Analyze Edge Sensor Data with DuckDB and Polars

                      
                     
"""
Production implementation for analyzing edge sensor data.
Integrates DuckDB and Polars for efficient data processing.
"""

import os
import logging
import polars as pl
import duckdb
from typing import List, Dict, Any, Tuple

# Logger setup for monitoring the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_path: str = os.getenv('DUCKDB_DATABASE_PATH', 'sensor_data.duckdb')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for required fields.
    
    Args:
        data: Dictionary containing sensor data.
    Returns:
        True if valid data.
    Raises:
        ValueError: If validation fails.
    """
    if 'sensor_id' not in data:
        raise ValueError('Missing required field: sensor_id')
    if 'value' not in data:
        raise ValueError('Missing required field: value')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Raw input data dictionary.
    Returns:
        Sanitized data dictionary.
    """
    return {key: str(value).strip() for key, value in data.items()}

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize sensor data for consistent formatting.
    
    Args:
        data: Raw sensor data.
    Returns:
        Normalized data dictionary.
    """
    data['value'] = float(data['value'])  # Ensure value is a float
    return data

def transform_records(records: List[Dict[str, Any]]) -> pl.DataFrame:
    """Transform a list of records into a Polars DataFrame.
    
    Args:
        records: List of sensor data records.
    Returns:
        Polars DataFrame representing the records.
    """
    return pl.DataFrame(records)

def fetch_data(sensor_id: str) -> List[Dict[str, Any]]:
    """Fetch sensor data from the DuckDB database.
    
    Args:
        sensor_id: ID of the sensor to fetch data for.
    Returns:
        List of sensor data records.
    """
    conn = duckdb.connect(Config.database_path)
    query = f"SELECT * FROM sensor_data WHERE sensor_id = '{sensor_id}';"
    return conn.execute(query).fetchall()

def save_to_db(df: pl.DataFrame) -> None:
    """Save the Polars DataFrame to the DuckDB database.
    
    Args:
        df: Polars DataFrame containing sensor data.
    """
    conn = duckdb.connect(Config.database_path)
    conn.execute("CREATE TABLE IF NOT EXISTS sensor_data (sensor_id VARCHAR, value DOUBLE, timestamp TIMESTAMP);")
    conn.execute("INSERT INTO sensor_data SELECT * FROM df;")

def aggregate_metrics(df: pl.DataFrame) -> Dict[str, Any]:
    """Aggregate metrics from the DataFrame.
    
    Args:
        df: Polars DataFrame containing sensor data.
    Returns:
        Dictionary of aggregated metrics.
    """
    return {
        'mean': df['value'].mean(),
        'max': df['value'].max(),
        'min': df['value'].min(),
    }

def format_output(metrics: Dict[str, Any]) -> str:
    """Format the output metrics into a string.
    
    Args:
        metrics: Dictionary of aggregated metrics.
    Returns:
        Formatted metrics string.
    """
    return f"Metrics - Mean: {metrics['mean']}, Max: {metrics['max']}, Min: {metrics['min']}"

class SensorDataAnalyzer:
    """Main class to analyze sensor data from edge devices."""

    def __init__(self, sensor_id: str):
        self.sensor_id = sensor_id
        self.records: List[Dict[str, Any]] = []

    def process_data(self) -> None:
        """Main workflow for processing sensor data."""
        try:
            logger.info(f'Starting data processing for sensor: {self.sensor_id}')
            raw_data = fetch_data(self.sensor_id)  # Fetch raw data
            for record in raw_data:
                validate_input(record)  # Validate each record
                sanitized_record = sanitize_fields(record)  # Sanitize input
                normalized_record = normalize_data(sanitized_record)  # Normalize data
                self.records.append(normalized_record)  # Store normalized record
            df = transform_records(self.records)  # Transform to DataFrame
            metrics = aggregate_metrics(df)  # Aggregate metrics
            output = format_output(metrics)  # Format output
            logger.info(output)  # Log output
            save_to_db(df)  # Save DataFrame to database
        except Exception as e:
            logger.error(f'Error processing data: {str(e)}')
            handle_errors(e)  # Handle errors gracefully

    def run(self) -> None:
        """Run the main analysis workflow."""
        self.process_data()  # Start the data processing

if __name__ == '__main__':
    sensor_id = 'sensor_1'  # Example sensor ID
    analyzer = SensorDataAnalyzer(sensor_id)  # Create analyzer instance
    analyzer.run()  # Run the analysis