Build Windowed Quality Metrics for Assembly Lines with Apache Flink and DuckDB

"""
Production implementation for building windowed quality metrics for assembly lines.
Provides secure, scalable operations using Apache Flink and DuckDB.
"""

from typing import Dict, Any, List
import os
import logging
import time
import duckdb
from concurrent.futures import ThreadPoolExecutor, as_completed

# Logger setup to track application behavior
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    max_workers: int = int(os.getenv('MAX_WORKERS', 5))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for processing.
    
    Args:
        data: Input dictionary to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data or 'timestamp' not in data:
        raise ValueError('Missing required fields: id, timestamp')
    logger.debug('Input validation passed.')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize fields in input data.
    
    Args:
        data: Input dictionary to sanitize
    Returns:
        Sanitized input dictionary
    """
    sanitized = {k: str(v).strip() for k, v in data.items()}
    logger.debug('Sanitized fields: %s', sanitized)
    return sanitized

def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize input data for processing.
    
    Args:
        data: List of input dictionaries
    Returns:
        List of normalized dictionaries
    """
    normalized = []
    for record in data:
        try:
            if validate_input(record):
                sanitized_record = sanitize_fields(record)
                normalized.append(sanitized_record)
        except ValueError as e:
            logger.warning('Validation error: %s', e)
    return normalized

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from a source.
    
    Returns:
        List of data records
    """
    # Simulating data fetching from an external source
    logger.info('Fetching data...')
    data = [
        {'id': 1, 'timestamp': '2023-10-01T12:00:00', 'quality': 95},
        {'id': 2, 'timestamp': '2023-10-01T12:05:00', 'quality': 89},
        {'id': 3, 'timestamp': '2023-10-01T12:10:00', 'quality': 98},
    ]
    logger.info('Fetched data: %s', data)
    return data

def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from normalized records.
    
    Args:
        records: List of normalized data records
    Returns:
        Dictionary of aggregated metrics
    """
    total_quality = sum(record['quality'] for record in records)
    count = len(records)
    average_quality = total_quality / count if count > 0 else 0
    logger.info('Aggregated metrics: Average Quality = %s', average_quality)
    return {'average_quality': average_quality}

def save_to_db(metrics: Dict[str, Any]) -> None:
    """Save metrics to DuckDB database.
    
    Args:
        metrics: Metrics dictionary to save
    Raises:
        Exception: If database operation fails
    """
    try:
        logger.info('Saving metrics to database...')
        conn = duckdb.connect(Config.database_url)
        conn.execute('CREATE TABLE IF NOT EXISTS quality_metrics (average_quality FLOAT)')
        conn.execute('INSERT INTO quality_metrics VALUES (?)', (metrics['average_quality'],))
        conn.commit()
        logger.info('Metrics saved to database.')
    except Exception as e:
        logger.error('Error saving to database: %s', e)
        raise
    finally:
        conn.close()

def handle_errors(fn):
    """Decorator to handle errors in functions.
    
    Args:
        fn: Function to wrap
    """
    def wrapper(*args, **kwargs):
        try:
            return fn(*args, **kwargs)
        except Exception as e:
            logger.error('Error in function %s: %s', fn.__name__, e)
            raise
    return wrapper

@handle_errors
def process_batch():
    """Process a batch of data and save metrics.
    """
    data = fetch_data()  # Fetch new data
    normalized_data = normalize_data(data)  # Normalize fetched data
    metrics = aggregate_metrics(normalized_data)  # Aggregate metrics
    save_to_db(metrics)  # Save metrics to database

def main():
    """Main function to orchestrate the workflow.
    """
    logger.info('Starting metrics processing...')
    with ThreadPoolExecutor(max_workers=Config.max_workers) as executor:
        futures = [executor.submit(process_batch) for _ in range(3)]  # Simulate multiple batches
        for future in as_completed(futures):
            try:
                future.result()  # Wait for each batch to complete
            except Exception as e:
                logger.error('Batch processing failed: %s', e)

if __name__ == '__main__':
    main()  # Run the main function