Monitor Digital Twin Pipeline Traces with Arize Phoenix and ZenML

"""
Production implementation for monitoring digital twin pipeline traces.
Provides secure, scalable operations with Arize Phoenix and ZenML.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Logging setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    arize_api_key: str = os.getenv('ARIZE_API_KEY')

# SQLAlchemy engine and session setup for connection pooling
engine = create_engine(Config.database_url, pool_size=20, max_overflow=0)
Session = sessionmaker(bind=engine)

# Validate input data for processing
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for the digital twin pipeline.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'trace_id' not in data:
        raise ValueError('Missing trace_id')
    if not isinstance(data['trace_id'], str):
        raise ValueError('trace_id must be a string')
    return True

# Sanitize fields to prevent injection attacks
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to ensure safe processing.
    
    Args:
        data: Input data dictionary
    Returns:
        Sanitized data dictionary
    """
    return {k: str(v).strip() for k, v in data.items()}  # Sanitize fields

# Transform records for further processing
def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform the input records into a desired format.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    return [{**record, 'normalized_value': record['value'] / 100} for record in records]  # Example transformation

# Fetch data from ZenML
async def fetch_data(trace_id: str) -> List[Dict[str, Any]]:
    """Fetch data from ZenML using trace_id.
    
    Args:
        trace_id: ID of the trace to fetch
    Returns:
        List of records
    """
    try:
        response = requests.get(f'https://zenml.io/api/v1/traces/{trace_id}')
        response.raise_for_status()
        return response.json()['records']
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise

# Save processed data to the database
async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save the transformed records into the database.
    
    Args:
        records: List of records to save
    Raises:
        Exception: If database operations fail
    """
    with Session() as session:
        for record in records:
            session.execute(text('INSERT INTO processed_traces (trace_id, value) VALUES (:trace_id, :value)'),
                             {'trace_id': record['trace_id'], 'value': record['normalized_value']})
        session.commit()

# Aggregate metrics for analysis
def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from processed records.
    
    Args:
        records: List of records to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    total_value = sum(record['normalized_value'] for record in records)
    avg_value = total_value / len(records) if records else 0
    return {'total_value': total_value, 'average_value': avg_value}

# Format output for logging
def format_output(data: Dict[str, Any]) -> str:
    """Format output data for logging.
    
    Args:
        data: Data to format
    Returns:
        Formatted string
    """
    return f"Metrics - Total: {data['total_value']}, Average: {data['average_value']}"

# Main orchestrator class
class DigitalTwinMonitor:
    def __init__(self, trace_id: str):
        self.trace_id = trace_id

    async def run(self) -> None:
        """Run the monitoring process for the digital twin.
        
        Raises:
            Exception: If any step fails
        """
        try:
            # Step 1: Fetch data
            raw_data = await fetch_data(self.trace_id)  # Fetching raw data
            # Step 2: Validate data
            await validate_input(raw_data)  # Validating fetched data
            # Step 3: Sanitize data
            sanitized_data = sanitize_fields(raw_data)  # Sanitizing fetched data
            # Step 4: Transform data
            transformed_data = transform_records(sanitized_data)  # Transforming data
            # Step 5: Save to DB
            await save_to_db(transformed_data)  # Saving to database
            # Step 6: Aggregate metrics
            metrics = aggregate_metrics(transformed_data)  # Aggregating metrics
            logger.info(format_output(metrics))  # Logging output
        except Exception as e:
            logger.error(f'Error in monitoring process: {e}')
            raise

if __name__ == '__main__':
    # Example usage
    monitor = DigitalTwinMonitor(trace_id='12345')
    monitor.run()  # Run the monitoring process