Build AWS-Connected Equipment Digital Twins with AWS IoT TwinMaker SDK and MLflow

"""
Production implementation for building AWS-connected equipment digital twins.
Provides secure, scalable operations using AWS IoT TwinMaker SDK and MLflow.
"""
from typing import Dict, Any, List
import os
import logging
import boto3
from botocore.exceptions import ClientError
import json
import time

# Initialize logging for tracking events and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to hold environment variables.
    Contains AWS and MLflow configurations for the application.
    """
    twinmaker_client: Any = boto3.client('twinmaker')  # AWS TwinMaker client initialization
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')  # Get MLflow Tracking URI from environment

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate incoming data for digital twin creation.
    
    Args:
        data (Dict[str, Any]): Input data to validate
    Returns:
        bool: True if valid, False otherwise
    Raises:
        ValueError: If validation fails
    """
    if 'name' not in data:
        raise ValueError('Missing required field: name')  # Ensure name is provided
    if 'type' not in data:
        raise ValueError('Missing required field: type')  # Ensure type is provided
    return True  # Validation passed

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

def fetch_data(twin_id: str) -> Dict[str, Any]:
    """
    Fetch digital twin data from AWS TwinMaker.
    
    Args:
        twin_id (str): The ID of the digital twin
    Returns:
        Dict[str, Any]: Digital twin data
    Raises:
        ClientError: If the AWS call fails
    """
    try:
        response = Config.twinmaker_client.get_digital_twin(id=twin_id)  # Fetching digital twin data
        return response['digitalTwin']
    except ClientError as e:
        logger.error(f'Error fetching data for twin {twin_id}: {e}')  # Log error message
        raise

def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save digital twin data to MLflow for tracking.
    
    Args:
        data (Dict[str, Any]): Data to save
    """
    # MLflow logging
    import mlflow
    mlflow.set_tracking_uri(Config.mlflow_tracking_uri)  # Set MLflow tracking URI
    with mlflow.start_run():  # Start a new MLflow run
        mlflow.log_params(data)  # Log parameters
        logger.info(f'Saved data to MLflow for {data.get("name")}')  # Log successful saving

def process_batch(twin_data: List[Dict[str, Any]]) -> None:
    """
    Process a batch of digital twins.
    
    Args:
        twin_data (List[Dict[str, Any]]): List of digital twin data
    """
    for data in twin_data:
        try:
            validate_input(data)  # Validate each twin's data
            sanitized_data = sanitize_fields(data)  # Sanitize input data
            save_to_db(sanitized_data)  # Save sanitized data to MLflow
        except Exception as e:
            logger.error(f'Failed processing twin {data.get("name", "unknown")} due to {e}')  # Log errors

def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """
    Aggregate metrics from digital twin data.
    
    Args:
        data (List[Dict[str, Any]]): List of digital twin data
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    metrics = {"count": len(data)}  # Count of digital twins
    # Further metrics can be computed here
    return metrics

if __name__ == '__main__':
    # Example usage of the implemented functions
    sample_data = [
        {"name": "Twin1", "type": "sensor", "location": "Warehouse"},
        {"name": "Twin2", "type": "actuator", "location": "Factory"}
    ]
    process_batch(sample_data)  # Process a sample batch of digital twins
    logger.info('Batch processing completed successfully.')  # Indicate completion