Benchmark: Lodash vs Ramda (2020-08 edition)

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.27.0/ramda.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.19/lodash.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) .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);

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

Latest run results:

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

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Let's break down what's being tested in this benchmark.

**Benchmark Overview**

The benchmark compares the performance of Lodash and Ramda, two popular JavaScript libraries for functional programming, on a specific test case involving data filtering and transformation.

**Test Case**

The test case involves creating an array of 10,000 objects with a `counter` property, then applying several transformations to this data using both Lodash and Ramda. The transformations include:

1. Filtering out odd numbers (`isOdd(num)` function)
2. Squaring the remaining numbers (`square(num)` function)
3. Checking if the squared number has less than 3 digits (`lessThanThreeDigits(num)` function)

**Options being compared**

The benchmark compares three different approaches to applying these transformations using Ramda:

1. **Ramda without relying on currying or composition**: This approach uses standalone functions, such as `R.filter`, `R.map`, and `R.pluck`, to apply the transformations.
2. **Ramda with currying and composition**: This approach uses functional programming concepts like currying (transforming a function that takes multiple arguments into one that takes an array of arguments) and composition (applying functions in sequence). The transformations are applied using piped functions, such as `R.pipe`.
3. **Lodash**: The benchmark also compares the performance of Lodash's `chain` method to apply the transformations.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Ramda without relying on currying or composition**:
	* Pros: More familiar, easier to read for those not familiar with functional programming.
	* Cons: Less efficient than piped functions.
2. **Ramda with currying and composition**:
	* Pros: More concise, can be more expressive and readable.
	* Cons: Requires a good understanding of functional programming concepts.
3. **Lodash**:
	* Pros: Familiar for those already using Lodash.
	* Cons: May not be as efficient as Ramda's piped functions.

**Library explanations**

1. **Ramda**: A functional programming library that provides a set of higher-order functions (functions that take other functions as arguments) to manipulate and transform data.
2. **Lodash**: A utility library that provides a large collection of helper functions for common tasks, such as array manipulation, string manipulation, and more.

**Special JS feature or syntax**

This benchmark does not use any special JavaScript features or syntax beyond what's available in modern JavaScript implementations.

Related benchmarks:

Lodash vs Ramda (2020-08 edition) (version: 0)

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

Created: 5 years ago by: Registered User

Jump to the latest result

Lodash

Ramda without relying on currying or composition

Ramda with currying and composition

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):