Background Job Processing

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• 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:

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