Background Job Processing

Table of Contents

• [Overview](#overview)
• [When to Use](#when-to-use)
• [Quick Start](#quick-start)
• [Reference Guides](#reference-guides)
• [Best Practices](#best-practices)

Overview

Build robust background job processing systems with distributed task queues, worker pools, job scheduling, error handling, retry policies, and monitoring for efficient asynchronous task execution.

When to Use

• Handling long-running operations asynchronously
• Sending emails in background
• Generating reports or exports
• Processing large datasets
• Scheduling recurring tasks
• Distributing compute-intensive operations

Quick Start

Minimal working example:

# celery_app.py
from celery import Celery
from kombu import Exchange, Queue
import os

app = Celery('myapp')

# Configuration
app.conf.update(
    broker_url=os.getenv('REDIS_URL', 'redis://localhost:6379/0'),
    result_backend=os.getenv('REDIS_URL', 'redis://localhost:6379/0'),
    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
    broker_connection_retry_on_startup=True,
)

# Queue configuration
default_exchange = Exchange('tasks', type='direct')
app.conf.task_queues = (
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Best Practices

✅ DO

• Use task timeouts to prevent hanging jobs
• Implement retry logic with exponential backoff
• Make tasks idempotent
• Use job priorities for critical tasks
• Monitor queue depths and job failures
• Log job execution details
• Clean up completed jobs
• Set appropriate batch sizes for memory efficiency
• Use dead-letter queues for failed jobs
• Test jobs independently

❌ DON'T

• Use synchronous operations in async tasks
• Ignore job failures
• Make tasks dependent on external state
• Use unbounded retries
• Store large objects in job data
• Forget to handle timeouts
• Run jobs without monitoring
• Use blocking operations in queues
• Forget to track job progress
• Mix unrelated operations in one job