Instrument Digital Twin ML Pipelines with Distributed Traces Using MLflow and OpenTelemetry

"""
Production implementation for Instrument Digital Twin ML Pipelines.
Provides secure, scalable operations using MLflow and OpenTelemetry.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import time
import requests
from pydantic import BaseModel, ValidationError
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker, Session
from sqlalchemy.exc import SQLAlchemyError

# Setting up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Config class to manage environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')

# Helper function to validate input data
async def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate the input data for processing.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data:
        raise ValueError('Missing id')
    if 'model_data' not in data:
        raise ValueError('Missing model_data')
    return True

# Helper function to sanitize input fields
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}

# Helper function to fetch data from a source
async def fetch_data(url: str) -> Optional[Dict[str, Any]]:
    """Fetch data from the given URL.
    
    Args:
        url: URL to fetch data from
    Returns:
        Response data in JSON format
    Raises:
        Exception: If the request fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f"Error fetching data from {url}: {e}")
        raise

# Helper function to save data to the database
async def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: Data to be saved
    Raises:
        DatabaseError: If saving fails
    """
    engine = create_engine(Config.database_url)
    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
    session: Session = SessionLocal()
    try:
        session.execute(text("INSERT INTO model_data (id, data) VALUES (:id, :data)"), data)
        session.commit()  # Commit the transaction
    except SQLAlchemyError as e:
        session.rollback()  # Rollback in case of error
        logger.error(f"Error saving to database: {e}")
        raise
    finally:
        session.close()  # Ensure session is closed

# Helper function to transform records
def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        records: List of records to transform
    Returns:
        List of transformed records
    """
    # Example transformation - can be customized
    return [{"id": record['id'], "processed_data": record['model_data']} for record in records]

# Helper function to aggregate metrics
def aggregate_metrics(metrics: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed data.
    
    Args:
        metrics: List of metrics to aggregate
    Returns:
        Aggregated metrics
    """
    # Example aggregation
    return {"count": len(metrics), "average": sum(m['value'] for m in metrics) / len(metrics)}

# Main class orchestrating the workflow
class DigitalTwinMLPipeline:
    def __init__(self) -> None:
        self.config = Config()  # Load configuration

    async def run(self, input_data: Dict[str, Any]) -> None:
        """Run the ML pipeline with the provided input data.
        
        Args:
            input_data: Data to process
        """
        try:
            # Validate and sanitize input data
            await validate_input_data(input_data)
            sanitized_data = sanitize_fields(input_data)
            # Fetch additional data if necessary
            additional_data = await fetch_data(sanitized_data['data_source'])
            # Combine and transform records
            transformed_data = transform_records([sanitized_data, additional_data])
            # Save transformed data to the database
            await save_to_db(transformed_data)
            logger.info("Data processed and saved successfully.")
        except Exception as e:
            logger.error(f"Pipeline run failed: {e}")

# Example usage
if __name__ == '__main__':
    # Example input data
    input_data = {"id": "123", "model_data": "some_data", "data_source": "http://example.com/data"}
    pipeline = DigitalTwinMLPipeline()  # Instantiate the pipeline class
    import asyncio
    asyncio.run(pipeline.run(input_data))  # Run the pipeline