Benchmark: Array: [] vs at()

Script Preparation code:

var size = 0xffffff;
var data = Array.from({length: size}, (_, index) => index);

Tests:

Array[]
let sum = 0 for (let i; i < size; i++) { sum += data[i] }
Array.at()
let sum = 0 for (let i; i < size; i++) { sum += data.at(i) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array[]
Array.at()

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36

Browser/OS: Chrome 129 on Windows

View result in a separate tab

Test name	Executions per second
Array[]	9895164.0 Ops/sec
Array.at()	9902255.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark and its test cases, explaining what's being tested, the pros and cons of each approach, and other considerations.

**Benchmark Overview**

MeasureThat.net is a website where users can create and run JavaScript microbenchmarks. The provided benchmark measures the performance difference between two approaches to access elements in an array: using the `at()` method versus simply indexing into the array (`[]`).

**Script Preparation Code**

The script preparation code creates an array of a large size (`0xffffff`) and assigns each index a value starting from 0.

```javascript
var size = 0xffffff;
var data = Array.from({length: size}, (_, index) => index);
```

This code ensures that the array has a sufficient number of elements to be tested, without specifying any specific value for the `size` variable. This allows users to test different sizes and see how performance scales.

**Html Preparation Code**

The html preparation code is empty (`null`). This suggests that the benchmark doesn't rely on any specific HTML structure or layout, which is good because it makes the benchmark more portable and focused on JavaScript performance.

**Individual Test Cases**

There are two individual test cases:

1. **"Array[]"`**
	* Benchmark Definition: `let sum = 0; for (let i; i < size; i++) { sum += data[i]; }`
	* This approach uses a traditional indexing method to access elements in the array.
2. **"Array.at()"** 
	* Benchmark Definition: `let sum = 0; for (let i; i < size; i++) { sum += data.at(i); }`
	* This approach uses the `at()` method to access elements in the array.

**Pros and Cons of Each Approach**

1. **"Array[]"**
	* Pros:
		+ Widely supported across browsers and environments.
		+ Easy to understand and implement for most developers.
	* Cons:
		+ Can be slower than `at()` due to the need to perform index calculations.
2. **"Array.at()"**
	* Pros:
		+ Faster than traditional indexing due to optimized implementation.
		+ Reduces the risk of integer overflows or out-of-bounds errors.
	* Cons:
		+ Requires support for the `at()` method, which might not be available in older browsers.
		+ Might have performance overhead due to function calls.

**Library and Purpose**

There is no explicit library mentioned in the benchmark code. However, the `Array.from()` method is used to create the array, which is a part of the ECMAScript 2015 standard (ES6). This means that modern browsers and environments support this feature without any additional libraries.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntaxes used in this benchmark. The code only relies on standard JavaScript syntax and methods.

**Other Considerations**

When interpreting the benchmark results, keep in mind:

* **Size**: The size of the array being tested can significantly impact performance.
* **Browser Support**: Make sure that the test cases are run in environments that support both `Array.at()` and traditional indexing.
* **Optimizations**: Modern browsers often perform optimizations that can affect benchmark results. These should be considered when interpreting the results.

**Alternatives**

Some alternative approaches to accessing elements in an array include:

1. Using a library like Lodash, which provides optimized functions for common array operations.
2. Implementing custom optimization techniques, such as memoization or caching.
3. Using parallel processing or concurrent execution to improve performance.

Keep in mind that these alternatives might require additional setup and configuration, and may not be supported across all environments.

Related benchmarks:

Array: [] vs at() (version: 1)

Comparing performance of: Array[] vs Array.at()

Created: one year ago by: Registered User

Jump to the latest result

Array[]

Array.at()

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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