Benchmark: Ramda vs. Lodash

Ramda vs. Lodash (version: 0)

Compares performance on the same task using Lodash vs two styles of Ramda vs two styles of "native" Javascript. In all cases the task is pulling "counter" property from each item in an array, filtering out odd items, squaring them, then returning those squared values that have less than two digits.

Comparing performance of: Lodash vs Ramda without relying on currying or composition vs Ramda with currying and composition

Created: 7 years ago by: Guest

Jump to the latest result

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/4.17.4/lodash.min.js"></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:

Lodash
var result = _.chain(data) .pluck('counter') .filter(isOdd) .map(square) .filter(lessThanThreeDigits) .value();
Ramda without relying on currying or composition
var result = R.filter(lessThanThreeDigits, R.map(square, R.filter(isOdd, R.pluck('counter', data))));
Ramda with currying and composition
var result = R.pipe( R.pluck('counter'), R.filter(isOdd), R.map(square), R.filter(lessThanThreeDigits) )(data);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
Ramda without relying on currying or composition
Ramda with currying and composition

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'm excited to dive into explaining the JavaScript microbenchmark you provided.

**Benchmark Overview**

The benchmark compares the performance of three approaches:

1. **Lodash**: The `_.chain` method is used, which applies a series of functions in sequence.
2. **Ramda without relying on currying or composition**: This approach uses individual functions to perform each operation, rather than chaining them together like Lodash.
3. **Ramda with currying and composition**: This approach takes advantage of Ramda's functional programming features, using composed functions to perform the operations.

**Options Compared**

The options being compared are:

* Using a functional programming library (Lodash) versus a pure JavaScript implementation (Ramda).
* Chaining functions together (Lodash) versus applying individual functions in sequence (Ramda without currying/composition).

**Pros and Cons**

**Lodash:**

Pros:

* Easy to use and understand, as it provides a familiar API.
* Built-in support for functional programming features.

Cons:

* May introduce unnecessary overhead due to the chaining of functions.
* Less control over the execution flow.

**Ramda without currying/composition:**

Pros:

* More control over the execution flow, allowing for optimization opportunities.
* Can be more efficient in terms of memory usage.

Cons:

* Requires a deeper understanding of functional programming concepts.
* May be less readable due to the lack of a familiar API.

**Ramda with currying and composition:**

Pros:

* Takes advantage of Ramda's optimized implementation, resulting in potentially better performance.
* Provides a concise and expressive way to perform complex operations.

Cons:

* Requires a good understanding of functional programming concepts and Ramda's syntax.

**Other Considerations**

* **Currying**: Ramda uses currying to enable composition of functions. Currying is a technique where a function is applied with fewer arguments than it takes in total, allowing for more flexible function chaining.
* **Composition**: Ramda provides a way to compose functions together using the `pipe` method or by applying multiple functions in sequence.

**Library and Purpose**

The `_.chain`, `_.pluck`, `_.filter`, `_.map`, `_.pipe`, and other methods used in Lodash are designed to simplify functional programming tasks. They provide a convenient interface for composing operations, making it easier to write concise and readable code.

**Special JavaScript Feature/Syntax**

There is no specific JavaScript feature or syntax mentioned in the benchmark that requires special attention. However, the use of currying and composition in Ramda does introduce some nuances for developers familiar with functional programming concepts.

In summary, the benchmark provides a clear comparison between three approaches: Lodash's chaining-based approach, Ramda without currying/composition, and Ramda with currying and composition. By evaluating these options, developers can gain insights into the performance characteristics of each approach and make informed decisions about their choice of functional programming library or pure JavaScript implementation.

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.129 Safari/537.36

Browser/OS: Chrome 81 on Mac OS X 10.15.4

View result in a separate tab

Test name	Executions per second
Lodash	0.0 Ops/sec
Ramda without relying on currying or composition	979.8 Ops/sec
Ramda with currying and composition	1165.2 Ops/sec

Related benchmarks:

Ramda vs. Lodash (version: 0)

Compares performance on the same task using Lodash vs two styles of Ramda vs two styles of "native" Javascript. In all cases the task is pulling "counter" property from each item in an array, filtering out odd items, squaring them, then returning those squared values that have less than two digits.

Comparing performance of: Lodash vs Ramda without relying on currying or composition vs Ramda with currying and composition

Created: 7 years ago by: Guest

Jump to the latest result

Lodash

Ramda without relying on currying or composition

Ramda with currying and composition

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):