Benchmark: slice vs do while 2

Script Preparation code:

var data = Array(1000).fill(0).map((v, i) => i);

Tests:

slice
var copy = data.slice(0, 100);
do while
var i = 0; var copy = []; do { copy.push(data[i]); i++; } while(i < 100)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
do while

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36

Browser/OS: Chrome 119 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
slice	17427784.0 Ops/sec
do while	226587.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

**What is being tested?**

MeasureThat.net is testing two different approaches to create a copy of an array: `slice()` and a custom implementation using a `do-while` loop.

The test case uses an array of 1000 elements, which are initialized with incremental values starting from 0. This allows us to focus on the creation of the copy without worrying about data allocation or other external factors.

**Options compared**

We have two options being compared:

1. **`slice()`:**
	* Creates a new array by slicing the original array.
	* Returns a shallow copy of the original array, meaning that it only copies the references to the elements, not the elements themselves.
	* Pros: Fast and simple, suitable for most use cases.
	* Cons: Can lead to issues when dealing with mutable objects or large arrays.
2. **Custom `do-while` loop implementation:**
	* Pushes each element from the original array into a new array using a custom loop.
	* Pros: Allows for more control over the creation process, potentially leading to better performance in certain scenarios.
	* Cons: More complex and slower than the `slice()` method.

**Other considerations**

When choosing between these two approaches, we should consider the following factors:

* **Performance:** In most cases, `slice()` is sufficient and fast enough. However, if you need more control over the creation process or are dealing with extremely large arrays, the custom implementation might be worth considering.
* **Memory usage:** When working with large arrays, it's essential to consider memory usage. The `do-while` loop implementation might lead to higher memory allocation due to the creation of a new array.
* **Code readability and maintainability:** While the custom implementation provides more control, it also increases complexity and makes the code harder to read.

**Library or special JS feature**

In this case, we're not using any external libraries. However, if you were to use a library like Lodash, you might find its `slice` function useful. But for simplicity, we'll focus on these two core methods.

**Special JS features**

There are no special JavaScript features being used in this benchmark beyond the standard syntax and built-in functions.

Now that we've explored the test case, let's take a look at some alternatives:

* **Other array creation methods:** You might want to explore other methods like `Array.from()`, `Array.prototype.concat()`, or even using `Buffer` for large arrays.
* **Native WebAssembly (WASM) implementations:** If you're interested in exploring the performance of WASM-based solutions, MeasureThat.net provides a range of benchmarks that can be used as a starting point.

Keep in mind that JavaScript is a dynamic language with many nuances and edge cases. When it comes to performance-critical code, it's essential to consider various factors, including hardware, software, and algorithmic optimization techniques.

Related benchmarks:

slice vs do while 2 (version: 0)

Comparing performance of: slice vs do while

Created: 2 years ago by: Guest

Jump to the latest result

slice

do while

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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