Practical Celery

CAMERON MASKE
twitter: @cameronmaske
email: cam@trackmaven.com
web: http://cameronmaske.com

WHAT IS CELERY?
HOW DOES IT WORK?

USING CELERY, BEST
PRACTICES AND SCALING.

CELERY
ASYNCHRONOUS
DISTRIBUTED
TASK QUEUE

OUT OF THE
REQUEST/RESPONSE
CYCLE.
Example: Sending emails asynchronously.

TASKS IN THE
BACKGROUND.
Example: Computational heavy jobs.
Example: Interacting with external APIs.

HISTORY
Python.
Released (0.1) in 2009.
Currently on 3.1, with 3.2 in alpha.
Developed by Ask Solem (@asksol)

PRODUCER
Produces a task for the queue.

BROKER
Stores the task backlog
Answers, what work remains to be done?
RabbitMQ, Redis, SQLAlchemy, Django's ORM, MongoDB...

WORKER
Execute and consumes tasks.
Distributed.

RESULTS BACKEND.
Stores the results from our tasks.
Redis, Redis, SQLAlchemy, Django's ORM, MongoDB...
Optional!

from celery import Celery
app = Celery('tasks', backend='amqp', broker='amqp://guest@localhost//')
@app.task
def add(x, y):
return x + y

>>> result = add.delay(4, 4)
>>> result.state
'SUCCESS'
>>> result.id
'4cc7438e-afd4-4f8f-a2f3-f46567e7ca77'
>>> result.get()
8
http://celery.readthedocs.org/en/latest/reference/celery.result.html

PICK YOUR FLAVOR.
@app.task
def add(x, y):
return x + y
add(2, 4)
class AddTask(app.Task):
def run(self, x, y):
return x + y
AddTask().run(2, 4)

# Async
add.delay(2, 4)
add.apply_aync(args=(2, 4), expires=30)
# Eager!
result = add.apply(args=(2, 4)) # Executes locally.
# Or...
add(2, 4) # Does not return a celery result!

http://docs.celeryproject.org/en/master/django/first-steps-with-django.
html
- project/
- config/__init__.py
- config/settings.py
- config/urls.py
- manage.py

# project/config/celery.py
from __future__ import absolute_import
import os
from celery import Celery
from django.conf import settings
# Set the default Django settings module for the 'celery' program.
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
app = Celery('app')
# Using a string here means the worker will not have to
# pickle the object when using Windows.
app.config_from_object('django.conf:settings')
app.autodiscover_tasks(lambda: settings.INSTALLED_APPS)
@app.task(bind=True)
def debug_task(self):
print('Request: {0!r}'.format(self.request))

# project/config/__init__.py
from __future__ import absolute_import
# This will make sure the app is always imported when
# Django starts so that shared_task will use this app.
from .celery import app as celery_app
__all__ = ['celery_app']

celery -A project worker -l info

TESTING
# settings.py
import sys
if 'test' in sys.argv:
CELERY_EAGER_PROPAGATES_EXCEPTIONS=True,
CELERY_ALWAYS_EAGER=True,
BROKER_BACKEND='memory'

PATTERNS
AND BEST
PRACTICES.

NEVER PASS OBJECTS AS
ARGUMENTS.

# Bad
@app.task()
def send_reminder(reminder):
reminder.send_email()
# Good
@app.task()
def send_reminder(pk):
try:
reminder = Reminder.objects.get(pk=pk)
except Reminder.DoesNotExist:
return
reminder.send_email()

KEEP TASKS GRANUAL.
CAN PROCESS MORE IN
PARALLEL.

AVOID LAUNCHING
SYNCHRONOUS
SUBTASKS

# Bad
@app.task
def update_page_info(url):
page = fetch_page.delay(url).get()
info = parse_page.delay(url, page).get()
store_page_info.delay(url, info)
@app.task
def fetch_page(url):
return myhttplib.get(url)
@app.task
def parse_page(url, page):
return myparser.parse_document(page)
@app.task
def store_page_info(url, info):
return PageInfo.objects.create(url, info)

# Good
def update_page_info(url):
chain = fetch_page.s() | parse_page.s() | store_page_info.s(url)
chain()
@app.task()
def fetch_page(url):
return myhttplib.get(url)
@app.task()
def parse_page(page):
return myparser.parse_document(page)
@app.task(ignore_result=True)
def store_page_info(info, url):
PageInfo.objects.create(url=url, info=info)
http://celery.readthedocs.org/en/latest/userguide/canvas.html

PERIODIC TASKS.
http://celery.readthedocs.org/en/latest/userguide/periodic-tasks.
html

from datetime import timedelta
@app.periodic_task(run_every=timedelta(minutes=5)):
def run_every_five():
pass

class RunEveryFive(app.PeriodicTask):
run_every = timedelta(minutes=5)
def run(self):
pass

@app.task():
def run_every_five():
pass
CELERYBEAT_SCHEDULE = {
'run-every-five': {
'task': 'tasks.run_every_five',
'schedule': timedelta(seconds=30)
},
}

CRON STYLE.
from celery.schedules import crontab
crontab(minute=0, hour='*/3') # Every 3 hours.
crontab(day_of_week='sunday') # Every minute on Sundays.
crontab(0, 0, 0, month_of_year='*/3') # First month of every quarter.

@app.periodic_task(run_every=crontab(minute=0, hour=1))
def schedule_emails():
user_ids = User.objects.values_list('id', flat=True)
for user_id in user_ids:
send_daily_email.delay(user_id)
@app.task()
def send_daily_email(user_id):
user = User.objects.get(id=user_id)
try:
today = datetime.now()
Email.objects.get(
user=user, date__year=today.year, date__month=today.month, date__day=today.day)
except Email.DoesNotExist:
email = Email(user=user, body="Hey, don't forget to LOGIN PLEASE!")
email.send()
email.save()

CELERY BEAT A.K.A THE
SCHEDULER.
celery -A project beat

NEVER RUN A BEAT +
WORKER ON A SINGLE
CELERY PROCESS.
# Really bad idea....
celery -A project worker -B

FREQUENTLY RUNNING
PERIODIC TASKS.
BEWARE OF "TASK STACKING"

Schedule task runs every 5 minutes.
Tasks take 30 minutes.
Schedule task stacks.
Bad stuff.

EXPIRES!
from time import sleep
@app.periodic_task(expires=5*60, run_every=timedelta(minutes=5))
def schedule_task():
for _ in range(30):
one_minute_task.delay()
@app.task(expires=5*60)
def one_minute_task():
sleep(60)

from celery.exceptions import Retry
@app.task(max_retries=10)
def gather_data():
try:
data = api.get_data()
# etc, etc, ...
except api.RateLimited as e:
raise Retry(exc=e, when=e.cooldown)
except api.IsDown:
return

class DebugTask(app.Task):
def after_return(self, status, retval, task_id, args, kwargs, einfo):
print("I'm done!")
def on_failure(self, exc, task_id, args, kwargs, einfo):
print("I failed :(")
def on_retry(self, exc, task_id, args, kwargs, einfo):
print("I'll try again!")
def on_success(self, retval, task_id, args, kwargs):
print("I did it!")

ABSTRACT
class AbstractTask(app.Task):
abstract = True
def after_return(self, *args, **kwargs):
print("All done!")
@app.task(base=AbstractTask)
def add(x, y):
return x + y

INSTANTIATION
class DatabaseTask(app.Task):
abstract = True
_db = None
@property
def db(self):
if self._db is None:
self._db = Database.connect()
return self._db

ENSURE A TASK IS
EXECUTED ONE AT A TIME

from celery import task
from celery.utils.log import get_task_logger
from django.core.cache import cache
from django.utils.hashcompat import md5_constructor as md5
from djangofeeds.models import Feed
logger = get_task_logger(__name__)
LOCK_EXPIRE = 60 * 5 # Lock expires in 5 minutes
@task
def import_feed(feed_url):
# The cache key consists of the task name and the MD5 digest
# of the feed URL.
feed_url_digest = md5(feed_url).hexdigest()
lock_id = '{0}-lock-{1}'.format(self.name, feed_url_hexdigest)
# cache.add fails if if the key already exists
acquire_lock = lambda: cache.add(lock_id, 'true', LOCK_EXPIRE)
# memcache delete is very slow, but we have to use it to take
# advantage of using add() for atomic locking
release_lock = lambda: cache.delete(lock_id)
logger.debug('Importing feed: %s', feed_url)
if acquire_lock():
try:
feed = Feed.objects.import_feed(feed_url)
finally:
release_lock()
return feed.url
logger.debug(
'Feed %s is already being imported by another worker', feed_url)

# settings.py
CELERY_IGNORE_RESULT = True
CELERYD_TASK_SOFT_TIME_LIMIT = 500
CELERYD_TASK_TIME_LIMIT = 1000

# tasks.py
app.task(ignore_result=True, soft_time_limit=60, time_limit=120)
def add(x, y):
pass

# settings.py
CELERYD_MAX_TASKS_PER_CHILD = 500
CELERYD_PREFETCH_MULTIPLIER = 4

SO MANY
CHOICES!
RabbitMQ
Redis
SQLAlchemy
Django's ORM
MongoDB
Amazon SQS
CouchDB
Beanstalk
IronMQ

DJANGO ORM.
# settings.py
BROKER_URL = 'django://'
INSTALLED_APPS = (
'kombu.transport.django',
) CELERY_RESULT_BACKEND='djcelery.backends.database:DatabaseBackend'
python manage.py syncdb

DON'T DO THIS FOR
ANYTHING SERIOUS.

C OPTIMIZED LIBRARY
$ pip install librabbitmq

CONCURRENCY
celery -A project worker -C 10
celery -A project worker --autoscale=10,1

INCREASED CONCURRENCY CAN
QUICKLY DRAIN CONNECTIONS ON
YOUR DATABASE
Use a connection pooler (pgbouncer).

CELERY_ROUTES = {
'email.tasks.send_mail': {
'queue': 'priority',
},
}
# or
send_mail.apply_async(queue="priority")
celery -A project worker -Q email

Make tasks faster.
Reduce volume of tasks.

RABBITMQ MANGEMENT PLUGIN
HAS A GREAT HTTP API!

Practical Celery

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