Benchmark: Spread Operator: Array

Tests:

Spread array at head
const numbers = [1, 2, 3]; const result = [...numbers, 10];
Spread array at tail
const numbers = [1, 2, 3]; const result = [10, ...numbers];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread array at head
Spread array at tail

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its test cases.

**Benchmark Definition**

The benchmark is defined by a JSON object that describes two test cases: `Spread Operator: Array`. The goal of this benchmark is to compare the performance of JavaScript when using the spread operator (`...`) in different ways.

**Options Compared**

There are two options compared:

1. **Spreading at head**: This option involves creating a new array by spreading elements from an existing array and appending new elements at the end: `const result = [10, ...numbers];`.
2. **Spreading at tail**: This option involves creating a new array by spreading elements from an existing array and prepending new elements at the beginning: `const result = [...numbers, 10];`.

**Pros and Cons of Each Approach**

1. **Spreading at head**:
	* Pros: May be faster because it only requires appending elements to the end of the array.
	* Cons: Can lead to slower performance when dealing with large arrays, as it involves creating a new array and then modifying its length property.
2. **Spreading at tail**:
	* Pros: Can be more efficient for large arrays because it avoids modifying the length property of the resulting array.
	* Cons: May be slower due to the overhead of prepending elements to the beginning of the array.

**Library Usage**

There is no library explicitly mentioned in this benchmark. However, JavaScript's built-in `Array.prototype.push()` and `Array.prototype.unshift()` methods are used in both test cases.

**Special JS Features or Syntax**

The spread operator (`...`) is a new feature introduced in ECMAScript 2015 (ES6). It allows for more concise array creation and manipulation.

**Other Considerations**

* Performance: Both approaches have trade-offs regarding performance, depending on the size of the input arrays.
* Memory usage: Spreading at tail might use less memory than spreading at head because it avoids creating a new array with the same length as the original one.
* Code readability: Spreading at head can make code more readable for some developers, while others prefer spreading at tail.

**Alternative Approaches**

Other approaches to achieve similar results include:

1. Using `Array.prototype.concat()` and modifying the resulting array's length property: `const result = [...numbers].concat([10]);`
2. Using a loop to iterate over the elements of the original array: `let result = []; for (const num of numbers) { result.push(num); } result.push(10);`

However, these approaches may have different performance characteristics compared to using the spread operator.

Overall, this benchmark is designed to compare the performance of two specific use cases involving the JavaScript spread operator. By understanding the pros and cons of each approach, developers can choose the most efficient method for their use case.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36

Browser/OS: Chrome 90 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Spread array at head	26651440.0 Ops/sec
Spread array at tail	35164180.0 Ops/sec

Related benchmarks:

Spread Operator: Array (version: 2)

How To Accelerate the JavaScript Spread Operator https://dmitripavlutin.com/javascript-spread-operator-performance-optimization/

Comparing performance of: Spread array at head vs Spread array at tail

Created: 3 years ago by: Registered User

Jump to the latest result

Spread array at head

Spread array at tail

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):