Build Real-Time Factory Event Windows with Stateful DataFrames Using Pathway and DuckDB

"""
Production implementation for building real-time factory event windows using Pathway and DuckDB.
Provides secure, scalable operations to manage factory data in stateful DataFrames.
"""

from typing import Dict, Any, List
import os
import logging
import duckdb
import pandas as pd
import time

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

# Configuration class to hold environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'duckdb://:memory:')

# Validate input data to ensure it meets required schema
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
    """
    if 'event_id' not in data or 'timestamp' not in data:
        raise ValueError('Missing required fields: event_id, timestamp')
    return True

# Sanitize data fields to avoid SQL injection
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).replace("'", "\'") for k, v in data.items()}

# Normalizes incoming data to a standard format
def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize incoming data.
    
    Args:
        data: Data to normalize
    Returns:
        Normalized data
    """
    data['timestamp'] = pd.to_datetime(data['timestamp'])  # Convert to datetime
    return data

# Process a batch of events and insert into DuckDB
async def process_batch(events: List[Dict[str, Any]]) -> None:
    """Process a batch of events.
    
    Args:
        events: List of event records to process
    """
    conn = duckdb.connect(Config.database_url)
    for event in events:
        try:
            validated_event = await validate_input(event)  # Validate each event
            sanitized_event = sanitize_fields(validated_event)  # Sanitize fields
            normalized_event = normalize_data(sanitized_event)  # Normalize data
            insert_query = "INSERT INTO factory_events VALUES (?, ?)"
            conn.execute(insert_query, (normalized_event['event_id'], normalized_event['timestamp']))
            logger.info(f"Inserted event: {normalized_event['event_id']}")
        except Exception as e:
            logger.error(f"Error processing event {event}: {str(e)}")
    conn.close()  # Close the connection

# Aggregate metrics from processed events
def aggregate_metrics() -> pd.DataFrame:
    """Aggregate metrics from the factory events.
    
    Returns:
        DataFrame with aggregated metrics
    """
    conn = duckdb.connect(Config.database_url)
    result = conn.execute("SELECT event_id, COUNT(*) AS count FROM factory_events GROUP BY event_id").fetchdf()
    conn.close()  # Close the connection
    return result

# Fetch data from an external API - placeholder function
async def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        api_url: URL of the API
    Returns:
        Data fetched from the API
    """
    # Simulating API call
    time.sleep(1)  # Simulate network delay
    return {'event_id': '123', 'timestamp': '2023-10-01T12:00:00Z'}

# Save processed metrics to a database
async def save_to_db(data: pd.DataFrame) -> None:
    """Save aggregated metrics to the database.
    
    Args:
        data: DataFrame with metrics to save
    """
    conn = duckdb.connect(Config.database_url)
    conn.execute("CREATE TABLE IF NOT EXISTS metrics (event_id VARCHAR, count INTEGER)")
    for index, row in data.iterrows():
        conn.execute("INSERT INTO metrics VALUES (?, ?)", (row['event_id'], row['count']))
    conn.close()  # Close the connection

# Format output for logging and display
def format_output(data: Any) -> str:
    """Format data for output.
    
    Args:
        data: Data to format
    Returns:
        Formatted string
    """
    return str(data)

# Handling errors gracefully
def handle_errors(exc: Exception) -> None:
    """Handle errors during processing.
    
    Args:
        exc: Exception to handle
    """
    logger.error(f"An error occurred: {str(exc)}")

# Main orchestrator for the application
class RealTimeEventProcessor:
    def __init__(self):
        logger.info("Initializing RealTimeEventProcessor")

    async def run(self):
        try:
            while True:
                # Simulating fetching data from an API
                event_data = await fetch_data('http://api.example.com/events')
                await process_batch([event_data])
                metrics = aggregate_metrics()  # Aggregate metrics
                await save_to_db(metrics)  # Save metrics
                time.sleep(5)  # Wait before next polling
        except Exception as e:
            handle_errors(e)  # Handle any unexpected errors

if __name__ == '__main__':
    processor = RealTimeEventProcessor()  # Instantiate the processor
    import asyncio
    asyncio.run(processor.run())  # Run the event processor