Benchmark: Lodash vs Ramda - MeasureThat.net

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.js"></script>

Script Preparation code:

  var data = [..._.range(1000).map(function(i) {
    return {
      counter: i
    }
  }),..._.range(1000).map(function(i) {
    return {
      counter: i
    }
  })];

Tests:

Lodash
var result = _.uniq(data);
Ramda without relying on currying or composition
var result = R.uniq(data);
roll
function* unique(xs) { const seen = new Set(); for (const x of xs) { if (seen.has(x)) continue; seen.add(x); yield x; } } var result = [...unique(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
roll

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents a JavaScript benchmark comparing the performance of Lodash and Ramda in creating an array with unique elements from a given data set.

**Benchmark Definition JSON Explanation**

* `Name` and `Description` fields are optional and can be used to identify the benchmark.
* `Script Preparation Code` is executed before running the benchmark, preparing the JavaScript environment. In this case, it creates two arrays of 1000 objects each using Lodash's `_range` and `_map` functions.
* `Html Preparation Code` includes script tags to load Ramda and Lodash libraries from a CDN.

**Individual Test Cases**

The benchmark consists of three test cases:

1. **Lodash**: The first test case runs the `_.uniq(data)` function, which removes duplicates from the prepared data array using Lodash's unique function.
2. **Ramda without relying on currying or composition**: The second test case uses Ramda's `R.uniq` function, which also removes duplicates from the prepared data array without using currying or composition.
3. **roll**: The third test case implements a custom solution using an iterator (`function* unique`) that keeps track of seen elements in a set and yields only unique values.

**Comparison Options**

The benchmark compares the performance of Lodash's `_uniq` function, Ramda's `R.uniq` function (without relying on currying or composition), and the custom `roll` solution.

**Pros and Cons of Each Approach**

* **Lodash**: Pros: Well-maintained library with a large community, extensive API. Cons: Might be overkill for this specific benchmark, adds unnecessary overhead.
* **Ramda without relying on currying or composition**: Pros: More efficient than Lodash's `_uniq` function, as it avoids the cost of currying and composition. Cons: Requires knowledge of Ramda's API and might not be familiar to all developers.
* **roll**: Pros: Custom solution tailored for this specific benchmark, potentially more efficient due to its simplicity. Cons: Might require additional development effort to maintain and understand.

**Library and Syntax Considerations**

* Ramda is a functional programming library providing a variety of useful functions, including `R.uniq`. Its syntax can be unfamiliar to developers without experience with functional programming.
* Lodash is a comprehensive utility library with many features beyond just the `_uniq` function. Its syntax might be more accessible to developers.

**Other Alternatives**

For this specific benchmark, other alternatives could include:

* Using built-in JavaScript functions like `Set` or `Map` to remove duplicates, which would eliminate the need for external libraries.
* Implementing a custom solution similar to `roll`, but using different iteration strategies or data structures.
* Using other functional programming libraries like Underscore.js (which is part of the same ecosystem as Lodash) or other alternative approaches.

Keep in mind that the choice of library and implementation strategy depends on the specific requirements and constraints of the project.

Related benchmarks:

Lodash vs Ramda (version: 0)

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

Created: 8 years ago by: Guest

Jump to the latest result

Lodash

Ramda without relying on currying or composition

roll

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):