# jobs.worker
Background job worker system with Celery integration for asynchronous task processing

deps: @..models.job[Job, JobStatus, JobType]; @..models.user[User]; @..models.order[Order]; @..models.inventory[Inventory]; @..core.database[SessionLocal, get_redis]

# Celery configuration
CELERY_BROKER_URL: str = os.getenv("REDIS_URL", "redis://localhost:6379")
CELERY_RESULT_BACKEND: str = os.getenv("REDIS_URL", "redis://localhost:6379")

celery_app = Celery(
    "ecommerce_jobs",
    broker=CELERY_BROKER_URL,
    backend=CELERY_RESULT_BACKEND,
    include=["jobs.tasks"]
)

celery_app.conf.update(
    task_serializer="json",
    accept_content=["json"],
    result_serializer="json",
    timezone="UTC",
    enable_utc=True,
    task_track_started=True,
    task_time_limit=30 * 60,  # 30 minutes
    task_soft_time_limit=25 * 60,  # 25 minutes
    worker_prefetch_multiplier=1
)

# Job worker class
JobWorker:
    - __init__(db: Session)
    
    # Job processing
    - process_job(job_id: str) -> bool
    - start_job(job: Job) -> None
    - complete_job(job: Job, result: dict = None) -> None
    - fail_job(job: Job, error_message: str, traceback: str = None) -> None
    - update_job_progress(job: Job, percentage: int, message: str = None) -> None
    
    # Job type processors
    - process_email_job(job: Job) -> dict
    - process_order_job(job: Job) -> dict
    - process_inventory_sync_job(job: Job) -> dict
    - process_payment_job(job: Job) -> dict
    - process_analytics_job(job: Job) -> dict
    - process_image_processing_job(job: Job) -> dict
    - process_data_export_job(job: Job) -> dict
    
    # Retry logic
    - should_retry_job(job: Job, exception: Exception) -> bool
    - schedule_job_retry(job: Job, delay_seconds: int = None) -> None
    - calculate_retry_delay(retry_count: int) -> int
    
    # Job management
    - get_pending_jobs(queue_name: str = "default", limit: int = 10) -> List[Job]
    - cleanup_old_jobs(days_old: int = 30) -> int
    - get_worker_statistics() -> dict

# Email service integration
EmailService:
    - __init__(smtp_host: str, smtp_port: int, username: str, password: str)
    - send_email(to: str, subject: str, body: str, html_body: str = None) -> bool
    - send_template_email(to: str, template: str, context: dict) -> bool
    - send_bulk_email(recipients: List[str], subject: str, body: str) -> dict

# Job queue management functions
start_worker():
    """Start the Celery worker process"""
    # Configure logging
    # Start worker with appropriate settings
    # Handle graceful shutdown

schedule_periodic_tasks():
    """Schedule recurring background tasks"""
    # Inventory sync jobs
    # Analytics report generation
    # System cleanup tasks
    # Health check jobs

monitor_job_queues():
    """Monitor job queue health"""
    # Check queue lengths
    # Monitor worker status
    # Alert on job failures
    # Return queue statistics

# Utility functions
create_job_async(job_type: JobType, title: str, parameters: dict = None, 
                user_id: int = None, priority: int = 0, 
                scheduled_for: datetime = None) -> str:
    """Create and queue a background job"""
    # Create job record
    # Queue job for processing
    # Return job ID

get_job_result(job_id: str) -> dict:
    """Get job result from Celery"""
    # Query job status from Celery
    # Return result data

cancel_job_async(job_id: str) -> bool:
    """Cancel a queued or running job"""
    # Cancel job in Celery
    # Update job status
    # Return cancellation status

notes: Full Celery integration, comprehensive job processing, retry logic with exponential backoff, email service integration, job monitoring, queue health management