Benchmark: Lodash vs Ramda vs Native 28/01/2022

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.28.0/ramda.min.js" crossorigin="anonymous"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.20/lodash.min.js" crossorigin="anonymous"></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) .map(item => item.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);
Native
var result = data .map(x => x.counter) .filter(isOdd) .map(square) .filter(lessThanThreeDigits) ;
transducer
const transducer = R.compose(R.map((x) => x.counter), R.filter(isOdd), R.map(square), R.filter(lessThanThreeDigits)); const results = R.into([], transducer, data);
transducer2
const transducer = R.compose(R.map((x) => x.counter), R.filter(isOdd), R.map(square), R.filter(lessThanThreeDigits)); const results = R.transduce(transducer, (xs, x) => (xs.push(x), xs), [], 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
Native
transducer
transducer2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (X11; CrOS x86_64 14324.72.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.91 Safari/537.36

Browser/OS: Chrome 97 on Chrome OS 14324.72.0

View result in a separate tab

Test name	Executions per second
Lodash	550.4 Ops/sec
Ramda without relying on currying or composition	483.8 Ops/sec
Ramda with currying and composition	422.4 Ops/sec
Native	550.8 Ops/sec
transducer	507.6 Ops/sec
transducer2	494.4 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The benchmark compares the performance of three JavaScript libraries: Lodash, Ramda, and native array functions. The test case uses a sequence of map, filter, map, filter operations on an array of 10,000 objects.

**Options Compared**

There are four options compared in this benchmark:

1. **Lodash**: A popular JavaScript library for functional programming.
2. **Ramda**: Another popular JavaScript library for functional programming.
3. **Native**: The native JavaScript array functions (e.g., `map()`, `filter()`).
4. **Transducer versions of Ramda and Lodash**: These are alternative implementations of the Ramda and Lodash libraries that use a transducer approach.

**Pros and Cons**

Here's a brief summary of the pros and cons of each option:

1. **Lodash**:
	* Pros: Widely used, easy to learn, and provides a comprehensive set of functional programming utilities.
	* Cons: Can be slower than native array functions due to its overhead.
2. **Ramda**:
	* Pros: Provides a unique and powerful set of functional programming utilities, and is known for its performance.
	* Cons: Can have a steeper learning curve due to its different API.
3. **Native**: The native JavaScript array functions are usually the fastest option, but they require more manual code and may not be as convenient to use.
4. **Transducer versions of Ramda and Lodash**:
	* Pros: Can provide better performance than traditional function composition approaches, especially for large datasets.
	* Cons: May have a higher learning curve due to their unique API and transducer approach.

**Library Purpose**

In this benchmark, both Lodash and Ramda are used as functional programming libraries that provide a set of pre-built functions for common tasks like filtering, mapping, and reducing data. They aim to simplify code and make it more concise.

The **transducer versions** of Ramda and Lodash use a different approach called "transduction" (a combination of transformation and accumulation) to process data. This approach can be more efficient than traditional function composition approaches for large datasets.

**Special JS Features**

There are no special JavaScript features mentioned in this benchmark. The focus is on comparing the performance of different libraries and their implementations.

I hope this explanation helps you understand the benchmark!

Related benchmarks:

Lodash vs Ramda vs Native 28/01/2022 (version: 0)

Tests libraries lodash, ramda and native array functions on a sequence of map, filter, map filter operations.

Comparing performance of: Lodash vs Ramda without relying on currying or composition vs Ramda with currying and composition vs Native vs transducer vs transducer2

Created: 2 years ago by: Registered User

Jump to the latest result

Lodash

Ramda without relying on currying or composition

Ramda with currying and composition

Native

transducer

transducer2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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