Enrich Industrial Sensor Streams with PyFlink and Hugging Face Transformers

                      
                     
"""
Production implementation for enriching industrial sensor streams with PyFlink and Hugging Face Transformers.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import time
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit
import requests

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

class Config:
    """Configuration class for environment variables."""
    def __init__(self):
        self.sensors_url: str = os.getenv('SENSORS_URL')
        self.transformer_api: str = os.getenv('TRANSFORMER_API')
        self.retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))

config = Config()

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate sensor data input.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'sensor_id' not in data:
        raise ValueError('Missing sensor_id')
    if 'value' not in data:
        raise ValueError('Missing value')
    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()}

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from sensors endpoint.
    
    Returns:
        List[Dict[str, Any]]: Sensor data
    Raises:
        Exception: If fetching data fails
    """
    try:
        logger.info('Fetching data from sensors...')
        response = requests.get(config.sensors_url)
        response.raise_for_status()  # Raise error for bad responses
        sensors_data = response.json()
        return sensors_data
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise Exception('Failed to fetch data')

def transform_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform data using Hugging Face Transformers API.
    
    Args:
        data: Data to transform
    Returns:
        Dict[str, Any]: Transformed data
    Raises:
        Exception: If transformation fails
    """
    try:
        logger.info('Transforming data with Hugging Face...')
        response = requests.post(config.transformer_api, json=data)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error transforming data: {e}')
        raise Exception('Failed to transform data')

def save_to_db(data: Dict[str, Any]) -> None:
    """Save transformed data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving data fails
    """
    # Simulated save operation
    logger.info('Saving data to the database...')
    # Here you would add actual database logic

def process_batch(sensors_data: List[Dict[str, Any]]) -> None:
    """Process a batch of sensor data.
    
    Args:
        sensors_data: List of sensor data
    """
    for record in sensors_data:
        try:
            validate_input(record)  # Validate each record
            sanitized_data = sanitize_fields(record)  # Sanitize data
            transformed_data = transform_data(sanitized_data)  # Transform data
            save_to_db(transformed_data)  # Save to DB
        except Exception as e:
            logger.error(f'Error processing record {record}: {e}')  # Log errors

class SensorStreamProcessor:
    """Main class for processing sensor streams."""
    def __init__(self):
        self.spark = SparkSession.builder.appName('SensorStreamEnrichment').getOrCreate()

    def run(self) -> None:
        """Run the sensor stream enrichment process."""
        while True:
            try:
                sensors_data = fetch_data()  # Fetch sensor data
                process_batch(sensors_data)  # Process fetched data
                time.sleep(5)  # Delay for rate limiting
            except Exception as e:
                logger.error(f'Error in processing loop: {e}')  # Handle loop errors

if __name__ == '__main__':
    processor = SensorStreamProcessor()
    processor.run()  # Start processing