Aggregate Shop Floor Metrics for ML Features with Apache Spark and Polars

"""
Production implementation for aggregating shop floor metrics.
Provides secure, scalable operations using Apache Spark and Polars.
"""
from typing import Dict, Any, List
import os
import logging
import polars as pl
from pyspark.sql import SparkSession
from functools import wraps
import time

# Set up logging to monitor the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL') # Fetching DB URL from environment
    spark_master: str = os.getenv('SPARK_MASTER', 'local[*]') # Default to local mode

# Retry decorator for handling transient errors
def retry(max_retries: int = 3, backoff: float = 2.0):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            attempts = 0
            while attempts < max_retries:
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    attempts += 1
                    logger.warning(f"Attempt {attempts} failed: {e}")
                    time.sleep(backoff ** attempts)
            raise RuntimeError(f'Failed after {max_retries} attempts')
        return wrapper
    return decorator

# Validate input data ensuring necessary fields are present
async 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 'shop_id' not in data:
        raise ValueError('Missing shop_id')
    if 'metrics' not in data:
        raise ValueError('Missing metrics')
    return True

# Sanitize fields to prevent injection attacks
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}  # Stripping unnecessary spaces

# Fetch data from the database
@retry()
async def fetch_data(shop_id: str) -> List[Dict[str, Any]]:
    """Fetch metrics from the database.
    
    Args:
        shop_id: ID of the shop
    Returns:
        List of metrics
    """
    logger.info(f'Fetching data for shop_id: {shop_id}')
    # Simulating database fetch
    # Actual DB access code would be added here
    return [{'timestamp': '2023-10-10T10:00:00Z', 'value': 100}, {'timestamp': '2023-10-10T11:00:00Z', 'value': 150}]

# Transform records to Polars DataFrame
def transform_records(records: List[Dict[str, Any]]) -> pl.DataFrame:
    """Transform raw records to Polars DataFrame.
    
    Args:
        records: Raw input records
    Returns:
        Polars DataFrame
    """
    return pl.DataFrame(records)  # Converting list of dicts to Polars DataFrame

# Aggregate metrics to derive features for ML
def aggregate_metrics(df: pl.DataFrame) -> pl.DataFrame:
    """Aggregate shop floor metrics.
    
    Args:
        df: Polars DataFrame of metrics
    Returns:
        Aggregated DataFrame
    """
    aggregated_df = df.groupby('timestamp').agg(pl.col('value').mean().alias('mean_value'))  # Aggregating metrics
    logger.info('Aggregated metrics successfully')
    return aggregated_df

# Save aggregated data to the database
@retry()
async def save_to_db(df: pl.DataFrame) -> None:
    """Save DataFrame to the database.
    
    Args:
        df: DataFrame to save
    Raises:
        RuntimeError: If saving fails
    """
    logger.info('Saving aggregated data to the database')
    # Simulating database save
    # Actual DB save code would be added here
    logger.info('Data saved successfully')

# Main orchestrator class for handling the workflow
class MetricsAggregator:
    def __init__(self, config: Config):
        self.config = config
        self.spark = SparkSession.builder.master(self.config.spark_master).getOrCreate()  # Initialize Spark session

    async def process_metrics(self, shop_id: str):
        """Process metrics for a given shop ID.
        
        Args:
            shop_id: ID of the shop
        """
        try:
            # Fetch and validate data
            data = await fetch_data(shop_id)
            await validate_input(data)
            sanitized_data = sanitize_fields(data)

            # Transform and aggregate metrics
            df = transform_records(sanitized_data)
            aggregated_df = aggregate_metrics(df)

            # Save results back to the database
            await save_to_db(aggregated_df)
        except Exception as e:
            logger.error(f'Error processing metrics: {e}')  # Log the error

# Main entry point for execution
if __name__ == '__main__':
    config = Config()  # Load configuration
    aggregator = MetricsAggregator(config)  # Create aggregator instance
    # Example usage
    import asyncio
    asyncio.run(aggregator.process_metrics('shop_001'))