Build Real-Time Lakehouse Analytics for Manufacturing with DataFusion and PyIceberg

"""
Production implementation for Real-Time Lakehouse Analytics for Manufacturing.
Utilizes DataFusion and PyIceberg for efficient data processing and analytics.
"""
from typing import Dict, Any, List, Union
import os
import logging
import json
import time
import requests
from contextlib import contextmanager
from sqlalchemy import create_engine, text

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL') or 'sqlite:///:memory:'
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 5))
    retry_delay: int = int(os.getenv('RETRY_DELAY', 2))

@contextmanager
def connection_pool() -> None:
    """Context manager for database connection pooling.
    
    Yields an SQLAlchemy engine for executing queries.
    """
    engine = create_engine(Config.database_url)
    try:
        yield engine
    finally:
        engine.dispose()  # Cleanup the engine

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for processing.
    
    Args:
        data: Input data dictionary
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if not isinstance(data, dict):
        raise ValueError('Input data must be a dictionary.')
    if 'records' not in data:
        raise ValueError('Missing records in input data.')
    return True  # Input is valid

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent SQL injection.
    
    Args:
        data: Input data dictionary
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    return sanitized_data

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Union[str, int]]]:
    """Transform records for analysis.
    
    Args:
        records: List of records to transform
    Returns:
        List[Dict[str, Union[str, int]]]: Transformed records
    """
    transformed = []
    for record in records:
        transformed_record = {
            'id': record['id'],
            'value': int(record['value']),  # Ensure value is an integer
            'timestamp': record['timestamp'],
        }
        transformed.append(transformed_record)
    return transformed

async def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records and store in database.
    
    Args:
        records: List of records to process
    """
    logger.info('Processing batch of records.')
    with connection_pool() as engine:
        with engine.connect() as connection:
            for record in records:
                query = text("INSERT INTO manufacturing_data (id, value, timestamp) VALUES (:id, :value, :timestamp)")
                connection.execute(query, **record)
    logger.info('Batch processing complete.')

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from external API.
    
    Args:
        api_url: The URL of the API to fetch data from
    Returns:
        List[Dict[str, Any]]: List of records retrieved from API
    Raises:
        RuntimeError: If the API request fails
    """
    try:
        response = requests.get(api_url)
        if response.status_code != 200:
            raise RuntimeError('Failed to fetch data from API.')
        return response.json().get('records', [])
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise 

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save sanitized and transformed data to the database.
    
    Args:
        data: List of transformed records
    """
    await process_batch(data)

async def call_api_and_process(api_url: str) -> None:
    """Call external API and process the data.
    
    Args:
        api_url: The URL of the API to call
    """
    try:
        # Fetch data from the API
        raw_data = await fetch_data(api_url)
        # Validate input data
        await validate_input(raw_data)
        # Sanitize input fields
        sanitized_data = await sanitize_fields(raw_data)
        # Transform records for analytics
        transformed_data = await transform_records(sanitized_data['records'])
        # Save to database
        await save_to_db(transformed_data)
    except ValueError as ve:
        logger.error(f'Validation error: {ve}')
    except RuntimeError as re:
        logger.error(f'Runtime error: {re}')
    except Exception as e:
        logger.error(f'Unexpected error: {e}')

if __name__ == '__main__':
    # Example usage
    api_endpoint = 'http://example.com/api/data'
    while True:
        try:
            await call_api_and_process(api_endpoint)
            time.sleep(10)  # Polling every 10 seconds
        except Exception as e:
            logger.error(f'Error during processing: {e}')
            time.sleep(Config.retry_delay)  # Wait before retrying