Benchmark: destructure vs pop and clone

Script Preparation code:

var arr = []

for (let i = 0; i < 50; i++) {
    arr.push(i);
}

Tests:

destruc
var [firstElem, ...rest] = arr;
clone + pop
var clone = [...arr]; var lastElem = arr.pop();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
destruc
clone + pop

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/104.0.5112.102 Safari/537.36 Edg/104.0.1293.70

Browser/OS: Chrome 104 on Windows

View result in a separate tab

Test name	Executions per second
destruc	1499832.6 Ops/sec
clone + pop	1969426.5 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

I'd be happy to help explain what's being tested in this benchmark.

The provided JSON represents a JavaScript microbenchmark that measures the performance difference between two approaches: destructuring assignment and using `Array.prototype.pop()` followed by cloning the array.

**Script Preparation Code**

The script preparation code creates an array `arr` with 50 elements, where each element is a number from 0 to 49. This array will be used as the input for both benchmarking approaches.

**Benchmark Definition JSON**

The benchmark definition contains two test cases:

1. **Destructuring assignment**: `"var [firstElem, ...rest] = arr;"`
This approach uses destructuring assignment to extract the first element (`firstElem`) and the rest of the elements (`rest`) from the array `arr`. The resulting values are stored in separate variables.

2. **Using `Array.prototype.pop()` followed by cloning**: `"var clone = [...arr];\r\nvar lastElem = arr.pop();"`
This approach creates a shallow copy of the array `arr` using the spread operator (`[...arr]`). It then removes and stores the last element from the original array using `Array.prototype.pop()`, leaving an empty array with all elements except the first one.

**Comparison**

The benchmark compares the performance of these two approaches:

1. **Destructuring assignment**: This approach is generally faster because it only requires a single operation to extract the desired values, without creating intermediate arrays.
2. **Using `Array.prototype.pop()` followed by cloning**: This approach is slower because it involves two operations: popping an element from the array and then cloning the entire array.

**Pros and Cons**

1. **Destructuring assignment**:
	* Pros: Faster, more concise, and easier to read.
	* Cons: May not be as effective for extracting all elements or modifying the original array.
2. **Using `Array.prototype.pop()` followed by cloning**:
	* Pros: Can be used to extract a single element while leaving the rest of the array intact.
	* Cons: Slower, more verbose, and may consume more memory.

Other considerations:

1. **Library usage**: Neither approach uses any external libraries, making them language-agnostic.
2. **Special JS features or syntax**: There are no special JS features or syntax used in this benchmark.
3. **Alternatives**:
	* For destructuring assignment, you can also use `let [firstElem] = arr;` to extract only the first element.
	* For using `Array.prototype.pop()` followed by cloning, you could use `var lastElem = arr[arr.length - 1];` to extract the last element without cloning.

In summary, this benchmark measures the performance difference between two approaches for extracting elements from an array: destructuring assignment and using `Array.prototype.pop()` followed by cloning. The results indicate that destructuring assignment is generally faster, making it a more efficient choice when possible.

Related benchmarks:

destructure vs pop and clone (version: 0)

Comparing performance of: destruc vs clone + pop

Created: 2 years ago by: Guest

Jump to the latest result

destruc

clone + pop

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):