Benchmark: Ramda vs. Lodash vs. Native array methods

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js"></script>

Script Preparation code:

var data = _.range(50000).map(function(i) {
  return {
    counter: i
  }
});

function isOdd({ counter }) {
  return counter % 2 === 1;
}

function square({ counter }) {
  return counter * counter;
}

function lessThanThreeDigits(value) {
  return value.toString().length < 3;
}

Tests:

Lodash
var result = _.chain(data) .filter(isOdd) .map(square) .filter(lessThanThreeDigits) .value();
Ramda compose
var result = R.compose( R.filter(isOdd), R.map(square), R.filter(lessThanThreeDigits) )(data);
Ramda pipe
var result = R.pipe( R.filter(isOdd), R.map(square), R.filter(lessThanThreeDigits) )(data);
Native
const result = data .filter(isOdd) .map(square) .filter(lessThanThreeDigits);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Ramda compose
Ramda pipe
Native

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The benchmark is comparing the performance of three approaches to process an array of objects:

1. Ramda (a functional programming library)
2. Lodash (another popular utility library for JavaScript)
3. Native JavaScript methods (without using any external libraries)

**Test Cases**

Each test case has a similar structure, involving filtering, mapping, and another filtering operation on the `data` array. The only difference is in how the operations are chained together:

* Ramda: uses `R.compose()` or `R.pipe()`, which apply functions in sequence.
* Lodash: uses `_chain()` to chain functions together.
* Native JavaScript: uses simple method calls, without any library assistance.

**Options Compared**

The benchmark compares three options for processing the `data` array:

1. **Ramda**: using functional programming principles with `R.compose()` or `R.pipe()`.
2. **Lodash**: using a more imperative approach with `_chain()`.
3. **Native JavaScript**: using native functions, like array methods and `forEach()`, without any external libraries.

**Pros and Cons of Each Approach**

Here's a brief summary:

* **Ramda (functional programming)**:
	+ Pros: expressive, concise code, can be more efficient due to caching.
	+ Cons: might be less familiar to some developers, additional library dependency.
* **Lodash (imperative)**:
	+ Pros: widely used and well-known, easy to read for those familiar with its API.
	+ Cons: might lead to more verbose code, less efficient due to unnecessary operations.
* **Native JavaScript**:
	+ Pros: no external dependencies, straightforward code that's easily understandable.
	+ Cons: might be slower or more error-prone without proper caching and optimizations.

**Library Usage**

The `data` array is processed using two libraries:

1. Ramda (version 0.25.0)
2. Lodash (version 4.17.5)

Both libraries are popular utility libraries for JavaScript, providing a range of functions for tasks like array manipulation, string processing, and more.

**Special JS Features**

There's no specific mention of special JavaScript features or syntax in the benchmark code. It only uses standard JavaScript features, such as arrow functions, template literals, and object methods.

**Alternatives**

If you're interested in exploring alternative approaches to this benchmark, consider:

1. **Other functional programming libraries**: such as Immutable.js, Svelte, or Vuex.
2. **Alternative utility libraries**: like jQuery (although not recommended for performance-critical applications).
3. **More native JavaScript approaches**: using techniques like memoization, caching, or optimizing array methods.

Keep in mind that the choice of approach depends on your specific project requirements, performance needs, and personal preferences as a developer.

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Lodash	861.5 Ops/sec
Ramda compose	1989.1 Ops/sec
Ramda pipe	791.3 Ops/sec
Native	701.5 Ops/sec

Related benchmarks:

Ramda vs. Lodash vs. Native array methods (version: 0)

Comparing performance of: Lodash vs Ramda compose vs Ramda pipe vs Native

Created: 10 months ago by: Guest

Jump to the latest result

Lodash

Ramda compose

Ramda pipe

Native

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):