Schedule and Replay Industrial ML Batch Jobs on Kubernetes with Argo Workflows and BentoML

"""
Production implementation for scheduling and replaying industrial ML batch jobs.
Utilizes Argo Workflows and BentoML for streamlined operations.
"""
from typing import Dict, Any, List
import os
import logging
import requests
import time
from contextlib import contextmanager

# Setting up logging for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    # Environment variable configurations
    bento_service_url: str = os.getenv('BENTO_SERVICE_URL')
    argo_workflow_url: str = os.getenv('ARGO_WORKFLOW_URL')
    retry_attempts: int = 3

@contextmanager
def connect_db() -> None:
    """Context manager for database connection.
    
    Yields:
        Database connection object
    """    
    db_conn = None  # Placeholder for DB connection
    try:
        # Simulate opening a database connection
        logger.info('Connecting to the database...')
        db_conn = 'db_connection'
        yield db_conn
    except Exception as e:
        logger.error(f'Error connecting to database: {e}')
    finally:
        if db_conn:
            logger.info('Closing the database connection...')
            # Simulate closing the database connection
            pass

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 'job_id' not in data:
        raise ValueError('Missing job_id')
    if 'parameters' not in data:
        raise ValueError('Missing parameters')
    return True

async def fetch_data(job_id: str) -> Dict[str, Any]:
    """Fetch job data from an external API.
    
    Args:
        job_id: The ID of the job to fetch data for
    Returns:
        Job data as a dictionary
    Raises:
        Exception: If the fetch fails
    """    
    try:
        response = requests.get(f'{Config.bento_service_url}/jobs/{job_id}')
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except Exception as e:
        logger.error(f'Error fetching data for job {job_id}: {e}')
        raise

async def save_to_db(job_data: Dict[str, Any]) -> None:
    """Save job data into the database.
    
    Args:
        job_data: The job data to save
    """    
    with connect_db() as db_conn:
        # Simulate saving data to the database
        logger.info(f'Saving job data: {job_data} to database...')
        pass  # Replace with actual DB operation

async def call_api(endpoint: str, data: Dict[str, Any]) -> None:
    """Call external API for processing.
    
    Args:
        endpoint: The API endpoint to call
        data: The data to send in the request
    Raises:
        Exception: If the API call fails
    """    
    for attempt in range(Config.retry_attempts):
        try:
            response = requests.post(endpoint, json=data)
            response.raise_for_status()
            logger.info('API call successful')
            return
        except Exception as e:
            logger.warning(f'Retry {attempt + 1}/{Config.retry_attempts} failed: {e}')
            time.sleep(2 ** attempt)  # Exponential backoff
    raise Exception('Max retry attempts exceeded')

async def process_batch(data: Dict[str, Any]) -> None:
    """Process the batch of job data.
    
    Args:
        data: The data for the batch job
    """    
    await validate_input(data)  # Validate input
    job_data = await fetch_data(data['job_id'])  # Fetch job data
    await save_to_db(job_data)  # Save data to DB
    await call_api(Config.bento_service_url, job_data)  # Call API

class JobScheduler:
    """Main orchestrator for scheduling jobs.
    
    Attributes:
        job_queue: List of job data
    """    
    def __init__(self):
        self.job_queue: List[Dict[str, Any]] = []  # Queue for jobs

    def schedule_job(self, job_data: Dict[str, Any]) -> None:
        """Schedule a new job.
        
        Args:
            job_data: Data required to schedule the job
        """        
        self.job_queue.append(job_data)  # Add job to queue
        logger.info(f'Job scheduled: {job_data}')

    async def run_jobs(self) -> None:
        """Execute all scheduled jobs.
        """        
        for job in self.job_queue:
            try:
                await process_batch(job)  # Process each job in the queue
            except Exception as e:
                logger.error(f'Error processing job {job}: {e}')

if __name__ == '__main__':
    # Example usage
    scheduler = JobScheduler()
    job_example = {'job_id': '123', 'parameters': {'param1': 'value1'}}
    scheduler.schedule_job(job_example)  # Schedule a job
    import asyncio
    asyncio.run(scheduler.run_jobs())  # Run scheduled jobs