Version and Reproduce Digital Twin Experiments with DVC and ZenML

"""
Production implementation for versioning and reproducing digital twin experiments using DVC and ZenML.
This module provides secure, scalable operations for managing data science workflows.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import json
import requests
from zenml.pipelines import pipeline
from zenml.steps import step
from dvc.api import DVCFileSystem

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    dvc_repo_url: str = os.getenv('DVC_REPO_URL')
    zenml_repo_url: str = os.getenv('ZENML_REPO_URL')
    database_url: str = os.getenv('DATABASE_URL')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for experiments.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'experiment_id' not in data:
        raise ValueError('Missing experiment_id')  # Must have experiment ID
    if not isinstance(data['parameters'], dict):
        raise ValueError('Parameters must be a dictionary')  # Parameters should be a dict
    return True

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

@step
def fetch_data(experiment_id: str) -> List[Dict[str, Any]]:
    """Fetch experiment data from DVC.
    
    Args:
        experiment_id: ID of the experiment to fetch
    Returns:
        List[Dict[str, Any]]: List of experiment data
    """
    logger.info(f"Fetching data for experiment_id: {experiment_id}")
    fs = DVCFileSystem(repo=Config.dvc_repo_url)
    try:
        data = fs.get(f'data/{experiment_id}.json')  # Fetching from DVC
        logger.info("Data fetched successfully")
        return json.loads(data)
    except Exception as e:
        logger.error(f"Error fetching data: {e}")
        raise RuntimeError("Failed to fetch data")

@step
def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        RuntimeError: If saving fails
    """
    logger.info("Saving data to the database")
    # Here you would implement your DB saving logic, e.g., using SQLAlchemy
    try:
        # db.session.bulk_insert_mappings(Model, data)  # Example of bulk insert
        logger.info("Data saved successfully")
    except Exception as e:
        logger.error(f"Error saving to database: {e}")
        raise RuntimeError("Failed to save data")

@step
def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw data into a suitable format for analysis.
    
    Args:
        data: Raw data to transform
    Returns:
        List[Dict[str, Any]]: Transformed data
    """
    logger.info("Transforming records")
    transformed_data = []
    for record in data:
        transformed_record = {
            'experiment_id': record['id'],
            'metrics': record.get('metrics', {}),
            'timestamp': time.time(),  # Add a timestamp
        }
        transformed_data.append(transformed_record)
    logger.info("Records transformed successfully")
    return transformed_data

def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from transformed data.
    
    Args:
        data: Transformed data
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    logger.info("Aggregating metrics")
    metrics = {}
    for record in data:
        for key, value in record['metrics'].items():
            metrics[key] = metrics.get(key, 0) + value  # Simple aggregation
    logger.info("Metrics aggregated successfully")
    return metrics

class ExperimentPipeline:
    """Main orchestrator for running the experiment pipeline."""

    @staticmethod
    @pipeline
    def run_pipeline(experiment_id: str):
        """Run the entire experiment pipeline.
        
        Args:
            experiment_id: ID of the experiment to run
        """
        data = fetch_data(experiment_id)
        transformed_data = transform_records(data)
        metrics = aggregate_metrics(transformed_data)
        save_to_db(metrics)

if __name__ == '__main__':
    # Example usage
    example_data = {'experiment_id': 'exp_123', 'parameters': {'param1': 'value1'}}
    try:
        if validate_input(example_data):
            sanitized_data = sanitize_fields(example_data)
            ExperimentPipeline.run_pipeline(sanitized_data['experiment_id'])
            logger.info("Pipeline executed successfully")
    except Exception as e:
        logger.error(f"Error in pipeline execution: {e}")
        raise