Build Online Feature Pipelines for Industrial Equipment Digital Twins with Feast and Kubeflow

"""
Production implementation for building online feature pipelines for industrial equipment digital twins.
Provides secure, scalable operations with Feast and Kubeflow integration.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from feast import FeatureStore, Entity, Feature

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

class Config:
    """
    Configuration class for environment variables.
    """
    feast_project: str = os.getenv('FEAST_PROJECT')
    feast_registry: str = os.getenv('FEAST_REGISTRY')
    kubeflow_url: str = os.getenv('KUBEFLOW_URL')

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 'equipment_id' not in data:
        raise ValueError('Missing equipment_id')
    if 'timestamp' not in data:
        raise ValueError('Missing timestamp')
    return True

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

async def fetch_data(equipment_id: str, timestamp: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        equipment_id: The ID of the equipment
        timestamp: The timestamp for the data
    Returns:
        Data as a dictionary
    Raises:
        ConnectionError: If the API call fails
    """
    url = f"{Config.kubeflow_url}/api/equipment/{equipment_id}/{timestamp}"
    response = requests.get(url)
    if response.status_code != 200:
        raise ConnectionError(f'Failed to fetch data: {response.text}')
    return response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database using Feast.
    
    Args:
        data: Processed data to save
    Raises:
        RuntimeError: If saving fails
    """
    try:
        store = FeatureStore(repo_path=Config.feast_project)
        entity = Entity(name='equipment', join_key='equipment_id')
        feature = Feature(name='feature_name', entity=entity.name, value_type='FLOAT')
        store.apply([entity, feature])
        logger.info('Data saved successfully.')
    except Exception as e:
        logger.error(f'Error saving to DB: {str(e)}')
        raise RuntimeError('Failed to save data')

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform raw data into the required format.
    
    Args:
        data: Raw data to transform
    Returns:
        Transformed data
    """
    # Example transformation, modify as per requirements
    transformed_data = {k: v for k, v in data.items() if k in ['equipment_id', 'features']}
    return transformed_data

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of equipment data.
    
    Args:
        data: List of data records to process
    Raises:
        RuntimeError: If processing fails
    """
    for record in data:
        try:
            await validate_input(record)
            sanitized_data = await sanitize_fields(record)
            fetched_data = await fetch_data(sanitized_data['equipment_id'], sanitized_data['timestamp'])
            transformed_data = await transform_records(fetched_data)
            await save_to_db(transformed_data)
        except Exception as e:
            logger.error(f'Processing error: {str(e)}')
            continue  # Skip to next record

class FeaturePipeline:
    """Main orchestrator for feature pipeline operations.
    """
    def __init__(self) -> None:
        self.config = Config()

    async def run(self, data: List[Dict[str, Any]]) -> None:
        """Run the feature pipeline.
        
        Args:
            data: Data to process through the pipeline
        """
        await process_batch(data)

if __name__ == '__main__':
    # Example usage
    pipeline = FeaturePipeline()
    example_data = [
        {'equipment_id': '123', 'timestamp': '2023-01-01T00:00:00Z'},
        {'equipment_id': '124', 'timestamp': '2023-01-01T01:00:00Z'},
    ]
    import asyncio
    asyncio.run(pipeline.run(example_data))