Benchmark: dataset vs getAttribute speed123

HTML Preparation code:

<div class="data" data-test="lorem">test dataset</h3>

Tests:

dataset
delete document.querySelector('.data').dataset.test;
getAttribute
document.querySelector('.data').removeAttribute('test');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
dataset
getAttribute

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 months ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 17_6 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.6 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 17 on iOS 17.6

View result in a separate tab

Test name	Executions per second
dataset	4391518.0 Ops/sec
getAttribute	4596005.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

The provided JSON represents a benchmarking test case created on MeasureThat.net, which allows users to compare the performance of different approaches in JavaScript.

**Benchmark Definition**

The benchmark definition is a set of instructions that define how to create and run the test. In this case, there are two tests:

1. "dataset": This test creates an HTML element with a `data-test` attribute and then deletes it using the `removeAttribute()` method.
2. "getAttribute": This test creates an HTML element with a `data-test` attribute and then retrieves its value using the `getAttribute()` method.

**Options Compared**

The two tests compare two different approaches to delete or retrieve data from an element:

1. **Dataset approach**: The "dataset" test uses the `dataset` property of the element to store and retrieve data. This approach is particularly useful when working with HTML elements that have custom attributes.
2. **Attribute removal approach**: The "getAttribute" test uses the `removeAttribute()` method to delete the attribute from the element.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

* **Dataset approach (dataset)**:
	+ Pros: Efficient for storing and retrieving custom data attributes.
	+ Cons: May not be optimized for performance in all browsers or versions.
* **Attribute removal approach (getAttribute)**:
	+ Pros: Often faster and more efficient than using the `dataset` property, especially when working with attribute names that are unlikely to change frequently.
	+ Cons: May not be suitable for use cases where data attributes need to be stored and retrieved.

**Library Usage**

There is no explicit library usage mentioned in this benchmark. However, some JavaScript features, such as the `dataset` property, were introduced in modern browsers (e.g., Chrome 39+, Firefox 34+) and are supported by MeasureThat.net.

**Special JS Features or Syntax**

The "dataset" test utilizes the `dataset` property, which is a feature introduced in modern JavaScript. This property allows HTML elements to store and retrieve custom data attributes using dot notation (e.g., `.data.test`). The "getAttribute" test uses the `removeAttribute()` method, which is a standard JavaScript API.

**Other Alternatives**

If you're looking for alternative approaches or variations on these benchmarks, consider exploring other JavaScript performance testing tools and libraries, such as:

1. **Benchmark.js**: A popular benchmarking library for Node.js.
2. **jsperf**: Another well-known benchmarking tool for JavaScript.
3. **Google's Benchmark**: A high-performance benchmarking framework developed by Google.

Keep in mind that each of these alternatives might have different features, use cases, and performance characteristics compared to MeasureThat.net.

I hope this explanation helps you understand the provided benchmark and its underlying concepts!

Related benchmarks:

dataset vs getAttribute speed123 (version: 0)

Comparing performance of: dataset vs getAttribute

Created: 5 months ago by: Guest

Jump to the latest result

dataset

getAttribute

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):