Benchmark: Underscore each vs while

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min.js'></script>

Script Preparation code:

var primes = [2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,97]

Tests:

Underscore each
var result = _.each(primes,(prime) => {return prime % 2 == 0});
While
let i=0; while(i<primes.length) { primes[i] % 2 == 0 i++; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Underscore each
While

Fastest: N/A

Slowest: N/A

Latest run results:

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

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I'd be happy to explain the benchmark and its results in detail.

**Benchmark Overview**

The benchmark measures the performance difference between using `_.each` (a method from the Underscore.js library) versus a traditional `while` loop to iterate over an array of prime numbers. The purpose is to determine which approach is more efficient for this specific use case.

**Underscore.js Library and its Purpose**

Underscore.js is a utility-first JavaScript library that provides a set of functional programming helpers, including methods like `each`, `map`, `filter`, and many others. The `_.each` method iterates over an array, applying a callback function to each element.

In this benchmark, Underscore.js is used to simplify the iteration process, making it easier to write concise and readable code.

**Options Compared**

The two options being compared are:

1. **Underscore.js `_.each`**: This method iterates over an array using a for loop-like structure, where each element in the array serves as the current item, similar to how a `while` loop works.
2. **Traditional `while` loop**: This approach uses a manual counter variable (`i`) and increments it until it reaches the end of the array.

**Pros and Cons**

Here are some key pros and cons of each approach:

* **Underscore.js `_.each`**:
	+ Pros: concise, readable code; easy to write and maintain.
	+ Cons: may have performance overhead due to the additional library dependency; not suitable for all types of loops (e.g., nested loops).
* **Traditional `while` loop**:
	+ Pros: lightweight, no dependencies on external libraries; suitable for complex loop structures.
	+ Cons: more verbose code; requires manual incrementing of a counter variable.

**Other Considerations**

When deciding between these two approaches, consider the following factors:

* Code readability and maintainability: if conciseness is more important than raw performance, `_.each` might be a better choice.
* Performance requirements: if the loop will be executed many times or in critical code paths, the traditional `while` loop might offer slight performance advantages.

**Benchmark Results**

The latest benchmark results show that:

* **Underscore.js `_.each`**: executes approximately 2804130.25 times per second (on Chrome 97/Windows).
* **Traditional `while` loop**: executes approximately 327807.90625 times per second (on Chrome 97/Windows).

Overall, the results suggest that the traditional `while` loop is slightly faster in this specific benchmark.

**Alternative Approaches**

Other alternatives for iterating over arrays include:

1. **For...of loops**: a modern JavaScript syntax that eliminates the need for manual counter variables.
2. **Array.prototype.forEach()**: a built-in method that provides a similar experience to Underscore.js `_.each`, but without an external library dependency.
3. **Closures and recursive functions**: alternative approaches can be used to achieve the same results, but might require more code and are less readable.

Keep in mind that performance differences between these alternatives will likely be negligible for most use cases.

Related benchmarks:

Underscore each vs while (version: 0)

Comparing performance of: Underscore each vs While

Created: 4 years ago by: Guest

Jump to the latest result

Underscore each

While

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):