Benchmark: test slice czourhgirzjgprz

HTML Preparation code:

<!--your preparation HTML code goes here-->

Script Preparation code:

/*your preparation JavaScript code goes here
To execute async code during the script preparation, wrap it as function globalMeasureThatScriptPrepareFunction, example:*/
async function globalMeasureThatScriptPrepareFunction() {
    // This function is optional, feel free to remove it.
    // await someThing();
}

Tests:

Me case
const arr = [1, 2, 3, 4, 5, 6, 7, 8, 9] const displayedFilters = [] const hiddenFilters = new Set() for (let i = 0; i < arr.length; i += 1) { if (i < 2) { displayedFilters.push(arr[i]); } else { hiddenFilters.add(arr[i]); } }
Fab case
const arr = [1, 2, 3, 4, 5, 6, 7, 8, 9] const splitSelectedFilters = (arr) => ({ displayedFilters: arr.slice(0, 2), hiddenFilters: new Set(arr.slice(2)), });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Me case
Fab case

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36

Browser/OS: Chrome 135 on Linux

View result in a separate tab

Fab case

182.2M/s

Me case

7.9M/s

View exact numbers

Test name	Executions per second
✓ Fab case	182,199,776 Ops/sec
Me case	7,873,740 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated one year ago):

LLMs can make mistakes. Check important info.

In this benchmark, two JavaScript approaches for processing an array of numbers are compared. The main objective is to determine which method is more efficient at separating elements into two categories: `displayedFilters` and `hiddenFilters`.

### Approaches Compared

1. **Me Case**:
   ```javascript
   const arr = [1, 2, 3, 4, 5, 6, 7, 8, 9];

const displayedFilters = [];
   const hiddenFilters = new Set();

for (let i = 0; i < arr.length; i += 1) {
       if (i < 2) {
           displayedFilters.push(arr[i]);
       } else {
           hiddenFilters.add(arr[i]);
       }
   }
   ```
   - **Description**: This approach uses a loop to iterate through the array `arr`. For the first two indices, it pushes the numbers into the `displayedFilters` array; for all subsequent indices, it adds them to a `Set` called `hiddenFilters`.
   - **Pros**:
     - Direct control over how elements are processed.
     - Utilizes the `Set` data structure for `hiddenFilters`, which ensures uniqueness.
   - **Cons**:
     - The loop contains conditional logic that may add overhead.
     - Potentially more verbose and less expressive than alternative functions.

2. **Fab Case**:
   ```javascript
   const arr = [1, 2, 3, 4, 5, 6, 7, 8, 9];

const splitSelectedFilters = (arr) => ({
       displayedFilters: arr.slice(0, 2),
       hiddenFilters: new Set(arr.slice(2)),
   });
   ```
   - **Description**: This method uses the `Array.prototype.slice()` method to extract the first two elements into `displayedFilters`. It then converts the remainder of the array starting from index 2 into a `Set` for `hiddenFilters`.
   - **Pros**:
     - More concise and expressive; utilizes built-in array methods to improve readability.
     - Eliminates the need for manual condition-checking within loops, potentially leading to fewer errors.
   - **Cons**:
     - Creates intermediate arrays with `slice()`, which may have minor performance implications, particularly with large datasets.

### Performance Results
From the benchmark results, we see the following execution speeds:
- **Fab Case** (Function approach): 182,199,776 executions per second.
- **Me Case** (Loop approach): 7,873,740.5 executions per second.

Clearly, the `Fab Case` performs significantly better than the `Me Case`.

### Considerations and Alternatives
When comparing the two approaches, engineers might consider:

1. **Readability vs. Performance**: While the `Me Case` may offer more detailed control, the `Fab Case` not only performs better but is also easier to read and maintain. When working in teams, cleaner code can lead to easier onboarding and fewer bugs.

2. **Use of Data Structures**: The `Set` is used in both examples to ensure unique items in `hiddenFilters`. When designing applications, the choice of data structures can affect both performance and functionality. For example, if duplicates were allowed in `hiddenFilters`, a standard array might be more appropriate.

3. **Alternative Approaches**: Other alternatives might include using `forEach()`, `map()`, or even external libraries like Lodash with utility functions designed for these kinds of tasks (`_.partition`, for example). However, using native methods is generally preferred for performance unless libraries provide significantly enhanced capabilities.

### Conclusion
In summary, this benchmark demonstrates two different methods for processing and categorizing elements in an array. The results strongly favor using higher-order functions such as `slice()` combined with concise function definitions for better performance and readability in JavaScript.

test slice czourhgirzjgprz (version: 1)

Comparing performance of: `Me case vs Fab case`

Created: one year ago by: Guest

Go to the latest result

Me case

Fab case

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one year ago):

test slice czourhgirzjgprz (version: 1)

Comparing performance of: Me case vs Fab case

Created: one year ago by: Guest

Go to the latest result

Me case

Fab case

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one year ago):

Related benchmarks

Comparing performance of: `Me case vs Fab case`