Benchmark: Attribute Parser 2

HTML Preparation code:

<div class="main"></div>

Script Preparation code:

const largeTemplate = `<div class="large" data-options="{&quot;smSlowUrl&quot;:&quot;https://kstatic.googleusercontent.com/files/35f440ff890c6189ae31c65dd8471dc64cc11c2b7e155980a1905b22e0d1efff89de049d0907d05f8967ec1e54dea7d7febe1327d70ac8f35bb8d17a059fcaf2&quot;,&quot;smFastUrl&quot;:&quot;https://kstatic.googleusercontent.com/files/c535a43e6fa85b3fd539a235907e8dd385aa19a62094ad815a1679aa34a0129bea2c4c570217e9ff11c884bffed237e2dd75954fd91be1a1bdd44e4ca4256999&quot;,&quot;lgSlowUrl&quot;:&quot;https://kstatic.googleusercontent.com/files/c146fa9200c5a4e4be6ea7d0586bc2c566d8c26d31523e778fee3b17adf078470e28924f107ff7a5cb6e83a67ce804b8dcdb23a54080b4445d5b03ae5d407c5a&quot;,&quot;lgFastUrl&quot;:&quot;https://kstatic.googleusercontent.com/files/f27e919a897b995d58ff3aa0717f09a756c09941362324208bde96c89c3f1fedb14bd70d7a3ea11f64b65b3409f78fa2b4c7b679f3d1c7f8f31576ef563f21a9&quot;,&quot;pauseLabel&quot;:&quot;Pause hero football video&quot;,&quot;playLabel&quot;:&quot;Play hero football video&quot;}">Large</div>`;
const smallTemplate = `<div class="small" 
data-a="[&quot;foo&quot;, &quot;bar&quot;, &quot;blah&quot]"
data-b="https://kstatic.googleusercontent.com/files/c535a43e6fa85b3fd539a235907e8dd385aa19a62094ad815a1679aa34a0129bea2c4c570217e9ff11c884bffed237e2dd75954fd91be1a1bdd44e4ca4256999" 
data-c="https://kstatic.googleusercontent.com/files/c146fa9200c5a4e4be6ea7d0586bc2c566d8c26d31523e778fee3b17adf078470e28924f107ff7a5cb6e83a67ce804b8dcdb23a54080b4445d5b03ae5d407c5a" 
data-d="https://kstatic.googleusercontent.com/files/f27e919a897b995d58ff3aa0717f09a756c09941362324208bde96c89c3f1fedb14bd70d7a3ea11f64b65b3409f78fa2b4c7b679f3d1c7f8f31576ef563f21a9" 
data-e="Pause hero football video" 
date-f="Play hero football video&">Small</div>`;

let i = 1000;
const root = document.querySelector('.main');
while (i--) {
    root.innerHTML += largeTemplate;
    root.innerHTML += smallTemplate;
}

Tests:

Atomic Parser
const largeNodes = Array.from(document.querySelectorAll('.large')); largeNodes.map((node) => { const options = node.getAttribute('data-options'); return JSON.parse(options); });
Piecemeal Parser
const smallNodes = Array.from(document.querySelectorAll('.small')); smallNodes.map((node) => { const data = node.dataset; const a = JSON.parse(data.a); const b = '' + data.b; const c = '' + data.c; const d = '' + data.d; const e = '' + data.e; const f = '' + data.f; return {a, b, c, d, e, f}; });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Atomic Parser
Piecemeal Parser

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/122.0.0.0 Safari/537.36

Browser/OS: Chrome 122 on Mac OS X 10.15.7

View result in a separate tab

Piecemeal Parser

1K/s

Atomic Parser

1K/s

View exact numbers

Test name	Executions per second
✓ Piecemeal Parser	1,270 Ops/sec
Atomic Parser	1,085 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into explaining the benchmark test.

**Benchmark Definition:**

The benchmark is designed to measure the performance of two different approaches to parse attribute data from HTML elements. The test case consists of two parts:

1. **Atomic Parser:** This approach uses the `getAttribute` method to retrieve the `data-options` attribute from each `.large` element, and then parses the resulting string as JSON using `JSON.parse`.
2. **Piecemeal Parser:** This approach uses the `dataset` property to access individual attributes (e.g., `a`, `b`, `c`, etc.) directly from each `.small` element, without needing to concatenate them into a single string.

**Options Compared:**

The two approaches differ in how they process attribute data:

1. **Atomic Parser:** This approach:
	* Uses the `getAttribute` method to retrieve the entire `data-options` attribute as a single string.
	* Parses this string as JSON using `JSON.parse`.
2. **Piecemeal Parser:** This approach:
	* Uses the `dataset` property to access individual attributes (e.g., `a`, `b`, etc.) directly from each element.
	* Converts these attribute values into JavaScript variables (e.g., `const a = data.a;`) and returns an object with all the properties.

**Pros and Cons:**

1. **Atomic Parser:**
	+ Pros:
		- Simplifies parsing logic, as it only needs to handle one JSON string.
		- Could be more efficient if the attribute data is large and doesn't fit in a single JavaScript variable.
	* Cons:
		- Requires concatenating multiple attributes into a single string, which might lead to performance issues for large datasets.
		- Needs to handle JSON parsing errors, which can slow down execution.
2. **Piecemeal Parser:**
	+ Pros:
		- Can process individual attributes more efficiently, as they don't need to be concatenated or parsed into a single string.
		- Reduces the risk of parsing errors, as each attribute is processed individually.
	* Cons:
		- Requires accessing and processing multiple dataset properties, which might increase overhead for larger datasets.

**Library Usage:**

None of the provided benchmark definitions explicitly use any external libraries. However, it's worth noting that `JSON.parse()` relies on the built-in JSON parsing functionality in JavaScript engines.

**Special JS Features/Syntax:**

No special JavaScript features or syntax are used in these benchmark definitions. The code is straightforward and follows standard JavaScript practices.

**Other Alternatives:**

If this benchmark were to be modified or expanded, alternative approaches might include:

1. **Using a dedicated attribute parser library**, which could provide optimized parsing logic for specific use cases.
2. **Implementing a custom parsing function**, tailored to the specific requirements of the attribute data (e.g., handling specific attribute types or formats).
3. **Exploring other data processing techniques**, such as using a streaming approach to process attribute data in real-time.

By understanding these differences and trade-offs, developers can better choose between various approaches for parsing attribute data in their own projects.

Attribute Parser 2 (version: 0)

1 large object-attribute v. smaller attributes

Comparing performance of: `Atomic Parser vs Piecemeal Parser`

Created: 2 years ago by: Guest

Go to the latest result

Atomic Parser

Piecemeal Parser

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Attribute Parser 2 (version: 0)

1 large object-attribute v. smaller attributes

Comparing performance of: Atomic Parser vs Piecemeal Parser

Created: 2 years ago by: Guest

Go to the latest result

Atomic Parser

Piecemeal Parser

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `Atomic Parser vs Piecemeal Parser`