Benchmark: array test 3 - MeasureThat.net

Tests:

start
const array = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] const copy = [ 0, ...array ]
end
const array = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] const copy = [ ...array, 0 ]
push
const array = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] const copy = array.slice(0) copy.push(0)
unshift
const array = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] const copy = array.slice(0) copy.unshift(0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
start
end
push
unshift

Fastest: N/A

Slowest: N/A

Latest 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 break down the provided benchmark and explain what's being tested, compared, and analyzed.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmark that tests the performance of three different approaches to create a copy of an array:

1. Using `array = [0, ...array]` (also known as "spread operator" or "rest parameter" approach)
2. Using `const copy = [...array, 0]`
3. Using `const copy = array.slice(0) + [0]`

**Test Cases**

Each test case consists of a single line of code that creates a benchmark definition, which is a string representation of the JavaScript code to be executed. The three test cases are:

1. "start"
2. "end"
3. "push" (using `array = [0, ...array]`)
4. "unshift" (using `const copy = [...array, 0]`)

**Comparison**

The benchmark compares the performance of these four approaches:

* `start`: No operation is performed on the original array.
* `end`: The last element of the array is pushed to the end using the spread operator (`[...array, 0]`).
* `push`: The first element (0) is appended to the array using the spread operator (`[...array, 0]`).
* `unshift`: The first element (0) is prepended to the array using the spread operator (`[...array, 0]`).

**Pros and Cons**

Here's a brief summary of each approach:

1. **Spread Operator (`array = [0, ...array]`)**:
	* Pros: concise and readable syntax.
	* Cons: creates a new array with a reference to the original array's internal buffer, which can lead to issues when the original array is modified or garbage collected.
2. **Push and Unshift**:
	* Pros: more explicit and intuitive syntax, avoiding potential issues with spread operators.
	* Cons: may be less concise than spread operators.
3. **Slice** (`const copy = array.slice(0) + [0]`):
	* Pros: creates a new array without modifying the original array's internal buffer.
	* Cons: requires two operations (slice and concatenation), which can lead to performance overhead.

**Library and Special Features**

There is no library explicitly used in this benchmark. However, JavaScript features like spread operators (`...`) and rest parameters (`[...]`) are used.

No special JavaScript features or syntax are mentioned in the provided JSON.

**Alternatives**

If you're interested in alternative approaches for creating array copies, here are a few options:

* Using `Array.prototype.concat()` or `Array.prototype.push()`: These methods create a new array and add elements to it using push operations. While they may be slower than other approaches, they avoid potential issues with spread operators.
* Using `Array.prototype.slice()` with a filter: This method creates a new array by filtering the original array's elements.

Keep in mind that these alternatives might have different performance characteristics depending on the specific use case and JavaScript environment.

In summary, this benchmark provides an objective comparison of four approaches to create a copy of an array in JavaScript. By understanding the pros and cons of each approach, developers can choose the most suitable method for their specific needs.

Related benchmarks:

array test 3 (version: 0)

Comparing performance of: start vs end vs push vs unshift

Created: 6 years ago by: Guest

Jump to the latest result

start

end

push

unshift

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):