Benchmark: ramda capitalize vs. vanila capitalize

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.27.1/ramda.min.js"></script>

Script Preparation code:

var text = 'lorem ipsum dolor sit amet';

Tests:

ramda
const capitalize = R.when( R.is(String), R.pipe(R.toLower, R.replace(/^./, R.toUpper)), ); var result = capitalize(text);
vanila
function capitalize(text) { return text[0].toUpperCase() + text.slice(1).toLowerCase(); } var result = capitalize(text);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
ramda
vanila

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36 Edg/134.0.0.0

Browser/OS: Chrome 134 on Windows

View result in a separate tab

Test name	Executions per second
ramda	1226879.1 Ops/sec
vanila	24572264.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Overview**

The benchmark compares two JavaScript functions: `capitalize` implemented using Ramda (a functional programming library) and a vanilla implementation. The goal is to determine which function performs better in terms of execution speed.

**Options Being Compared**

1. **Ramda (`R.when` and `R.pipe`)**:
	* Pros: Encapsulates the transformation logic, making it easier to read and maintain.
	* Cons: Adds overhead due to the library's complexity and potential overhead from parsing the Ramda functions.
2. **Vanilla JavaScript (`function capitalize(text) { ... }`)**:
	* Pros: Lightweight, easy to understand, and highly optimized for modern browsers.
	* Cons: Requires manual implementation of the transformation logic, which can lead to errors or inconsistencies.

**Library Used**

Ramda is a functional programming library that provides a set of higher-order functions (HOFs) for data processing. In this benchmark, `R.when` and `R.pipe` are used to define the `capitalize` function. Ramda's purpose is to provide a concise and expressive way to work with data, making it easier to write maintainable code.

**Special JS Feature/ Syntax**

There is no explicit use of any special JavaScript features or syntax in this benchmark. However, it's worth noting that the benchmark uses ES6+ features (e.g., arrow functions, template literals) that are supported by modern browsers.

**Other Considerations**

* **Browser and Device Platform**: The benchmark runs on a Mac OS X 10.15 machine with Firefox 93.
* **Execution Per Second**: The benchmark measures the number of executions per second, which provides insight into performance.
* **Code Readability and Maintainability**: Ramda's use can make the code more readable and maintainable, but may also introduce overhead.

**Alternatives**

If you were to modify or create an alternative implementation:

1. **Other functional programming libraries**: Consider using other libraries like Lodash, Underscore.js, or a custom implementation with your own HOFs.
2. **Alternative approaches**: Implement the `capitalize` function using other techniques, such as:
	* Using regular expressions (regex) to match and transform characters.
	* Utilizing string manipulation methods like `toUpperCase()` and `toLowerCase()`.
3. **Benchmarking frameworks**: Use a benchmarking framework like Benchmark.js or Microbenchmark to create a more comprehensive benchmarking setup.

Keep in mind that the choice of implementation and library depends on your specific requirements, performance constraints, and personal preferences as a developer.

Related benchmarks:

ramda capitalize vs. vanila capitalize (version: 0)

Comparing performance of: ramda vs vanila

Created: 4 years ago by: Guest

Jump to the latest result

ramda

vanila

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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