Performance**: If speed is crucial, the Ramda implementation might provide better performance due to its optimized algorithms.
* **Maintenance and Readability**: The custom implementation may be more maintainable, as changes can be made in one place, but it comes at the cost of additional code.
* **Dependency Management**: Adding an external dependency like Ramda can introduce complexity, especially for projects with strict dependencies or security requirements.

**Library Used: Ramda**

Ramda is a popular functional programming library for JavaScript. It provides various utility functions and algorithms optimized for performance and readability. The `R.xprod` function specifically implements the Cartesian product algorithm, which might be more efficient than a custom implementation.

In the context of this benchmark, using Ramda likely enables faster execution times due to its optimized implementation.

**Special JS Feature or Syntax**

There are no specific JavaScript features or syntax mentioned in the provided code. However, if we were to consider other aspects, we could look at:

* **Async operations**: If the `xprod` function was designed to handle asynchronous inputs or produce asynchronous outputs, we would need to account for these differences.
* **Type checking**: Depending on the type annotations used in the implementation, additional considerations might arise.

Keep in mind that this benchmark focuses primarily on the performance comparison of the two `xprod` implementations.

xprod (custom impl vs ramda) (version: 4)

Comparing performance of: `ramda vs custom impl`

Created: 8 years ago by: Registered User

Go to the latest result

ramda

custom impl

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

xprod (custom impl vs ramda) (version: 4)

Comparing performance of: ramda vs custom impl

Created: 8 years ago by: Registered User

Go to the latest result

ramda

custom impl

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `ramda vs custom impl`