Detect Anomalies in Streaming IIoT Sensor Data with River and Polars

"""
Production implementation for detecting anomalies in streaming IIoT sensor data using River and Polars.
Provides secure, scalable operations for real-time analysis.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import polars as pl
from river import anomaly, stream
from river import compose
from river import metrics
from river import preprocessing

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

class Config:
    """Configuration class to manage environment variables."""
    DATABASE_URL: str = os.getenv('DATABASE_URL')
    RETRY_LIMIT: int = int(os.getenv('RETRY_LIMIT', 3))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data structure.
    
    Args:
        data: Input data to validate.
    Returns:
        bool: True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails.
    """
    if 'sensor_id' not in data or 'value' not in data:
        raise ValueError('Missing required fields: sensor_id or value')
    return True

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

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize sensor value for consistency.
    
    Args:
        data: Input data to normalize.
    Returns:
        Dict[str, Any]: Normalized data.
    """
    data['value'] = float(data['value'])  # Ensure value is float
    return data

def transform_records(data: Dict[str, Any]) -> Tuple[str, float]:
    """Transform the input record into a tuple for streaming.
    
    Args:
        data: Input sensor data.
    Returns:
        Tuple[str, float]: Tuple containing sensor_id and value.
    """
    return data['sensor_id'], data['value']

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from the streaming source.
    
    Returns:
        List[Dict[str, Any]]: List of sensor data records.
    """
    # Simulated fetching from a database or API
    return [
        {'sensor_id': 'sensor_1', 'value': '23.5'},
        {'sensor_id': 'sensor_2', 'value': '18.3'},
        {'sensor_id': 'sensor_1', 'value': '25.0'},
    ]

def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: List of processed data records.
    """
    # Placeholder for database save logic
    logger.info('Data saved to the database.')

def call_api(data: List[Dict[str, Any]]) -> None:
    """Call an external API with processed data.
    
    Args:
        data: List of data to send to the API.
    """
    # Simulated API call, handle response accordingly
    logger.info('API called with processed data.')

class AnomalyDetector:
    """Main orchestrator class for anomaly detection."""
    def __init__(self) -> None:
        self.model = anomaly.KNN()  # Initialize KNN model for anomaly detection
        self.metrics = metrics.MAE()  # Initialize metrics for evaluation

    def process_batch(self, batch: List[Dict[str, Any]]) -> None:
        """Process a batch of sensor data.
        
        Args:
            batch: List of raw sensor data.
        """
        for record in batch:
            try:
                validate_input(record)  # Validate each incoming record
                record = sanitize_fields(record)  # Sanitize fields
                record = normalize_data(record)  # Normalize data
                sensor_id, value = transform_records(record)  # Transform record
                self.model.learn_one(sensor_id, value)  # Update model with new data
                logger.info(f'Detected anomaly for {sensor_id}: {value}')
            except Exception as e:
                logger.error(f'Error processing record {record}: {str(e)}')

    def aggregate_metrics(self) -> None:
        """Aggregate metrics for performance evaluation."""
        # Code to aggregate metrics and log results
        logger.info(f'Metrics: {self.metrics}')

if __name__ == '__main__':
    # Example usage
    detector = AnomalyDetector()  # Create an instance of the detector
    data = fetch_data()  # Fetch data from source
    detector.process_batch(data)  # Process each batch of data
    detector.aggregate_metrics()  # Aggregate and log metrics
    save_to_db(data)  # Save processed data to the database
    call_api(data)  # Call external API with processed data