Orchestrate Robotics Pipelines with OpenALRA and Kubeflow

                      
                     
"""
Production implementation for orchestrating robotics pipelines with OpenALRA and Kubeflow.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import time
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///robotics.db')
    kubeflow_url: str = os.getenv('KUBEFLOW_URL', 'http://localhost:8080')

# Connection pooling setup
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
Session = sessionmaker(bind=engine)

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 'robot_id' not in data:
        raise ValueError('Missing robot_id')
    if 'task' not in data:
        raise ValueError('Missing task')
    return True

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

async def transform_records(raw_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw data into the required format.
    
    Args:
        raw_data: List of raw input data
    Returns:
        List of transformed data
    """
    return [{'robot_id': record['robot_id'], 'task': record['task']} for record in raw_data]

async def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records.
    
    Args:
        records: List of records to process
    """
    for record in records:
        # Log the processing of the record
        logger.info(f'Processing record: {record}')
        await call_api(record)

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        api_url: URL to fetch data from
    Returns:
        List of data records
    Raises:
        Exception: If fetch fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()
        return response.json()  # Assuming the API returns JSON
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save records to the database.
    
    Args:
        records: List of records to save
    """
    with Session() as session:
        for record in records:
            session.execute(text('INSERT INTO tasks (robot_id, task) VALUES (:robot_id, :task)'), record)
        session.commit()

async def call_api(record: Dict[str, Any]) -> None:
    """Call an external API for processing.
    
    Args:
        record: The record to process
    """
    try:
        url = f'{Config.kubeflow_url}/process'
        response = requests.post(url, json=record)
        response.raise_for_status()
        logger.info(f'Successfully processed record: {record}')
    except Exception as e:
        logger.error(f'Error calling API: {e}')
        raise

async def aggregate_metrics() -> Dict[str, Any]:
    """Aggregate metrics from the database.
    
    Returns:
        Aggregated metrics
    """
    with Session() as session:
        result = session.execute(text('SELECT robot_id, COUNT(*) FROM tasks GROUP BY robot_id')).fetchall()
        return {row[0]: row[1] for row in result}

class RoboticsPipeline:
    """Main orchestrator for the robotics pipeline.
    
    Methods:
        execute_pipeline(): Executes the entire pipeline workflow.
    """
    async def execute_pipeline(self) -> None:
        """Execute the complete robotics pipeline.
        
        Returns:
            None
        """
        try:
            # Fetch and validate data
            raw_data = await fetch_data('http://example.com/api/tasks')
            await validate_input(raw_data)
            sanitized_data = await sanitize_fields(raw_data)
            transformed_data = await transform_records(sanitized_data)
            await process_batch(transformed_data)
            await save_to_db(transformed_data)
            metrics = await aggregate_metrics()
            logger.info(f'Aggregated metrics: {metrics}')
        except Exception as e:
            logger.error(f'Failed to execute pipeline: {e}')

if __name__ == '__main__':
    pipeline = RoboticsPipeline()
    # Execute the pipeline
    import asyncio
    asyncio.run(pipeline.execute_pipeline())