Build Real-Time Feature Pipelines for Industrial Digital Twin Models with Hopsworks and ZenML

"""
Production implementation for building real-time feature pipelines for Industrial Digital Twin models.
Provides secure, scalable operations using Hopsworks and ZenML.
"""

from typing import Dict, Any, List, Union
import os
import logging
import time
import requests
from pydantic import BaseModel, HttpUrl
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

# Configuration class
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///example.db')
    api_endpoint: HttpUrl = os.getenv('API_ENDPOINT', 'http://api.example.com')

# Create a database engine with connection pooling
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
SessionLocal = sessionmaker(bind=engine)

# Data model for input validation
class InputData(BaseModel):
    id: str
    features: Dict[str, Union[int, float]]

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    logger.info('Validating input data.')  # Log validation step
    if 'id' not in data:
        raise ValueError('Missing id')  # Raise error if 'id' is missing
    if 'features' not in data:
        raise ValueError('Missing features')  # Raise error if 'features' is missing
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    logger.info('Sanitizing input fields.')  # Log sanitization
    return {k: v for k, v in data.items() if v is not None}  # Remove None values

async def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch data from an API endpoint.
    
    Args:
        api_url: The API URL to fetch data from
    Returns:
        Fetched data as a dictionary
    Raises:
        Exception: If an error occurs during API call
    """
    logger.info(f'Fetching data from {api_url}.')  # Log API call
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()  # Return the JSON response
    except requests.exceptions.RequestException as e:
        logger.error(f'API call failed: {e}')  # Log error
        raise Exception('Failed to fetch data from API')  # Raise custom error

async def save_to_db(session: Any, record: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        session: Database session
        record: Data record to save
    """
    logger.info('Saving record to the database.')  # Log saving step
    session.execute(text("INSERT INTO records (id, features) VALUES (:id, :features)"), record)  # Insert record
    session.commit()  # Commit the transaction

async def transform_records(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Transform raw records into a desired format.
    
    Args:
        data: Raw data to transform
    Returns:
        Transformed data as a list of dictionaries
    """
    logger.info('Transforming records.')  # Log transformation
    transformed = []  # Initialize list for transformed records
    for item in data['features']:
        transformed.append({'id': data['id'], 'feature': item})  # Append transformed item
    return transformed  # Return transformed records

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of data records.
    
    Args:
        data: List of data records to process
    """
    logger.info(f'Processing batch of {len(data)} records.')  # Log batch processing
    with SessionLocal() as session:  # Use a context manager for the session
        for record in data:
            await save_to_db(session, record)  # Save each record to the database

async def aggregate_metrics(session: Any) -> None:
    """Aggregate metrics from the database.
    
    Args:
        session: Database session
    """
    logger.info('Aggregating metrics from the database.')  # Log aggregation
    result = session.execute(text("SELECT AVG(feature) FROM records"))  # Aggregate metrics
    logger.info(f'Aggregated metrics: {result.fetchall()}')  # Log results

# Main orchestrator class
class PipelineOrchestrator:
    """Orchestrator for real-time feature pipelines.
    
    Methods:
        run: Execute the pipeline
    """

    async def run(self, input_data: Dict[str, Any]) -> None:
        """Run the pipeline.
        
        Args:
            input_data: Input data for the pipeline
        """
        logger.info('Starting the pipeline.')  # Log pipeline start
        try:
            await validate_input(input_data)  # Validate input data
            sanitized_data = await sanitize_fields(input_data)  # Sanitize data
            api_data = await fetch_data(Config.api_endpoint)  # Fetch data from API
            transformed_data = await transform_records(api_data)  # Transform fetched data
            await process_batch(transformed_data)  # Process transformed data
            with SessionLocal() as session:
                await aggregate_metrics(session)  # Aggregate metrics
        except Exception as e:
            logger.error(f'Error in pipeline execution: {e}')  # Log any errors

if __name__ == '__main__':
    # Example usage
    input_example = {'id': '123', 'features': {'feature1': 1.0, 'feature2': 2.0}}
    orchestrator = PipelineOrchestrator()
    import asyncio
    asyncio.run(orchestrator.run(input_example))  # Run the pipeline asynchronously