Benchmark: lodash1 vs ramda1

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/4.17.4/lodash.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.10/lodash.fp.min.js"></script>

Script Preparation code:

var fp = _.noConflict();

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('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 using curring
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 using curring

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, the options compared, pros and cons of each approach, library usage, special JavaScript features or syntax (if applicable), and alternative approaches.

**Benchmark Overview**

The benchmark compares three versions of Ramda, a functional programming library for JavaScript, with Lodash, another popular utility library. The test cases are designed to measure performance differences between these two libraries in executing a specific data transformation pipeline.

**Test Cases**

1. **Lodash**: `var result = _.chain(data) .map('counter') .filter(isOdd) .map(square) .filter(lessThanThreeDigits) .value();`
2. **Ramda without currying or composition**: `var result = R.filter(lessThanThreeDigits, R.map(square, R.filter(isOdd, R.pluck('counter', data)))`*
3. **Ramda using currying**: `var result = R.pipe(R.pluck('counter'), R.filter(isOdd), R.map(square), R.filter(lessThanThreeDigits))(data);*

*The Ramda test case uses the `R.pluck()` function to extract the 'counter' property from each object in the `data` array. This is an example of using a "pluck" function, which is a common operation in functional programming.*

**Library Usage**

In the Lodash test case, `_chain()` is used as a pipeline function to chain multiple operations together.

In the Ramda test cases:

*   The first test case uses `R.filter()`, `R.map()`, and other functions directly from the Ramda library.
*   The second test case uses `R.pluck()` (from Ramda) with Lodash's `map()` function. This is an example of using a combination of libraries to achieve a specific result.
*   The third test case uses `R.pipe()` (from Ramda), which is equivalent to a pipeline-like chain of functions.

**Special JavaScript Features or Syntax**

None are explicitly mentioned in the benchmark code, but it's worth noting that these tests assume a basic understanding of functional programming concepts and use cases for both Lodash and Ramda.

**Pros and Cons of Each Approach**

1.  **Lodash**
    *   Pros:
        *   Easy to read and understand, especially for developers familiar with the Lodash API.
        *   Can be more straightforward in complex data transformations.
    *   Cons:
        *   May not leverage optimal JavaScript engine optimizations due to its high-level, imperative nature.
2.  **Ramda without Currying or Composition**
    *   Pros:
        *   Utilizes Ramda's optimized functions for data transformations, which can be more efficient than Lodash.
        *   Can provide better code reuse and modularity in applications using functional programming techniques.
    *   Cons:
        *   May require additional learning or adaptation to understand the Ramda API.
3.  **Ramda with Curring**
    *   Pros:
        *   Optimized for performance, as it leverages a pipeline-like structure that can be more efficient than direct function calls.
        *   Provides better code modularity and reuse through functional programming techniques.

**Other Alternatives**

1.  **Underscore.js**: Another popular utility library that provides similar functionality to Lodash but with different APIs for its methods.
2.  **Lo-Dash**: A drop-in replacement for Lodash, designed to maintain compatibility with the original API while providing some performance improvements.
3.  **Mithril**: A JavaScript framework that includes a set of utility functions and is often used in conjunction with functional programming techniques.

In conclusion, this benchmark helps compare the performance of Ramda's pipeline-based data transformations against Lodash's more straightforward, imperative approach. By considering pros and cons for each library's API and functionality, developers can choose the best toolset for their specific project requirements and expertise levels.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36

Browser/OS: Chrome 79 on Mac OS X 10.15.2

View result in a separate tab

Test name	Executions per second
Lodash	1170.6 Ops/sec
Ramda without relying on currying or composition	744.8 Ops/sec
Ramda using curring	729.9 Ops/sec

Related benchmarks:

lodash1 vs ramda1 (version: 0)

Comparing performance of: Lodash vs Ramda without relying on currying or composition vs Ramda using curring

Created: 4 years ago by: Guest

Jump to the latest result

Lodash

Ramda without relying on currying or composition

Ramda using curring

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):