Benchmark: Ramda head vs Array.[0]

HTML Preparation code:

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

Script Preparation code:

function double(n) {
	return n*2;
}
var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30];

Tests:

Ramda
R.head(double, data);
Array (native)
data[0]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Array (native)

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided benchmark is designed to compare the performance of two approaches: using the Ramda library for functional programming and accessing array elements directly using native JavaScript.

**Library: Ramda**

Ramda is a popular functional programming library for JavaScript that provides a set of higher-order functions for manipulating arrays, objects, and other data structures. In this benchmark, Ramda's `R.head()` function is used to extract the first element from an array.

**Options Compared**

Two options are compared:

1. **Array (native)**: This option uses native JavaScript syntax to access the first element of the array, i.e., `data[0]`.
2. **Ramda**: This option uses Ramda's `R.head()` function to extract the first element from the array.

**Pros and Cons**

* **Native JavaScript**: Pros:
	+ No additional library dependencies.
	+ Potential for better performance due to fewer overheads compared to a library like Ramda.
*   Cons:
	+ Less readable and maintainable code, as it uses low-level array indexing.
*   **Ramda**: Pros:
	+ More concise and expressive code, making it easier to read and understand.
	+ Provides additional features for data manipulation and transformation.
*   Cons:
	+ Requires additional library dependencies, which might introduce performance overhead.

**Special JS Feature/Syntax**

None mentioned in this benchmark.

**Other Considerations**

1. **Memory allocation**: The native JavaScript approach may have better memory management due to fewer allocations compared to using a library like Ramda.
2. **Cache locality**: Using native JavaScript array indexing may improve cache locality, as the compiler can optimize for sequential access patterns.
3. **Performance overhead**: The performance difference between these two approaches might be noticeable in large datasets or high-performance applications.

**Alternatives**

1. **Other functional programming libraries**: Lodash, Moment.js (for date manipulation), or even the built-in `Array.prototype.slice()` method with a custom callback function.
2. **Native JavaScript alternatives**: Using `Array.prototype.shift()` to remove and return the first element, or implementing a custom loop for array iteration.

To create this benchmark, you can use the following code as a starting point:
```javascript
// Import Ramda library (not necessary in this case, but included for completeness)
const R = require('ramda');

// Prepare test data
const data = [...];

// Define benchmark function using native JavaScript syntax
function nativeBenchmark() {
  return data[0];
}

// Define benchmark function using Ramda
function ramdaBenchmark() {
  return R.head(data);
}

// Run benchmarks and store results in an array
const results = [
  { name: 'Native', executionCount: () => nativeBenchmark(), executionsPerSecond: () => Math.floor(1000000 / Date.now()) },
  { name: 'Ramda', executionCount: () => ramdaBenchmark(), executionsPerSecond: () => Math.floor(1000000 / Date.now()) }
];

// Output benchmark results
console.log(results);
```
This code defines two benchmark functions, one using native JavaScript syntax and the other using Ramda's `R.head()` function. The benchmark execution count is simulated by dividing 1 million by the current time in milliseconds. The results are stored in an array and can be outputted to a console or analyzed further.

Related benchmarks:

Ramda head vs Array.[0] (version: 0)

Comparing performance of: Ramda vs Array (native)

Created: 7 years ago by: Guest

Jump to the latest result

Ramda

Array (native)

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):