Process Real-Time Assembly Line Metrics with PyFlink and Polars

                      
                     
"""
Production implementation for processing real-time assembly line metrics.
Utilizes PyFlink for data stream processing and Polars for data manipulation.
"""
import os
import logging
from typing import Dict, Any, List
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.common import Types
import polars as pl
import time

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class to load environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')
    flink_job_name: str = os.getenv('FLINK_JOB_NAME', 'AssemblyLineMetricsJob')

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

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

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize incoming data values.
    
    Args:
        data: Input data dictionary.
    Returns:
        dict: Normalized data.
    """  
    data['value'] = float(data['value'])  # Ensure value is float
    return data

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from the source.
    
    Returns:
        list: List of dictionaries representing incoming data.
    """  
    # Simulate data fetching
    return [{'id': 1, 'value': '100'}, {'id': 2, 'value': '200'}]

def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data.
    
    Args:
        batch: List of raw data dictionaries.
    Returns:
        list: Processed batch of dictionaries.
    """  
    processed = []
    for item in batch:
        try:
            if validate_input(item):  # Validate item
                sanitized = sanitize_fields(item)
                normalized = normalize_data(sanitized)
                processed.append(normalized)
        except ValueError as e:
            logger.warning(f'Validation failed: {str(e)}')  # Log validation failure
    return processed

def aggregate_metrics(data: List[Dict[str, Any]]) -> pl.DataFrame:
    """Aggregate metrics using Polars.
    
    Args:
        data: List of processed data dictionaries.
    Returns:
        DataFrame: Aggregated metrics.
    """  
    df = pl.DataFrame(data)  # Create Polars DataFrame
    return df.groupby('id').agg(pl.sum('value').alias('total_value'))

def save_to_db(df: pl.DataFrame) -> None:
    """Save aggregated metrics to the database.
    
    Args:
        df: DataFrame containing metrics to save.
    """  
    # Simulate saving to database
    logger.info('Saving metrics to the database...')
    time.sleep(1)  # Simulate a delay
    logger.info('Metrics saved successfully.')

class AssemblyLineMetrics:
    """Main orchestrator for processing metrics."""
    def __init__(self) -> None:
        self.env = StreamExecutionEnvironment.get_execution_environment()
        self.env.set_parallelism(1)  # Set parallelism for the job

    def run(self) -> None:
        """Run the data processing pipeline."""
        logger.info('Starting the metrics processing job...')
        raw_data = fetch_data()  # Fetch raw data
        processed_data = process_batch(raw_data)  # Process raw data
        aggregated_df = aggregate_metrics(processed_data)  # Aggregate metrics
        save_to_db(aggregated_df)  # Save results to DB
        logger.info('Job completed successfully!')

if __name__ == '__main__':
    # Example usage
    metrics_processor = AssemblyLineMetrics()
    metrics_processor.run()  # Execute the processing job