Files that changed from the base of the PR and between 2d0254f and e08fc37.

• backend/poetry.lock is excluded by !**/*.lock

• backend/Makefile (1 hunks)
• backend/app/processing/process_queue.py (7 hunks)
• backend/app/processing/process_scheduler.py (1 hunks)
• backend/app/repositories/process_repository.py (2 hunks)
• backend/pyproject.toml (1 hunks)
• backend/tests/processing/test_process_scheduler.py (1 hunks)

backend/Makefile (1)

Line range hint 1-20: LGTM! Makefile improvements enhance development workflow

The additions to the Makefile are well-implemented and provide valuable linting capabilities to the project. These changes will help maintain code quality and consistency. The new targets integrate well with the existing ones and follow the established patterns in the Makefile.

Great job on improving the development workflow! The new linting targets will be particularly useful for maintaining code quality across the project.

backend/pyproject.toml (1)

20-20: LGTM! Verify usage of the new dependency.

The addition of the schedule package (version ^1.2.2) aligns well with the PR objectives of refactoring the process queue. This lightweight scheduler is suitable for managing process execution timing, potentially in the new ProcessScheduler class mentioned in the AI summary.

To ensure proper integration, please run the following script to verify the usage of the schedule package in the codebase:

This will help confirm that the new dependency is being utilized as intended in the implementation.

✅ Verification successful

Verified Usage of 'schedule' Package

The schedule package (version ^1.2.2) has been successfully imported and utilized in backend/app/processing/process_scheduler.py. Specifically, schedule.every() and schedule.run_pending() functions are employed in the ProcessScheduler class to manage process execution timing.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Verify the usage of the 'schedule' package in the codebase

# Test: Search for import statements of 'schedule'
echo "Searching for 'schedule' import statements:"
rg --type python "^import schedule|^from schedule import"

# Test: Search for usage of 'schedule' functions
echo -e "\nSearching for 'schedule' function usage:"
rg --type python "schedule\.(every|run_pending|run_all|clear)"

Length of output: 392

---

Script:

#!/bin/bash
# Description: Verify the usage of the 'schedule' package in Python files within the codebase

# Test: Search for import statements of 'schedule'
echo "Searching for 'schedule' import statements in Python files:"
rg --glob "*.py" "^import schedule|^from schedule import"

# Test: Search for usage of 'schedule' functions
echo -e "\nSearching for 'schedule' function usage in Python files:"
rg --glob "*.py" "schedule\.(every|run_pending|run_all|clear)"

Length of output: 652

backend/app/repositories/process_repository.py (1)

95-105: 🛠️ Refactor suggestion

Consider enhancing the new function for clarity and flexibility.

The new get_process_steps_with_asset_content function provides more specific querying capabilities compared to the existing get_process_steps function. However, there are a few suggestions for improvement:

1. The function name could be more descriptive, e.g., get_process_steps_by_status_with_asset_content.
2. Consider adding a default value for the status parameter to allow querying all statuses if needed.
3. Add a docstring to explain the function's purpose, parameters, and return value.

Here's a suggested implementation with these improvements:

def get_process_steps_by_status_with_asset_content(
    db: Session,
    process_id: int,
    status: List[ProcessStatus] = None
) -> List[models.ProcessStep]:
    """
    Retrieve process steps for a given process, filtered by status, including asset content.

    Args:
        db (Session): The database session.
        process_id (int): The ID of the process to query.
        status (List[ProcessStatus], optional): List of statuses to filter by. If None, all statuses are included.

    Returns:
        List[models.ProcessStep]: A list of ProcessStep objects with related entities loaded.
    """
    query = db.query(models.ProcessStep).filter(models.ProcessStep.process_id == process_id)
    
    if status:
        query = query.filter(models.ProcessStep.status.in_(status))
    
    return query.options(
        joinedload(models.ProcessStep.process).joinedload(models.Process.project),
        joinedload(models.ProcessStep.asset).joinedload(models.Asset.content),
    ).all()

Could you please clarify the specific use case for this new function? Understanding its purpose will help ensure it meets the requirements effectively.

✅ Verification successful

Let's try searching for the function usage by targeting Python files directly.

---

Verification Successful: Function Usage Confirmed

The get_process_steps_with_asset_content function is actively used in backend/app/processing/process_queue.py, ensuring that its introduction serves a functional purpose within the codebase.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Find usages of the new function to understand its context

# Test: Search for function calls
rg --type python -A 5 'get_process_steps_with_asset_content'

Length of output: 91

---

Script:

#!/bin/bash
# Description: Find usages of the new function to understand its context

# Search for function calls in Python files
rg 'get_process_steps_with_asset_content' --glob "*.py" -A 5

Length of output: 1179

backend/tests/processing/test_process_scheduler.py (3)

1-5: LGTM: Imports and class definition are appropriate.

The import statements and class definition are well-structured for unit testing with mocking. The TestProcessScheduler class correctly inherits from unittest.TestCase.

---

6-9: LGTM: setUp method is well-implemented.

The setUp method correctly initializes mock objects and creates a ProcessScheduler instance, following best practices for unit testing. This setup allows for isolated testing of the ProcessScheduler class.

---

140-141: LGTM: Standard main block for running tests.

The inclusion of the if __name__ == '__main__': block is a good practice, allowing the tests to be run directly or the file to be imported as a module.

backend/app/processing/process_queue.py (3)

6-7: Imports are appropriate for the new functionality.

The added imports of AssetProcessingStatus and ProcessScheduler are necessary for the process management enhancements.

---

34-34: Adding return type annotation improves clarity.

Specifying -> None in the submit_process function enhances code readability and supports static type checking.

---

38-38: ProcessScheduler initialization is correctly implemented.

The process_execution_scheduler is properly instantiated with the specified interval and callback function.

backend/app/processing/process_scheduler.py (1)

21-21: Ensure scheduled jobs are properly configured in a multi-threaded environment

The schedule module is not inherently thread-safe when modifying jobs. Since you're scheduling tasks and running them in separate threads, there might be concurrency issues. Consider using thread-safe scheduling alternatives or ensuring that scheduling and job execution occur within the same thread.

To verify if there are potential issues, you can check for thread safety concerns in the schedule module usage.

Files that changed from the base of the PR and between e08fc37 and 13f0b93.

• backend/Makefile (1 hunks)
• backend/app/processing/process_queue.py (7 hunks)
• backend/app/processing/process_scheduler.py (1 hunks)

• backend/Makefile

backend/app/processing/process_scheduler.py (8)

1-5: LGTM: Import statements are well-organized and relevant.

The import statements are correctly organized, with standard library imports first, followed by local imports. All imported modules and classes are relevant to the functionality of the ProcessScheduler class.

---

7-14: LGTM: Well-defined class with informative docstring.

The ProcessScheduler class is well-named and follows proper naming conventions. The docstring provides a clear and comprehensive explanation of the class's purpose and behavior, which is excellent for maintainability and usability.

---

15-26: LGTM: Well-implemented initialization method.

The __init__ method is well-structured and initializes all necessary components:

• It correctly sets up instance variables.
• Creates a lock for thread safety.
• Handles the optional logger parameter gracefully.
• Sets up the scheduler with the provided interval.

This implementation provides a good foundation for the class's functionality.

---

28-36: LGTM: Well-implemented internal processing method.

The _reprocess_holding_processes method is correctly implemented:

• It uses a lock to ensure thread safety when accessing shared resources.
• The logic for processing tasks and stopping the scheduler when the queue is empty is sound.
• Appropriate logging is used to track execution.

This implementation helps maintain the integrity of the process queue and conserves resources when no tasks are pending.

---

38-42: LGTM: Efficient implementation of the scheduler loop.

The _run_scheduler method is well-implemented:

• It correctly uses a while loop controlled by the scheduler_running flag, allowing for controlled stopping of the scheduler.
• The method runs pending scheduled tasks efficiently.
• The 1-second sleep between iterations prevents excessive CPU usage.

This implementation provides a good balance between responsiveness and resource usage.

---

44-59: LGTM: Well-implemented start and stop methods.

The start_scheduler and stop_scheduler methods are correctly implemented:

• start_scheduler checks if the scheduler is already running before starting a new thread, preventing duplicate threads.
• The scheduler thread is set as a daemon thread, which is good for automatic cleanup when the main program exits.
• stop_scheduler uses a simple and effective method to stop the scheduler by setting the running flag to False.
• Both methods use appropriate logging to track the scheduler's state changes.

These methods provide good control over the scheduler's lifecycle.

---

60-65: LGTM: Well-implemented method for adding processes to the queue.

The add_process_to_queue method is correctly implemented:

• It uses a lock to ensure thread safety when modifying the shared queue.
• The method logs the addition of the process, which is good for tracking and debugging.
• It calls start_scheduler after adding the process, ensuring that the scheduler is running to process the newly added task.

This implementation provides a thread-safe and efficient way to add new processes to the queue.

---

1-65: Excellent implementation of the ProcessScheduler class.

The ProcessScheduler class is a well-designed and robust solution for managing a queue of processes to be executed. Key strengths of this implementation include:

1. Thread-safety: Proper use of locks to prevent race conditions.
2. Resource conservation: The scheduler automatically stops when the queue is empty.
3. Clear control methods: Well-implemented start, stop, and add process methods.
4. Good documentation: Comprehensive docstrings and appropriate logging.
5. Efficient design: Balances responsiveness and CPU usage in the scheduler loop.

This implementation provides a solid foundation for managing process execution in a multi-threaded environment. Great job!

backend/app/processing/process_queue.py (1)

34-35: LGTM! Improved type hinting.

The addition of the return type annotation -> None to the submit_process function signature enhances code clarity and supports static type checking. This is a good practice that aligns with Python type hinting conventions.