kevinjqliu commented on issue #542:
URL: https://github.com/apache/iceberg-python/issues/542#issuecomment-2016892964

   I asked an AI for answers...
   #### Prompt
   ```
   theres a bug in @pyarrow.py function `project_table` and `_task_to_table` 
   
   can you find the bug? it has to do with concurrency 
   ```
   
   #### Answer
   The bug in the 
[project_table](https://github.com/apache/iceberg-python/blob/6989b92c2d449beb9fe4817c64f619ea5bfc81dc/pyiceberg/io/pyarrow.py#L1044)
 and 
[_task_to_table](https://github.com/apache/iceberg-python/blob/6989b92c2d449beb9fe4817c64f619ea5bfc81dc/pyiceberg/io/pyarrow.py#L941)
 functions within 
[pyiceberg/io/pyarrow.py](https://github.com/apache/iceberg-python/blob/6989b92c2d449beb9fe4817c64f619ea5bfc81dc/pyiceberg/io/pyarrow.py#L1)
 related to concurrency likely stems from the handling of futures and the 
potential for race conditions or incorrect handling of asynchronous execution. 
Specifically, the issue could be related to how the futures are managed and how 
results are collected from these futures.
   
   In the `project_table` function, futures are created and submitted to an 
executor for asynchronous execution of `_task_to_table` tasks. The results of 
these futures are then collected and processed. However, there are a few areas 
where concurrency issues could arise:
   
   1. **Early Termination and Future Cancellation**: The code attempts to stop 
early if a limit on the number of records is satisfied (`if limit is not None 
and sum(row_counts) >= limit:`). After this check, it cancels any remaining 
futures that have not yet completed. This approach assumes that the futures are 
executed and completed in a specific order, which might not always be the case. 
Futures could complete in any order, and cancelling futures prematurely could 
lead to incomplete processing or missed data.
   
       ```python
       if limit is not None:
           _ = [f.cancel() for f in futures if not f.done()]
       ```
   
   2. **Inconsistent Ordering and Result Collection**: The futures are stored 
in a `SortedList` based on their submission order to ensure consistent 
ordering. However, the actual execution order of these futures is not 
guaranteed, and relying on the submission order for processing results could 
lead to inconsistencies, especially if the task execution times vary 
significantly.
   
       ```python
       completed_futures: SortedList[Future[pa.Table]] = 
SortedList(iterable=[], key=lambda f: futures_index[f])
       ```
   
   3. **Shared State Management (`row_counts`)**: The `row_counts` list is 
shared among all tasks and is used to determine when the limit is reached. 
However, this shared state is updated after each future completes, without 
proper synchronization. This could lead to race conditions where multiple 
threads attempt to update the `row_counts` list simultaneously, leading to 
incorrect counts.
   
       ```python
       row_counts.append(len(arrow_table))
       ```
   
   To address these issues, consider the following:
   
   - **Reevaluate Early Termination Logic**: Ensure that the logic for early 
termination and future cancellation correctly accounts for the asynchronous 
nature of task execution. It may be safer to collect and process all results 
before deciding whether the limit has been reached, rather than cancelling 
futures prematurely.
   
   - **Synchronization of Shared State**: If shared state (like `row_counts`) 
must be updated by multiple tasks, ensure proper synchronization to prevent 
race conditions. This could involve using thread-safe data structures or 
explicit locking mechanisms.
   
   - **Ordering of Results**: If the order of results is important, consider 
collecting all future results first and then sorting or processing them based 
on the desired criteria, rather than relying on the order of future submission.
   
   These suggestions aim to mitigate potential concurrency issues in the 
`project_table` and `_task_to_table` functions, ensuring correct and consistent 
processing of tasks in an asynchronous environment.
   


-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: [email protected]

For queries about this service, please contact Infrastructure at:
[email protected]


---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]

Reply via email to