django-celery
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---
name: django-celery
description: Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canv…
category: design
runtime: Node.js / Python
---
# django-celery output preview
## PART A: Task fit
- Use case: 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. Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Activate / Project Setup / Installation” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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. Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “When to Activate / Project Setup / Installation” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “When to Activate / Project Setup / Installation”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: django-celery
description: Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canv…
category: design
source: tomevault-io/skills-registry
---
# django-celery
## When to use
- Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitori…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Activate / Project Setup / Installation” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "django-celery" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Activate / Project Setup / Installation
rules -> SKILL.md triggers / order / output contract
runtime -> Node.js / Python | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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
| Check | Setting |
|---|---|
| Worker restarts on crash | supervisord or systemd unit |
CELERY_TASK_ACKS_LATE = True |
Re-queue tasks on worker crash |
CELERY_WORKER_PREFETCH_MULTIPLIER = 1 |
Fair distribution of long tasks |
| Separate queues per priority | -Q default,high_priority,low_priority |
CELERY_TASK_SOFT_TIME_LIMIT set |
Graceful timeout before hard kill |
| Sentry integration | Capture all task_failure signals |
| Flower or other monitor | Visibility into queue depths |
| Beat runs on single node only | Prevents duplicate scheduled task execution |
Related Skills
django-patterns— ORM, service layer, and project structuredjango-tdd— Testing Django models, views, and servicespython-testing— pytest configuration and fixtures
Source: affaan-m/everything-claude-code — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review