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django-celery

Add background jobs to Django with Celery, moving work such as email delivery and PDF generation outside web requests. Covers scheduled runs, retries, linked or parallel tasks, monitoring, and tests, with Redis or RabbitMQ carrying task messages.

OriginalShow the original description

Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.

Works withRedisSentry
AI rating80/ 100See how to install

Good for

  • Moving email delivery or PDF generation into background jobs
  • Scheduling inventory updates and recurring notifications
  • Designing retries and safeguards against duplicate processing
  • Linking tasks sequentially or running them in parallel
  • Investigating task failures and testing background jobs

Overview

What it does

Helps configure and design background processing in Django with Celery. It moves email delivery, PDF generation, and external API calls out of the work needed to respond to a web request. It covers recurring schedules, execution at a specified time, retries after failures, and execution time limits. It also explains how to connect tasks in sequence or collect results after parallel tasks finish.

When it helps

Use it when adding demanding work to a Django application or setting up inventory synchronization and recurring notifications. You can refer to procedures for investigating failures and queue backlogs, designs that prevent duplicate updates when tasks run again, and examples of task-level and application integration tests.

Example requests

  • “Move the email sent after registration into a Celery task.”
  • “Design scheduled inventory synchronization with retries for connection failures.”
  • “Work out time limits and tests for the PDF generation task.”

Notes

You need Django, Celery, and Redis or RabbitMQ to carry task messages. Examples use django-celery-results for result storage and django-celery-beat for scheduling through the administration interface. In production, run workers and Beat separately, with only one Beat instance to avoid duplicate scheduled runs.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodActivation criteria explicitly cover background processing, scheduled tasks, and failure investigation.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt provides configuration code, worker startup commands, and implementation examples for retries and duplicate-update prevention.
SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackingExamples send email and synchronize with an external CRM, but no pre-send confirmation procedure is given.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingA pip installation command is provided, but setup instructions for Redis or RabbitMQ themselves are absent.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.

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SKILL.md (original)

Review the instructions the agent will receive before you install.

Django + Celery Async Task Patterns

Production-grade patterns for background task processing in Django using Celery with Redis or RabbitMQ.

When to Activate

  • Adding background jobs or async processing to a Django app
  • Implementing periodic/scheduled tasks
  • Offloading slow operations (email, PDF generation, API calls) from request cycle
  • Setting up Celery Beat for cron-like scheduling
  • Debugging task failures, retries, or queue backlogs
  • Writing tests for Celery tasks

Project Setup

Installation

pip install 'celery[redis]' django-celery-results django-celery-beat

celery.py — App Entrypoint

# config/celery.py
import os
from celery import Celery

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings.development')

app = Celery('myproject')
app.config_from_object('django.conf:settings', namespace='CELERY')
app.autodiscover_tasks()  # Discovers tasks.py in each INSTALLED_APP

@app.task(bind=True, ignore_result=True)
def debug_task(self):
    print(f'Request: {self.request!r}')
# config/__init__.py
from .celery import app as celery_app

__all__ = ('celery_app',)

Django Settings

# config/settings/base.py

# Broker (Redis recommended for production)
CELERY_BROKER_URL = env('CELERY_BROKER_URL', default='redis://localhost:6379/0')
CELERY_RESULT_BACKEND = env('CELERY_RESULT_BACKEND', default='django-db')

# Serialization
CELERY_ACCEPT_CONTENT = ['json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'

# Task behavior
CELERY_TASK_TRACK_STARTED = True
CELERY_TASK_TIME_LIMIT = 30 * 60        # Hard limit: 30 min
CELERY_TASK_SOFT_TIME_LIMIT = 25 * 60   # Soft limit: sends SoftTimeLimitExceeded
CELERY_WORKER_PREFETCH_MULTIPLIER = 1   # Prevent worker hoarding long tasks
CELERY_TASK_ACKS_LATE = True            # Re-queue on worker crash

# Result persistence
CELERY_RESULT_EXPIRES = 60 * 60 * 24   # Keep results 24 hours

# Beat scheduler (for periodic tasks)
CELERY_BEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'

# Installed apps
INSTALLED_APPS += [
    'django_celery_results',
    'django_celery_beat',
]

Running Workers

# Start worker (development)
celery -A config worker --loglevel=info

# Start beat scheduler (periodic tasks)
celery -A config beat --loglevel=info --scheduler django_celery_beat.schedulers:DatabaseScheduler

# Combined worker + beat (dev only, never production)
celery -A config worker --beat --loglevel=info

# Production: multiple workers with concurrency
celery -A config worker --loglevel=warning --concurrency=4 -Q default,high_priority

Task Design Patterns

Basic Task

# apps/notifications/tasks.py
from celery import shared_task
import logging

logger = logging.getLogger(__name__)

@shared_task(name='notifications.send_welcome_email')
def send_welcome_email(user_id: int) -> None:
    """Send welcome email to newly registered user."""
    from apps.users.models import User
    from apps.notifications.services import EmailService

    try:
        user = User.objects.get(pk=user_id)
    except User.DoesNotExist:
        logger.warning('send_welcome_email: user %s not found', user_id)
        return  # Idempotent — do not raise, task already impossible to complete

    EmailService.send_welcome(user)
    logger.info('Welcome email sent to user %s', user_id)

Retryable Task

@shared_task(
    bind=True,
    name='integrations.sync_to_crm',
    max_retries=5,
    default_retry_delay=60,       # seconds before first retry
    autoretry_for=(ConnectionError, TimeoutError),
    retry_backoff=True,           # exponential backoff
    retry_backoff_max=600,        # cap at 10 minutes
    retry_jitter=True,            # randomise to avoid thundering herd
)
def sync_contact_to_crm(self, contact_id: int) -> dict:
    """Sync contact to external CRM with retry on transient failures."""
    from apps.crm.services import CRMClient

    try:
        result = CRMClient().sync(contact_id)
        return result
    except CRMClient.RateLimitError as exc:
        # Specific retry delay from response header
        raise self.retry(exc=exc, countdown=int(exc.retry_after))

Idempotent Task Pattern

Design tasks so they can safely run multiple times with the same inputs:

@shared_task(name='orders.mark_shipped')
def mark_order_shipped(order_id: int, tracking_number: str) -> None:
    """Mark order as shipped — safe to run multiple times."""
    from apps.orders.models import Order

    updated = Order.objects.filter(
        pk=order_id,
        status=Order.Status.PROCESSING,    # Guard: only update if not already shipped
    ).update(
        status=Order.Status.SHIPPED,
        tracking_number=tracking_number,
    )

    if not updated:
        logger.info('mark_order_shipped: order %s already shipped or not found', order_id)

Task with Soft Time Limit

from celery.exceptions import SoftTimeLimitExceeded

@shared_task(
    bind=True,
    name='reports.generate_pdf',
    soft_time_limit=120,
    time_limit=150,
)
def generate_pdf_report(self, report_id: int) -> str:
    """Generate PDF report with graceful timeout handling."""
    from apps.reports.services import PDFGenerator

    try:
        path = PDFGenerator.build(report_id)
        return path
    except SoftTimeLimitExceeded:
        # Clean up partial files before hard kill
        PDFGenerator.cleanup(report_id)
        raise

Calling Tasks

from datetime import timedelta
from django.utils import timezone

# Fire and forget (async)
send_welcome_email.delay(user.pk)

# Schedule in the future
send_reminder.apply_async(args=[user.pk], countdown=3600)  # 1 hour from now
send_reminder.apply_async(args=[user.pk], eta=timezone.now() + timedelta(days=1))

# Apply with queue routing
sync_contact_to_crm.apply_async(args=[contact.pk], queue='high_priority')

# Run synchronously (tests / debugging only)
result = generate_pdf_report.apply(args=[report.pk])

Beat Scheduling (Periodic Tasks)

Code-Defined Schedule

# config/settings/base.py
from celery.schedules import crontab

CELERY_BEAT_SCHEDULE = {
    'cleanup-expired-sessions': {
        'task': 'users.cleanup_expired_sessions',
        'schedule': crontab(hour=2, minute=0),   # 2am daily
    },
    'sync-inventory': {
        'task': 'products.sync_inventory',
        'schedule': 60.0,                         # every 60 seconds
    },
    'weekly-digest': {
        'task': 'notifications.send_weekly_digest',
        'schedule': crontab(day_of_week='monday', hour=8, minute=0),
    },
}

Database-Defined Schedule (via django-celery-beat)

# Manage periodic tasks from Django admin or code
from django_celery_beat.models import PeriodicTask, CrontabSchedule
import json

schedule, _ = CrontabSchedule.objects.get_or_create(
    hour='*/6', minute='0',
    timezone='UTC',
)

PeriodicTask.objects.update_or_create(
    name='Sync inventory every 6 hours',
    defaults={
        'crontab': schedule,
        'task': 'products.sync_inventory',
        'args': json.dumps([]),
        'enabled': True,
    }
)

Canvas: Chaining and Grouping Tasks

from celery import chain, group, chord

# Chain: run tasks sequentially, passing results
pipeline = chain(
    fetch_data.s(source_id),
    transform_data.s(),          # receives fetch_data result as first arg
    load_to_warehouse.s(),
)
pipeline.delay()

# Group: run tasks in parallel
parallel = group(
    send_welcome_email.s(user_id)
    for user_id in new_user_ids
)
parallel.delay()

# Chord: parallel tasks + callback when all complete
result = chord(
    group(process_chunk.s(chunk) for chunk in data_chunks),
    aggregate_results.s(),       # called with list of chunk results
)
result.delay()

Error Handling and Dead Letter Queue

# apps/core/tasks.py
from celery.signals import task_failure

@task_failure.connect
def on_task_failure(sender, task_id, exception, args, kwargs, traceback, einfo, **kw):
    """Log all task failures to Sentry / alerting."""
    import sentry_sdk
    with sentry_sdk.new_scope() as scope:
        scope.set_context('celery', {
            'task': sender.name,
            'task_id': task_id,
            'args': args,
            'kwargs': kwargs,
        })
        sentry_sdk.capture_exception(exception)
# Route failed tasks to dead-letter queue after max retries
@shared_task(
    bind=True,
    max_retries=3,
    name='payments.charge_card',
)
def charge_card(self, order_id: int) -> None:
    from apps.payments.models import Order, FailedCharge

    try:
        _do_charge(order_id)
    except Exception as exc:
        if self.request.retries >= self.max_retries:
            # Persist to dead-letter table for manual review
            FailedCharge.objects.create(
                order_id=order_id,
                error=str(exc),
                task_id=self.request.id,
            )
            return  # Don't raise — task is permanently failed
        raise self.retry(exc=exc)

Testing Celery Tasks

Unit Testing (No Broker)

# tests/test_tasks.py
import pytest
from unittest.mock import patch, MagicMock
from apps.notifications.tasks import send_welcome_email

class TestSendWelcomeEmail:

    @pytest.mark.django_db
    def test_sends_email_to_existing_user(self, user):
        with patch('apps.notifications.services.EmailService') as mock_email:
            send_welcome_email(user.pk)
            mock_email.send_welcome.assert_called_once_with(user)

    @pytest.mark.django_db
    def test_skips_missing_user_gracefully(self):
        """Should not raise when user is deleted between enqueue and execute."""
        send_welcome_email(99999)  # Non-existent user — must not raise

Integration Testing with CELERY_TASK_ALWAYS_EAGER

# config/settings/test.py
CELERY_TASK_ALWAYS_EAGER = True      # Run tasks synchronously in tests
CELERY_TASK_EAGER_PROPAGATES = True  # Re-raise exceptions from tasks

# tests/test_integration.py
@pytest.mark.django_db
def test_registration_triggers_welcome_email(client):
    with patch('apps.notifications.services.EmailService') as mock_email:
        response = client.post('/api/users/', {
            'email': 'new@example.com',
            'password': 'strongpass123',
        })

    assert response.status_code == 201
    mock_email.send_welcome.assert_called_once()

Testing Retries

@pytest.mark.django_db
def test_task_retries_on_connection_error():
    with patch('apps.crm.services.CRMClient.sync') as mock_sync:
        mock_sync.side_effect = ConnectionError('timeout')

        with pytest.raises(ConnectionError):
            sync_contact_to_crm.apply(args=[1], throw=True)

        assert mock_sync.call_count == 1  # First attempt only when eager

Monitoring

# Inspect active workers and queues
celery -A config inspect active
celery -A config inspect stats
celery -A config inspect reserved

# Check queue lengths (Redis)
redis-cli llen celery

# Flower: web-based real-time monitor
pip install flower
celery -A config flower --port=5555

Anti-Patterns

# BAD: Passing model instances — they may be stale by execution time
send_welcome_email.delay(user)        # Never pass ORM objects
send_welcome_email.delay(user.pk)     # Always pass PKs

# BAD: Calling tasks synchronously in production views
result = generate_report.apply()      # Blocks the request thread

# BAD: Non-idempotent task without guards
@shared_task
def charge_and_fulfill(order_id):
    order.charge()     # May charge twice if task retries!
    order.fulfill()

# GOOD: Idempotent with status guard
@shared_task
def charge_and_fulfill(order_id):
    order = Order.objects.select_for_update().get(pk=order_id)
    if order.status != Order.Status.PENDING:
        return  # Already processed
    order.charge()
    order.fulfill()

Production Checklist

CheckSetting
Worker restarts on crashsupervisord or systemd unit
CELERY_TASK_ACKS_LATE = TrueRe-queue tasks on worker crash
CELERY_WORKER_PREFETCH_MULTIPLIER = 1Fair distribution of long tasks
Separate queues per priority-Q default,high_priority,low_priority
CELERY_TASK_SOFT_TIME_LIMIT setGraceful timeout before hard kill
Sentry integrationCapture all task_failure signals
Flower or other monitorVisibility into queue depths
Beat runs on single node onlyPrevents duplicate scheduled task execution

Related Skills

  • django-patterns — ORM, service layer, and project structure
  • django-tdd — Testing Django models, views, and services
  • python-testing — pytest configuration and fixtures

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