Benchmark: 123456tg - MeasureThat.net

HTML Preparation code:

<div data-role="list"><div class="title">loc</div><span class="area" title="tesr" data-id="9" data-index="0">test</span><span class="area" title="test2" data-index="1">test2</span><span class="area" title="test3" data-index="2">test3</span></div>

Script Preparation code:

function getMoveList(){
	let list = document.querySelector('[data-role="list"]');
	let area = [...list.querySelectorAll('.area[title]')];
	let house = [...list.querySelectorAll('.house[title]')];
	let npc = [...list.querySelectorAll('.npc[title]')];
	return {area, house, npc, all: [...area, ...house, ...npc]};
}

Tests:

t2
function getRoute(name){ return getMoveList().area.find(el => el.innerText.includes(name)); } for(var i=0;i<100;i++){ let res = getRoute('test2'); console.log(res); }
t1
var Routes = null; Routes = getMoveList().area; function getRoute(name){ if (Routes == null) return console.log('Routes == null'); return Routes.find(el => el.innerText.includes(name)); } for(var i=0;i<100;i++){ let res = getRoute('test2'); console.log(res); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
t2
t1

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 12 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Windows

View result in a separate tab

Test name	Executions per second
t2	132.8 Ops/sec
t1	166.8 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 12 days ago):

LLMs can make mistakes. Check important info.

The benchmark defined in the provided JSON tests two different implementations of a function, `getRoute`, which retrieves an element from a list of areas based on a name passed to it. The benchmark aims to compare the performance of these two implementations under similar conditions.

### Overview of the Implementations

1. **Test Case T1**: This implementation initializes a `Routes` variable that holds the list of area elements retrieved from the `getMoveList` function. It checks if `Routes` is null and then uses the `.find()` method to locate an element whose inner text includes the specified name.

```javascript
   var Routes = null;
   Routes = getMoveList().area;
   function getRoute(name) {
       if (Routes == null) return console.log('Routes == null');
       return Routes.find(el => el.innerText.includes(name));
   }
   ```

**Pros**:
   - The retrieval of the `Routes` variable happens only once, which means subsequent calls to `getRoute` skip the process of discovering the area elements from the DOM again.
   - It is more efficient when the function is called multiple times, as it minimizes repeated DOM queries.

**Cons**:
   - If the structure of the DOM changes after `getMoveList` is first called, `Routes` would not reflect those changes since it captures the list once at the start.
   - More memory is used to store the list in the variable `Routes`.

2. **Test Case T2**: This implementation calls the `getMoveList` function each time `getRoute` is executed to create a new list, and then it finds the matching element in that current list.

```javascript
   function getRoute(name) {
       return getMoveList().area.find(el => el.innerText.includes(name));
   }
   ```

**Pros**:
   - It always retrieves the latest state of the DOM each time the function is called, which means it adapts to any changes in the area elements.
   - Simpler implementation without the need for a separate variable.

**Cons**:
   - Repeatedly querying the DOM can lead to performance degradation, especially if the `getRoute` function is called multiple times since each call triggers the overhead of a new DOM query.
   - Possibly higher latency due to the repetitive operations of gathering elements.

### Performance Results

From the benchmark results, we see:
- **Test Case T1 executed at approximately 166.80 executions per second**.
- **Test Case T2 executed at approximately 132.78 executions per second**.

This shows that Test Case T1 has a significantly better performance due to its design, emphasizing the benefits of caching results from expensive operations, such as DOM manipulation.

### Other Considerations

**Alternatives**:
- **Memoization**: An alternative to consider is memoization, where you could cache the results from `getRoute` based on the `name` value passed. This allows you to achieve a balance between rerunning the function for updated DOM and using cached results for repeated queries of the same name.
  
- **Using a JS Framework**: Frameworks/libraries like React, Vue, or Angular often provide state management features that can automatically update the UI when the underlying data changes. This would reduce the need to manually handle DOM querying and improve performance.

- **Using Virtual DOM**: By employing a virtual DOM approach (like React), changes to the actual DOM would be minimized, thus often leading to better performance. This is especially beneficial when dealing with frequently updated lists.

In conclusion, the benchmark showcases the trade-offs between caching data (which improves performance for repeated operations) and immediate accuracy in a changing environment, with the first approach proving significantly faster but potentially less responsive to real-time changes in the DOM.

Related benchmarks:

123456tg (version: 1)

Comparing performance of: t2 vs t1

Created: 12 days ago by: Guest

Jump to the latest result

t2

t1

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 12 days ago):