Benchmark: Splice vs new Array Modulo

Script Preparation code:

var randomArray=Array.from({length: 1000}, () => Math.floor(Math.random() * 100));

Tests:

Splice
for (let i = randomArray.length - 1; i >= 0; --i) { if (randomArray[i] != 1) { continue; } randomArray.splice(i, 1); } return randomArray;
Copy to new
var newArray = []; for (let i = randomArray.length - 1; i >= 0; --i) { if (randomArray[i]%2 == 1) { continue; } newArray.push(randomArray[i]); } return newArray;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Splice
Copy to new

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.5005.63 Safari/537.36

Browser/OS: Chrome 102 on Windows

View result in a separate tab

Test name	Executions per second
Splice	10632.6 Ops/sec
Copy to new	7147.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation.

**Benchmark Definition**

The benchmark is designed to measure the performance difference between two approaches: using `splice()` on an array and creating a new array using the spread operator (`new Array`).

**Script Preparation Code**

The script preparation code generates a random array of 1000 elements, where each element is a random integer between 0 and 99.

**Html Preparation Code**

There is no HTML preparation code provided in this benchmark definition. This suggests that the benchmark is focused solely on measuring JavaScript performance.

**Test Cases**

There are two test cases:

1. **Splice**: This test case uses the `splice()` method to remove elements from the original array until only one element remains.
2. **Copy to new**: This test case creates a new array using the spread operator (`new Array`) and then pushes each element from the original array into the new array, effectively copying the elements.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Splice**:
	+ Pros: `splice()` is a built-in method that can modify the original array in place, which might be beneficial for certain use cases.
	+ Cons: Modifying the original array can have unexpected side effects, such as changing the indices of subsequent elements. Additionally, `splice()` has to iterate over the entire array, making it less efficient than creating a new array.
* **Copy to new**:
	+ Pros: Creating a new array using the spread operator is more predictable and controlled, as each element is copied individually. This approach avoids modifying the original array and ensures that indices remain consistent.
	+ Cons: Creating a new array can be memory-intensive, especially for large datasets. Additionally, this approach requires creating an additional array, which might incur some overhead.

**Library**

There is no library explicitly mentioned in this benchmark definition. However, it's worth noting that the `Array.from()` method used to generate the random array might be considered a built-in function or a polyfill if not supported by older browsers.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntaxes mentioned in this benchmark definition.

**Alternatives**

Other alternatives for measuring the performance of array operations could include:

* Using `slice()` instead of `splice()`
* Using `filter()` and `map()` to create a new array
* Using `reduce()` to remove elements from the original array
* Using native WebAssembly support (if available) to optimize array operations

Keep in mind that these alternatives might have different trade-offs in terms of memory usage, performance, and code complexity.

Related benchmarks:

Splice vs new Array Modulo (version: 0)

Comparing performance of: Splice vs Copy to new

Created: 2 years ago by: Guest

Jump to the latest result

Splice

Copy to new

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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