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(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:

No previous run results

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

LLMs can make mistakes. Check important info.

**What is being tested?**

MeasureThat.net is testing the performance of two popular JavaScript libraries: Lodash and Ramda. The tests are designed to measure how efficiently these libraries can execute specific mathematical operations on a dataset.

**Options compared**

The benchmark compares three different approaches to executing the same test:

1. **Lodash**: Uses the `chain` method to compose multiple functions together, which allows for a more functional programming style.
2. **Ramda without currying or composition**: Uses the `filter`, `map`, and `pluck` methods directly, which is a more imperative programming style.
3. **Ramda with currying and composition**: Uses the `pipe` method to compose multiple functions together, similar to Lodash's `chain` method.

**Pros and cons of each approach**

1. **Lodash (chain)**:
	* Pros: Can be more concise and readable for functional programming tasks.
	* Cons: May have performance overhead due to the creation of intermediate arrays.
2. **Ramda without currying or composition**:
	* Pros: Can be faster since it avoids creating intermediate arrays.
	* Cons: May require more boilerplate code and be less readable for complex operations.
3. **Ramda with currying and composition**:
	* Pros: Provides a clear and predictable way to compose functions, which can lead to better performance.
	* Cons: Can be less readable for those unfamiliar with functional programming concepts.

**Libraries and their purposes**

1. **Lodash**: A popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, string processing, and more.
2. **Ramda**: A functional programming library for JavaScript that provides a set of higher-order functions for data transformation and manipulation.

**Special JS features or syntax**

None mentioned in the benchmark definitions.

**Other alternatives**

If you need to compare performance with other libraries or approaches, consider:

1. **Underscore.js**: Another popular utility library similar to Lodash.
2. **Mochas**: A testing framework that can be used to compare performance of different JavaScript implementations.
3. **Benchmarking tools**: Such as BenchmarkJS, which provides a standardized way to write and run benchmarks.

Keep in mind that the choice of library or approach ultimately depends on your specific use case, personal preference, and project requirements.

Related benchmarks:

Lodash vs Ramda (version: 0)

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

Created: 8 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):