Build Streaming Feature Pipelines for Predictive Maintenance with Bytewax and DuckDB

"""
Production implementation for building streaming feature pipelines for predictive maintenance.
Provides secure, scalable operations using Bytewax and DuckDB.
"""
from typing import Dict, Any, List
import os
import logging
import time
import duckdb
from bytewax import Dataflow, run

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'duckdb:///:memory:')

    @classmethod
    def validate(cls) -> None:
        if not cls.database_url:
            raise ValueError('DATABASE_URL environment variable not set.')

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 'sensor_id' not in data or 'timestamp' not in data:
        raise ValueError('Missing required fields: sensor_id or timestamp')
    return True

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

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform raw input data into features for the model.
    
    Args:
        data: Raw input data
    Returns:
        Transformed data for processing
    """
    data['value'] = float(data['value'])  # Ensure value is float
    return data

async def process_batch(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of records by aggregating metrics.
    
    Args:
        records: List of records to process
    Returns:
        Processed metrics
    """
    metrics = []
    for record in records:
        metrics.append({
            'sensor_id': record['sensor_id'],
            'avg_value': sum(record['value']) / len(record['value'])
        })
    return metrics

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from the streaming source.
    
    Returns:
        List of raw data records
    """    
    # Simulated fetch from a data source
    return [
        {'sensor_id': 'sensor_1', 'timestamp': time.time(), 'value': 22.5},
        {'sensor_id': 'sensor_1', 'timestamp': time.time(), 'value': 23.0}
    ]

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to DuckDB.
    
    Args:
        data: Processed data to save
    """
    connection = duckdb.connect(Config.database_url)
    for record in data:
        connection.execute(
            'INSERT INTO metrics (sensor_id, avg_value) VALUES (?, ?)',
            (record['sensor_id'], record['avg_value'])
        )
    connection.close()

async def call_api(data: Dict[str, Any]) -> None:
    """Call external API to notify about processed data.
    
    Args:
        data: Data to send to the API
    """
    # Simulated API call
    logger.info(f'Notifying API with data: {data}')

async def handle_errors(func):
    """Error handling decorator.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error occurred: {e}')
            raise
    return wrapper

class StreamingPipeline:
    def __init__(self):
        self.config = Config()
        self.config.validate()  # Validate configuration

    async def run(self):
        # Main processing loop
        logger.info('Starting streaming pipeline...')
        data = await fetch_data()  # Fetch raw data
        await validate_input(data)  # Validate data
        sanitized_data = await sanitize_fields(data)  # Sanitize fields
        transformed_data = await transform_records(sanitized_data)  # Transform data
        metrics = await process_batch(transformed_data)  # Process data
        await save_to_db(metrics)  # Save to database
        await call_api(metrics)  # Notify API

if __name__ == '__main__':
    pipeline = StreamingPipeline()
    run(pipeline.run())  # Run the streaming pipeline