Benchmark: Ramda(0.27.0) vs. Lodash(4.17.20) vs Natice (filter, find)

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/4.17.20/lodash.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;
}

Tests:

Lodash filter
var result = _.filter(data, isOdd);
Ramda filter
var result = R.filter(isOdd, data);
Native filter
var result = data.filter(({counter}) => isOdd(counter));
Lodash find
var result = _.find(data, {counter: 859});
Ramda find
var result = R.find(R.propEq('counter', 859), data);
Native find
var result = data.find(({counter}) => counter === 859);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash filter
Ramda filter
Native filter
Lodash find
Ramda find
Native find

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested, compared options, pros and cons of each approach, library usage, special JS features, and other considerations.

**Benchmark Definition**

The benchmark compares the performance of three approaches:

1. Lodash (a popular JavaScript utility library)
2. Ramda (another popular functional programming library for JavaScript)
3. Native ( native JavaScript implementation without any libraries)

**Script Preparation Code**

The script preparation code sets up the environment for the benchmark by:

* Loading the latest versions of Lodash and Ramda using CDN links
* Defining a small data set with 10,000 objects containing an "counter" property
* Defining a simple function `isOdd` that returns true if the input number is odd

**Html Preparation Code**

The html preparation code includes scripts from the CDN to load the benchmarking scripts.

**Individual Test Cases**

There are six individual test cases:

1. Lodash filter: uses `_filter` from Lodash to filter the data set based on `isOdd`
2. Ramda filter: uses `R.filter` from Ramda to filter the data set based on `isOdd`
3. Native filter: uses native JavaScript `Array.prototype.filter()` method to filter the data set based on `isOdd`
4. Lodash find: uses `_find` from Lodash to find an element in the data set that matches a condition
5. Ramda find: uses `R.find` from Ramda to find an element in the data set that matches a condition
6. Native find: uses native JavaScript `Array.prototype.findIndex()` method to find an index of an element in the data set that matches a condition

**Library Usage**

* Lodash is used for its `filter`, `_find` functions.
* Ramda is used for its `R.filter`, `R.find` functions.

**Special JS Features/ Syntax**

None mentioned, but native JavaScript implementations use methods that are familiar to most developers (e.g., `Array.prototype.filter()` and `Array.prototype.findIndex()`).

**Pros and Cons of Each Approach**

1. **Lodash filter**: Pros: Lodash has a large community, extensive documentation, and optimized performance for common use cases. Cons: dependency on Lodash library adds overhead.
2. **Ramda filter**: Pros: Ramda provides a more functional programming approach, which can lead to cleaner code and easier maintenance. Cons: requires learning new library and syntax.
3. **Native filter**: Pros: no additional dependencies, optimal performance for simple use cases. Cons: requires manual implementation of filtering logic.

**Other Considerations**

* The benchmark tests individual functions (`filter` and `find`) rather than entire libraries. This allows for a more focused comparison of the three approaches.
* The data set size (10,000 elements) is relatively small, which may not accurately represent real-world scenarios where larger datasets are common.
* The benchmark measures execution time per second (`ExecutionsPerSecond`), which can be an interesting metric. However, it's essential to consider other factors like memory usage and potential performance variations between browsers or environments.

**Alternatives**

Other JavaScript utility libraries that could be compared in a similar benchmark include:

* Underscore.js
* Fastify
* Preact
* Babel (for a more specific comparison of transpilation performance)

However, considering the focus on filtering and finding operations, it's worth exploring other popular functional programming libraries like:

* Immutable.js
* Stryker
* Famo.us

These libraries might offer different trade-offs in terms of performance, memory usage, or code complexity.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:88.0) Gecko/20100101 Firefox/88.0

Browser/OS: Firefox 88 on Linux

View result in a separate tab

Test name	Executions per second
Lodash filter	138.1 Ops/sec
Ramda filter	148.1 Ops/sec
Native filter	2419.8 Ops/sec
Lodash find	20817.5 Ops/sec
Ramda find	641.9 Ops/sec
Native find	45744.3 Ops/sec

Related benchmarks:

Ramda(0.27.0) vs. Lodash(4.17.20) vs Natice (filter, find) (version: 0)

Compares performance on the same tasks using Lodash vs Ramda vs Native.

Comparing performance of: Lodash filter vs Ramda filter vs Native filter vs Lodash find vs Ramda find vs Native find

Created: 3 years ago by: Registered User

Jump to the latest result

Lodash filter

Ramda filter

Native filter

Lodash find

Ramda find

Native find

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):