Benchmark: Ramda vs Native .chain

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.28.0/ramda.min.js" integrity="sha512-t0vPcE8ynwIFovsylwUuLPIbdhDj6fav2prN9fEu/VYBupsmrmk9x43Hvnt+Mgn2h5YPSJOk7PMo9zIeGedD1A==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>

Script Preparation code:

  var data = _.range(10000).map(function(i) {
    return {
      counter: i
    }
  });
  
  function isOdd(num) {
    return num % 2 === 1;
  }
  
  function square(num) {
    return num * num;
  }
  
  function lessThanThreeDigits(num) {
    return num.toString().length < 3;
  }

Tests:

Ramda
var result = _.chain(data) .map(item => item.counter) .filter(isOdd) .map(square) .filter(lessThanThreeDigits) .value();
Native
var result = data.map(item => item.counter) .filter(isOdd) .map(square) .filter(lessThanThreeDigits)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Native

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what is being tested.

**Benchmark Overview**

The benchmark compares the performance of two approaches: Ramda (a functional programming library) and Native JavaScript (without any external libraries).

**Options Compared**

Two options are compared:

1. **Ramda**: uses the `_.chain()` method to create a pipeline of operations, which allows for more expressive and concise code.
2. **Native**: does not use any external libraries or functions, instead using native JavaScript syntax.

**Pros and Cons**

**Ramda (Pipeline Approach)**

* Pros:
	+ More concise and readable code
	+ Easier to compose complex transformations
	+ Less prone to errors due to explicit filtering and mapping
* Cons:
	+ May incur additional overhead due to the pipeline creation mechanism
	+ May require more memory for storing the intermediate results

**Native (Imperative Approach)**

* Pros:
	+ Typically faster execution since it avoids the overhead of creating a pipeline
	+ Can be more efficient in terms of memory usage
* Cons:
	+ Code can become more verbose and harder to read due to the need for explicit loops and conditionals
	+ More prone to errors due to the complexity of the code

**Library: Ramda**

Ramda is a functional programming library that provides a set of higher-order functions for working with arrays and objects. The `_.chain()` method creates a pipeline of operations, allowing for more expressive and concise code.

In this benchmark, Ramda's `_.chain()` method is used to create a pipeline of operations: filtering, mapping, squaring, and filtering again. This approach allows for a more readable and maintainable codebase.

**Special JS Feature/Syntax**

None mentioned in the provided benchmark definition.

**Other Alternatives**

If you're not familiar with Ramda or prefer an alternative, here are some options:

1. **Lodash**: another popular functional programming library that provides similar functionality to Ramda.
2. **Array.prototype.reduce()**: an imperative approach that uses a reduction method to process the array, which can be more concise and readable in some cases.
3. **Custom Looping**: writing custom loops using `forEach`, `map`, or `reduce` methods can provide a more straightforward and efficient implementation.

Keep in mind that each option has its trade-offs, and the choice ultimately depends on your personal preference, project requirements, and performance constraints.

Related benchmarks:

Ramda vs Native .chain (version: 0)

Comparing performance of: Ramda vs Native

Created: 3 years ago by: Guest

Jump to the latest result

Ramda

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):