Best Practices

Performance Considerations

• Optimize database queries to minimize load
• Implement pagination for large data sets
• Use async/await for I/O-bound operations
• Monitor and optimize database indexes
• Implement connection pooling for database connections
• Consider partitioning strategies for large tables
• Optimize geospatial queries using appropriate PostGIS functions
• Use TimescaleDB hypertables for efficient time-series data storage and querying

Database Optimization

• Use appropriate indexes for frequently queried fields
• Optimize SQL queries for performance
• Use database connection pooling
• Implement caching for frequently accessed data
• Monitor query performance and set up alerts for slow queries
• Use EXPLAIN ANALYZE to identify performance bottlenecks

API Optimization

• Use async/await for I/O-bound operations
• Implement pagination for endpoints that return large datasets
• Use appropriate HTTP status codes and headers
• Implement rate limiting for public APIs
• Use connection pooling for external API calls

Resource Management

• Close database connections and file handles properly
• Use context managers for resource cleanup
• Monitor memory usage and optimize memory-intensive operations
• Implement proper connection pooling for database access

Security Guidelines

• Never store sensitive information in code repositories
• Use environment variables for configuration
• Implement proper authentication and authorization
• Validate all user inputs using SQLModel
• Protect against common web vulnerabilities (XSS, CSRF, SQL injection)
• Use HTTPS for all communications
• Implement rate limiting to prevent abuse
• Regularly update dependencies to address security vulnerabilities
• Apply principle of least privilege for database access
• Implement data encryption for sensitive information

Input Validation

• Validate all user inputs using Pydantic models (within SQLModel).
• Implement strict type checking for API parameters.
• Use parameterized queries to prevent SQL injection.

Authentication and Authorization

• Implement API key authentication as specified in requirement #21.
• Validate API keys for all protected endpoints.
• Implement role-based access control for different user types.

Data Protection

• Encrypt sensitive data in transit and at rest.
• Implement proper error handling to avoid leaking sensitive information.
• Follow the principle of least privilege for database access.

Error Handling and Logging Standards

Error Hierarchy:

• Define a custom exception hierarchy for different error types
• Use specific exception types for different error scenarios

Custom Exception Examples

# Base exception for all application-specific errors
class InfDBError(Exception):
    """Base exception for all InfDB-specific errors."""
    def __init__(self, message, details=None):
        super().__init__(message)
        self.details = details

# Domain-specific exceptions
class DatabaseConnectionError(InfDBError):
    """Raised when a database connection cannot be established."""
    pass

class GeospatialError(InfDBError):
    """Base exception for geospatial-related errors."""
    pass

class InvalidCoordinateError(GeospatialError):
    """Raised when invalid coordinates are provided."""
    pass

# Usage example
try:
    # Some operation that might fail
    result = repository.get_raster_center(building_id, resolution)
    if not result:
        raise InvalidCoordinateError(
            f"No raster found for building {building_id} at resolution {resolution}",
            details={"building_id": building_id, "resolution": resolution}
        )
except InvalidCoordinateError as e:
    # Handle the specific error
    logger.error(f"Coordinate error: {str(e)}", extra={"details": e.details})
    raise HTTPException(status_code=404, detail=str(e))
except DatabaseConnectionError as e:
    # Handle database connection errors
    logger.critical(f"Database connection failed: {str(e)}")
    raise HTTPException(status_code=503, detail="Service temporarily unavailable")
except InfDBError as e:
    # Handle any other application-specific errors
    logger.error(f"Application error: {str(e)}")
    raise HTTPException(status_code=500, detail=str(e))
except Exception as e:
    # Handle unexpected errors
    logger.exception(f"Unexpected error: {str(e)}")
    raise HTTPException(status_code=500, detail="An unexpected error occurred")

Error Messages

• Provide clear, actionable error messages.
• Include relevant context in error messages.
• Use structured error responses for API endpoints:

  {
      "error": "Invalid weather parameter",
      "details": "Parameter 'temperature' is not available for the specified date range."
  }
  

Logging

• Use the Python logging module for all logging
• Configure appropriate log levels
• Include relevant context in log messages (user ID, request ID, etc.)
• Structure logs for easy parsing and analysis
• Avoid logging sensitive information

Logging Levels:

• DEBUG: Detailed information for debugging
• INFO: Confirmation that things are working as expected
• WARNING: Indication that something unexpected happened
• ERROR: Due to a more serious problem, the software couldn't perform some function
• CRITICAL: A serious error indicating the program may be unable to continue running

Log Format:

{timestamp} [{level}] {module}: {message} {context}