Background Job Processing
Table of Contents
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:
| Guide | Contents |
|---|---|
| Python with Celery and Redis | Python with Celery and Redis |
| Node.js with Bull Queue | Node.js with Bull Queue |
| Ruby with Sidekiq | Ruby with Sidekiq |
| Job Retry and Error Handling | Job Retry and Error Handling |
| Monitoring and Observability | Monitoring and Observability |
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