Benchmark: Ramda -> Lodash -> Native -> for Loop -> Transdusers

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/4.17.5/lodash.core.min.js"></script>

Script Preparation code:

var sqr = x => x * x
var isEven = x => x > 0 && 0 === x % 2
var sum = (x, y) => x + y
var toString = x => '' + x
var len = x => x.length

var limit = 1e6
var arr = new Array(limit).fill().map((_, i) => i + 1)

Tests:

Ramda
var ramdaResult = R.pipe( R.filter(isEven), R.map(sqr), R.map(toString), R.map(len), R.reduce(sum, 0) )(arr)
Lodash
var lodashResul = _.chain(arr) .filter(isEven) .map(sqr) .map(toString) .map(len) .reduce(sum, 0) .value()
Native
var result = arr .filter(isEven) .map(sqr) .map(toString) .map(len) .reduce(sum, 0)
for Loop
let loopResult = 0 for (let i = 1; i <= limit; i++) { if (isEven(i)) { loopResult += ('' + sqr(i)).length } }
transdusers
const xForm = R.compose( R.filter(isEven), R.map(sqr), R.map(toString), R.map(len) ) const transduserResul = R.transduce(xForm, sum, 0, arr)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Lodash
Native
for Loop
transdusers

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.

Let's dive into the benchmark.

**What is being tested?**

The provided JSON represents a JavaScript microbenchmark, where four different approaches are compared to calculate the sum of squares of even numbers in an array:

1. Ramda (a functional programming library)
2. Lodash (another popular functional programming library)
3. Native (JavaScript code without any external libraries or loops)
4. for Loop (a traditional loop-based approach)

**Options comparison**

The benchmark compares the performance of each approach, measuring the number of executions per second.

* **Ramda**: Uses the `R.pipe` and `R.reduce` functions from Ramda to process the array. This approach leverages the functional programming concepts provided by Ramda.
* **Lodash**: Employs the `_.chain` method from Lodash to chain together multiple functions, similar to Ramda's piping mechanism. It then uses `_reduce` to calculate the final result.
* **Native**: Does not use any external libraries or loops. Instead, it implements a simple loop-based approach using JavaScript syntax.
* **for Loop**: Uses a traditional `for` loop to iterate over the array and accumulate the sum.

**Pros and cons of each approach**

Here's a brief summary:

1. **Ramda**:
	* Pros: Leverages functional programming concepts for concise code, reduces boilerplate.
	* Cons: Requires importing an external library (Ramda), might have higher overhead due to function calls.
2. **Lodash**:
	* Pros: Similar conciseness benefits as Ramda, uses a familiar API.
	* Cons: Still requires importing another external library (Lodash), similar overhead concerns.
3. **Native**:
	* Pros: No external libraries or loops involved, might be more efficient due to native execution.
	* Cons: Requires manual loop handling, which can lead to complexity and potential errors.
4. **for Loop**:
	* Pros: Simplest approach, doesn't require any external libraries or advanced concepts.
	* Cons: Most verbose, potentially slower due to the overhead of the loop.

**Other considerations**

When interpreting benchmark results, keep in mind:

* The JavaScript engine used by the browser (e.g., V8) and its optimization level can influence performance differences between approaches.
* External library overhead, such as HTTP requests or parse times, might impact execution speed.
* The specific hardware and software configuration of the testing environment can also affect results.

**Special considerations**

In this benchmark:

* There are no special JavaScript features or syntax used. All examples conform to standard JavaScript (ES6) syntax.

Now that we've covered the basics, let's look at some alternatives to these approaches:

Alternatives to Ramda and Lodash:

1. **Underscore.js**: Another popular functional programming library that provides similar functionality.
2. **PureScript**: A functional programming language with a large standard library and compiler.

Alternatives to Native loop approach:

1. **Array.prototype.reduce()**: Using the built-in `reduce()` method for array operations can be more concise than a custom loop.
2. **Arrow functions**: Simplified function declarations, like arrow functions (`x => x * x`), can replace traditional functions in some cases.

This concludes our exploration of MeasureThat.net's JavaScript microbenchmark.

Related benchmarks:

Ramda -> Lodash -> Native -> for Loop -> Transdusers (version: 4)

Comparing performance of: Ramda vs Lodash vs Native vs for Loop vs transdusers

Created: 6 years ago by: Registered User

Jump to the latest result

Ramda

Lodash

Native

for Loop

transdusers

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):