Benchmark: Clean up strategy

HTML Preparation code:

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

Script Preparation code:

var i = 0, MAX_SIZE = 10, a;
var loop = 100
var map = new Map();

for (i = 0; i < MAX_SIZE; i++) {
    map.set(i, i * i);
}

Tests:

Clean 1 by 1
/*When writing async/deferred tests, use `deferred.resolve()` to mark test as done*/ for (i = 0; i < loop; i++) { map.set(i, i * i); if (map.size >= MAX_SIZE) { let count = MAX_SIZE / 2; for (const key of map.keys()) { map.delete(key); count--; if (count <= 0) { break; } } } }
Clean by half
/*When writing async/deferred tests, use `deferred.resolve()` to mark test as done*/ for (i = 0; i < loop; i++) { map.set(i, i * i); if (map.size >= MAX_SIZE) { const itValue = map.keys() if(!itValue.done) { map.delete(itValue.value) } } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Clean 1 by 1
Clean by half

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36

Browser/OS: Chrome 133 on Mac OS X 10.15.7

View result in a separate tab

Clean by half

191K/s

Clean 1 by 1

153K/s

View exact numbers

Test name	Executions per second
✓ Clean by half	190,680 Ops/sec
Clean 1 by 1	153,397 Ops/sec

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

LLMs can make mistakes. Check important info.

The benchmark defined under "Clean up strategy" compares two different approaches to deleting entries from a JavaScript `Map` data structure. Both methods are evaluated in terms of performance, specifically judging how many operations can be executed per second. Let's break down the test cases and their implications.

### Test Cases

1. **Clean 1 by 1**
   - **Benchmark Definition**: This test iterates through the `Map` and deletes keys one at a time. It uses the `.delete()` method on the `Map`, which involves checking the size of the `Map` and iterating over its keys to perform deletion.
   - **Performance Implications**: 
     - Pros: This method ensures that entries are removed one by one, allowing for precise control over which items are deleted based on order and condition.
     - Cons: Deleting a single key in each iteration can be inefficient; it might incur more overhead due to multiple calls to the `.delete()` method, especially if the size of the `Map` grows larger than `MAX_SIZE`.

2. **Clean by Half**
   - **Benchmark Definition**: Instead of removing keys one by one, this test reuses the `keys()` iterator of the `Map` and performs a deletion of the first key found once the size criterion is met (`map.size >= MAX_SIZE`). This approach could be interpreted as a more efficient bulk removal strategy since it immediately acts on the iterator.
   - **Performance Implications**:
     - Pros: By employing the iterator, it may reduce the number of operations and alleviate the overhead of multiple `.delete()` calls, potentially resulting in higher performance under certain conditions.
     - Cons: Depending on the use case, this method might lack granularity; you could lose specific items you might want to preserve if you aren't modifying the approach to choose keys based on certain logic.

### Performance Results

From the benchmark results, the "Clean by half" approach outperforms the "Clean 1 by 1" method, achieving approximately **190,679.64 executions per second compared to 153,396.70 executions per second** for the other method. This clearly indicates that bulk deletion strategies can potentially provide better performance when dealing with collections, especially when iterating through large-sized data structures.

### Considerations

- **Map vs. Other Data Structures**: While using a `Map` allows for efficient key-value storage and retrieval, if the primary operation involves frequent deletions, other data structures, like arrays or sets, might be worth considering based on specific requirements (like maintaining insertion order or providing unique constraints).
  
- **Scalability**: If the `Map` is expected to grow significantly larger than `MAX_SIZE`, the performance characteristics could change, impacting whether one approach scales better than the other in a real-world application.

- **Alternatives**: The approach could also be compared to using `Set` data structures if the need is simply to store unique values without key-pair relationships. Furthermore, different strategies such as batching deletions (removing multiple keys before the need to check and iterate again) may also improve efficiency.

### Conclusion

Ultimately, the benchmark highlights the importance of choosing the right algorithm and data structure for specific tasks in software engineering. Understanding the implications of different approaches helps developers make educated decisions aimed at optimizing performance and managing the resources effectively.

Clean up strategy (version: 1)

Comparing performance of: `Clean 1 by 1 vs Clean by half`

Created: one year ago by: Guest

Go to the latest result

Clean 1 by 1

Clean by half

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Clean up strategy (version: 1)

Comparing performance of: Clean 1 by 1 vs Clean by half

Created: one year ago by: Guest

Go to the latest result

Clean 1 by 1

Clean by half

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: `Clean 1 by 1 vs Clean by half`