How to run a celery worker with Django app scalable by AWS Elastic Beanstalk?

Question

How to use Django with AWS Elastic Beanstalk that would also run tasks by celery on main node only?

Answer 1

This is how I set up celery with django on elastic beanstalk with scalability working fine.

Please keep in mind that 'leader_only' option for container_commands works only on environment rebuild or deployment of the App. If service works long enough, leader node may be removed by Elastic Beanstalk. To deal with that, you may have to apply instance protection for your leader node. Check: http://docs.aws.amazon.com/autoscaling/latest/userguide/as-instance-termination.html#instance-protection-instance

Add bash script for celery worker and beat configuration.

Add file root_folder/.ebextensions/files/celery_configuration.txt:

#!/usr/bin/env bash

# Get django environment variables
celeryenv=`cat /opt/python/current/env | tr '\n' ',' | sed 's/export //g' | sed 's/$PATH/%(ENV_PATH)s/g' | sed 's/$PYTHONPATH//g' | sed 's/$LD_LIBRARY_PATH//g' | sed 's/%/%%/g'`
celeryenv=${celeryenv%?}

# Create celery configuraiton script
celeryconf="[program:celeryd-worker]
; Set full path to celery program if using virtualenv
command=/opt/python/run/venv/bin/celery worker -A django_app --loglevel=INFO

directory=/opt/python/current/app
user=nobody
numprocs=1
stdout_logfile=/var/log/celery-worker.log
stderr_logfile=/var/log/celery-worker.log
autostart=true
autorestart=true
startsecs=10

; Need to wait for currently executing tasks to finish at shutdown.
; Increase this if you have very long running tasks.
stopwaitsecs = 600

; When resorting to send SIGKILL to the program to terminate it
; send SIGKILL to its whole process group instead,
; taking care of its children as well.
killasgroup=true

; if rabbitmq is supervised, set its priority higher
; so it starts first
priority=998

environment=$celeryenv

[program:celeryd-beat]
; Set full path to celery program if using virtualenv
command=/opt/python/run/venv/bin/celery beat -A django_app --loglevel=INFO --workdir=/tmp -S django --pidfile /tmp/celerybeat.pid

directory=/opt/python/current/app
user=nobody
numprocs=1
stdout_logfile=/var/log/celery-beat.log
stderr_logfile=/var/log/celery-beat.log
autostart=true
autorestart=true
startsecs=10

; Need to wait for currently executing tasks to finish at shutdown.
; Increase this if you have very long running tasks.
stopwaitsecs = 600

; When resorting to send SIGKILL to the program to terminate it
; send SIGKILL to its whole process group instead,
; taking care of its children as well.
killasgroup=true

; if rabbitmq is supervised, set its priority higher
; so it starts first
priority=998

environment=$celeryenv"

# Create the celery supervisord conf script
echo "$celeryconf" | tee /opt/python/etc/celery.conf

# Add configuration script to supervisord conf (if not there already)
if ! grep -Fxq "[include]" /opt/python/etc/supervisord.conf
  then
  echo "[include]" | tee -a /opt/python/etc/supervisord.conf
  echo "files: celery.conf" | tee -a /opt/python/etc/supervisord.conf
fi

# Reread the supervisord config
supervisorctl -c /opt/python/etc/supervisord.conf reread

# Update supervisord in cache without restarting all services
supervisorctl -c /opt/python/etc/supervisord.conf update

# Start/Restart celeryd through supervisord
supervisorctl -c /opt/python/etc/supervisord.conf restart celeryd-beat
supervisorctl -c /opt/python/etc/supervisord.conf restart celeryd-worker

Take care about script execution during deployment, but only on main node (leader_only: true). Add file root_folder/.ebextensions/02-python.config:

container_commands:
  04_celery_tasks:
    command: "cat .ebextensions/files/celery_configuration.txt > /opt/elasticbeanstalk/hooks/appdeploy/post/run_supervised_celeryd.sh && chmod 744 /opt/elasticbeanstalk/hooks/appdeploy/post/run_supervised_celeryd.sh"
    leader_only: true
  05_celery_tasks_run:
    command: "/opt/elasticbeanstalk/hooks/appdeploy/post/run_supervised_celeryd.sh"
    leader_only: true

• Beat is configurable without need of redeployment, with separate django applications: https://pypi.python.org/pypi/django_celery_beat.
• Storing task results is good idea to: https://pypi.python.org/pypi/django_celery_beat

File requirements.txt

celery==4.0.0
django_celery_beat==1.0.1
django_celery_results==1.0.1
pycurl==7.43.0 --global-option="--with-nss"

Configure celery for Amazon SQS broker (Get your desired endpoint from list: http://docs.aws.amazon.com/general/latest/gr/rande.html) root_folder/django_app/settings.py:

...
CELERY_RESULT_BACKEND = 'django-db'
CELERY_BROKER_URL = 'sqs://%s:%s@' % (aws_access_key_id, aws_secret_access_key)
# Due to error on lib region N Virginia is used temporarily. please set it on Ireland "eu-west-1" after fix.
CELERY_BROKER_TRANSPORT_OPTIONS = {
    "region": "eu-west-1",
    'queue_name_prefix': 'django_app-%s-' % os.environ.get('APP_ENV', 'dev'),
    'visibility_timeout': 360,
    'polling_interval': 1
}
...

Celery configuration for django django_app app

Add file root_folder/django_app/celery.py:

from __future__ import absolute_import, unicode_literals
import os
from celery import Celery

# set the default Django settings module for the 'celery' program.
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'django_app.settings')

app = Celery('django_app')

# Using a string here means the worker don't have to serialize
# the configuration object to child processes.
# - namespace='CELERY' means all celery-related configuration keys
#   should have a `CELERY_` prefix.
app.config_from_object('django.conf:settings', namespace='CELERY')

# Load task modules from all registered Django app configs.
app.autodiscover_tasks()

Modify file root_folder/django_app/__init__.py:

from __future__ import absolute_import, unicode_literals

# This will make sure the app is always imported when
# Django starts so that shared_task will use this app.
from django_app.celery import app as celery_app

__all__ = ['celery_app']

Check also:

• How do you run a worker with AWS Elastic Beanstalk? (solution without scalability)
• Pip Requirements.txt --global-option causing installation errors with other packages. "option not recognized" (solution for problems coming from obsolate pip on elastic beanstalk that cannto deal with global options for properly solving pycurl dependency)