Route Factory OPC-UA Events to Delta Lake with Apache Kafka and Polars

"""
Production implementation for routing OPC-UA events to Delta Lake using Apache Kafka and Polars.
Provides secure, scalable operations with efficient data processing.
"""
from typing import Dict, Any, List, Union
import os
import logging
import time
import asyncio
import polars as pl
from kafka import KafkaProducer, KafkaConsumer

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

class Config:
    """
    Configuration class for environment variables.
    """
    kafka_broker: str = os.getenv('KAFKA_BROKER', 'localhost:9092')
    delta_table_path: str = os.getenv('DELTA_TABLE_PATH', '/delta/events')

async def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate OPC-UA event data.
    Args:
        data: Input data 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 or timestamp')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize fields in the input data.
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def transform_records(records: List[Dict[str, Any]]) -> pl.DataFrame:
    """
    Transform records into a Polars DataFrame.
    Args:
        records: List of records to transform
    Returns:
        Polars DataFrame
    """
    return pl.DataFrame(records)

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """
    Process a batch of event data.
    Args:
        batch: List of event data records
    """
    try:
        for record in batch:
            await validate_input(record)  # Validate each record
            sanitized = await sanitize_fields(record)  # Sanitize fields
            # Transform and save to Delta Lake
            df = await transform_records([sanitized])  # Transform to DataFrame
            logger.info(f'Transforming record: {sanitized}')  # Log transformation
            await save_to_db(df)  # Save to Delta Lake
    except Exception as e:
        logger.error(f'Error processing batch: {e}')  # Log error

async def save_to_db(df: pl.DataFrame) -> None:
    """
    Save transformed DataFrame to Delta Lake.
    Args:
        df: Polars DataFrame to save
    """
    # Placeholder for saving to Delta Lake
    logger.info(f'Saving DataFrame to Delta Lake: {df}')  # Log saving

async def fetch_data(consumer: KafkaConsumer) -> None:
    """
    Fetch data from Kafka topic.
    Args:
        consumer: KafkaConsumer instance
    """
    try:
        for message in consumer:
            logger.info(f'Received message: {message.value}')  # Log received message
            await process_batch([message.value])  # Process each message
    except Exception as e:
        logger.error(f'Error fetching data: {e}')  # Log error

async def create_kafka_consumer() -> KafkaConsumer:
    """
    Create a Kafka consumer.
    Returns:
        KafkaConsumer instance
    """
    return KafkaConsumer('opc_events', bootstrap_servers=Config.kafka_broker)

async def main() -> None:
    """
    Main entry point for the application.
    """
    consumer = await create_kafka_consumer()  # Create Kafka consumer
    await fetch_data(consumer)  # Start fetching data

if __name__ == '__main__':
    # Run the main function in an asyncio event loop
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        logger.info('Shutting down...')  # Log shutdown
    except Exception as e:
        logger.error(f'Unexpected error: {e}')  # Log unexpected error