Benchmark: return vs spread

Script Preparation code:

var arr = [];
for(var i = 0; i < 10000; i++) {
  arr.push(i);
}

Tests:

spread
[...arr]
return
arr

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
spread
return

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

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Let's break down the provided JSON data and explain what is being tested.

**Benchmark Definition**
The benchmark definition is an object that represents the test to be performed. In this case, it contains two properties:

* `Name`: a unique name for the benchmark
* `Description`: an optional description of the benchmark (in this case, null)
* `Script Preparation Code` and `Html Preparation Code`: these are used to prepare the environment for running the benchmark

In this specific benchmark definition, there is only one script preparation code:

* `var arr = [];`
	+ This creates an empty array in JavaScript
	+ The array will be used as a test subject for the benchmark
* There is no HTML preparation code provided.

**Individual Test Cases**
The individual test cases are arrays of objects that represent specific scenarios to be tested. In this case, there are only two test cases:

* `Test Case 1: "spread"`
	+ `Benchmark Definition`: `"[...arr]"`
		- This is the JavaScript spread operator (also known as the rest parameter syntax) being used to create a new array containing all elements of the original `arr` array
		- The `arr` array was created in the script preparation code and has 10,000 elements
* `Test Case 2: "return"`
	+ `Benchmark Definition`: `arr`
		- This is simply returning the contents of the `arr` array as a single value

**Library and Special JS Features**
There are no libraries being used in this benchmark. However, it's worth noting that the use of the spread operator (`"...arr"`) is a relatively new JavaScript feature introduced in ECMAScript 2018.

**Options Compared**
The two test cases are comparing the performance of using the `spread` operator versus returning the array directly. This comparison highlights the trade-off between creating a new array (using `...arr`) and simply returning the existing array (`arr`).

Pros and Cons:

* Using `...arr`: creates a new array object, which can be more efficient in certain situations
	+ Pros: avoids modifying the original array, potentially leading to better performance
	+ Cons: requires an additional object creation step, which may incur some overhead
* Returning the array directly (`arr`): modifies the existing array and returns it as a single value
	+ Pros: simpler operation, potentially faster execution
	+ Cons: modifies the original array, which may lead to unexpected side effects

**Other Considerations**

In general, when deciding between these two approaches, consider the following factors:

* Use `...arr` if:
	+ You need to create a new array without modifying the original
	+ You're working with large datasets and want to avoid reassigning values in place
* Return the array directly (`arr`) if:
	+ You're working with small datasets or don't care about creating a new object
	+ You prioritize simplicity and performance over explicit array creation

**Alternative Approaches**

Some alternative approaches you might consider:

* Using `Array.from()` to create a new array from an existing iterable (e.g., `Array.from(arr)`)
* Creating a new array using `new Array()`, like this: `[...arr] = new Array(arr.length);`
* Using a library or utility function that can handle this specific use case, such as a JavaScript array manipulation library.

Keep in mind that these alternatives might not offer significant performance benefits over the original approach and may introduce additional complexity.

Related benchmarks:

return vs spread (version: 0)

Comparing performance of: spread vs return

Created: 6 years ago by: Guest

Jump to the latest result

spread

return

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):