Query IIoT Sensor Archives with Embedded OLAP Using chDB and Polars

"""
Production implementation for querying IIoT sensor archives.
Provides secure, scalable operations with embedded OLAP using chDB and Polars.
"""
import os
import logging
import time
import polars as pl
from typing import Dict, Any, List, Tuple
import sqlalchemy as sa
from sqlalchemy.orm import sessionmaker, scoped_session

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

# Database connection pooling
DATABASE_URL = os.getenv('DATABASE_URL')
engine = sa.create_engine(DATABASE_URL, pool_size=5, max_overflow=10)
session_factory = scoped_session(sessionmaker(bind=engine))

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = DATABASE_URL

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 'sensor_id' not in data:
        raise ValueError('Missing sensor_id')  # Ensure sensor_id is provided
    return True

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

def fetch_data(sensor_id: str, start_time: str, end_time: str) -> pl.DataFrame:
    """Fetch data from the database.
    Args:
        sensor_id: ID of the sensor
        start_time: Start time for the query
        end_time: End time for the query
    Returns:
        DataFrame containing sensor data
    """
    logger.info(f'Fetching data for sensor_id: {sensor_id} from {start_time} to {end_time}')
    with session_factory() as session:
        query = "SELECT * FROM sensor_data WHERE sensor_id = :sensor_id AND timestamp BETWEEN :start_time AND :end_time"  # SQL Query
        result = session.execute(sa.text(query), {'sensor_id': sensor_id, 'start_time': start_time, 'end_time': end_time})
        # Convert result to Polars DataFrame
        return pl.DataFrame(result.fetchall(), schema=result.keys())

def transform_records(df: pl.DataFrame) -> pl.DataFrame:
    """Transform records for OLAP processing.
    Args:
        df: Raw DataFrame
    Returns:
        Transformed DataFrame
    """
    # Example transformation
    return df.with_columns([pl.col('value').cast(pl.Float64)])

def aggregate_metrics(df: pl.DataFrame) -> Dict[str, Any]:
    """Aggregate metrics for analysis.
    Args:
        df: DataFrame to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    return {
        'average': df['value'].mean(),
        'max': df['value'].max(),
        'min': df['value'].min()
    }

def save_to_db(metrics: Dict[str, Any]) -> None:
    """Save aggregated metrics back to the database.
    Args:
        metrics: Metrics to save
    Raises:
        Exception: On failure to save
    """
    logger.info('Saving metrics to database')
    with session_factory() as session:
        metrics_entry = sa.Table('metrics', sa.MetaData(), autoload_with=engine)
        stmt = metrics_entry.insert().values(metrics)
        session.execute(stmt)
        session.commit()

def handle_errors(func):
    """Decorator to handle errors in functions.
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error occurred: {e}')
            raise
    return wrapper

@handle_errors
def process_batch(sensor_id: str, start_time: str, end_time: str) -> None:
    """Process a batch of sensor data.
    Args:
        sensor_id: Sensor ID
        start_time: Start time for batch processing
        end_time: End time for batch processing
    """
    validate_input({'sensor_id': sensor_id})  # Validate input
    raw_data = fetch_data(sensor_id, start_time, end_time)  # Fetch data
    transformed_data = transform_records(raw_data)  # Transform data
    metrics = aggregate_metrics(transformed_data)  # Aggregate metrics
    save_to_db(metrics)  # Save metrics to DB

if __name__ == '__main__':
    # Example usage
    sensor_id = 'sensor_1'
    start_time = '2023-01-01 00:00:00'
    end_time = '2023-01-01 23:59:59'
    process_batch(sensor_id, start_time, end_time)  # Process data batch