Benchmark: Array slice vs for loop (new Array)

Script Preparation code:

var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0];

Tests:

slice
var copy = data.slice(3, 8);
push
var copy = new Array(5); for (var i = 3; i < 8; i++) { copy[i] = data[i]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
push

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is designed to compare two approaches for creating an array copy: using `Array.prototype.slice()` (Method 1) versus using a traditional `for` loop with `push()` (Method 2).

**What's Being Tested**

In this specific benchmark, we're measuring the performance of these two methods on a sample dataset of 10 elements (`var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0];`). The test creates an array copy using each method and then measures how many executions per second are possible.

**Method 1: Array.prototype.slice()**

* This method creates a shallow copy of the original array by slicing it from index 3 to 7 (`copy = data.slice(3, 8);`).
* Pros:
	+ Simple and efficient way to create an array copy.
	+ Built-in method in the JavaScript standard library, making it widely supported.
* Cons:
	+ Creates a new array object with references to the original elements.
	+ May not be suitable for large arrays or complex data structures.

**Method 2: Traditional for loop with push()**

* This method creates an array copy by iterating over the original array and pushing each element into a new array (`copy = new Array(5); for (var i = 3; i < 8; i++) { copy[i] = data[i]; }`).
* Pros:
	+ Allows for more control over the array creation process.
	+ Can be useful when working with large arrays or complex data structures.
* Cons:
	+ More verbose and error-prone compared to `Array.prototype.slice()`.
	+ May have performance implications due to the overhead of pushing elements into an array.

**Other Considerations**

In general, using `Array.prototype.slice()` is a good choice when:

* You need a simple way to create an array copy.
* The original array is relatively small and doesn't require complex data structure handling.

On the other hand, using a traditional `for` loop with `push()` might be a better choice when:

* You need more control over the array creation process.
* You're working with large arrays or complex data structures that require careful manipulation.

**Library Used**

In this benchmark, none of the tested methods rely on any specific library. The `Array.prototype.slice()` method is a built-in JavaScript method, while the traditional `for` loop with `push()` does not use any external libraries.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in this benchmark.

**Other Alternatives**

If you're interested in exploring alternative approaches to creating array copies, consider the following methods:

* `Array.from()`: Creates an array from an iterable source (e.g., an array, string, or object).
* `concat()`: Concatenates one or more arrays.
* `Map` and `Set` APIs: Can be used to create mutable collections with efficient insertion and lookup operations.

Keep in mind that each of these alternatives has its own strengths and weaknesses, and may not offer the same level of performance as `Array.prototype.slice()` for simple array copying tasks.

Latest run results:

Run details: (Test run date: 21 days ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 17_6_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.6 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 17 on iOS 17.6.1

View result in a separate tab

Test name	Executions per second
slice	23736520.0 Ops/sec
push	3996798.0 Ops/sec

Related benchmarks:

Array slice vs for loop (new Array) (version: 0)

Comparing performance of: slice vs push

Created: 8 months ago by: Guest

Jump to the latest result

slice

push

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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