Processing long-running Django tasks using Celery + RabbitMQ + Supervisord + Monit

6 minute read

For a recent project I’m working on we had a requirement to be able to process long-running tasks in Django triggered by user interaction on the front-end. For instance, the user would click a button on the web page in order to trigger the back-end to, for example, build a CSV file containing a subset of the data in the database. In the browser a little popup window would get displayed, showing the progress of the task in the back-end. Once the task got completed in the back-end the user would be notified in the front-end of its completion and provided a link to download the final output (e.g. the built CSV file).

For a requirement like this, you don’t really want to be performing such a long-running task as part of the standard Django request -> response cycle since 1) the Django server is single-threaded, and 2) even if you’re using WSGI to run your Django app behind Apache or a similar server you’re not really going to want one of your Apache worker processes being tied up with a single client request for long periods of time as this would adversely impact other incoming client requests. Thus, any such long-running task should be performed in a process which then informs the main Django app once it’s done. Celery is a readily-available such system (a task-queue to be precise) which enables this and it is easy to integrate into Django using django-celery.

Setting up Django Celery

Setting up Django Celery has already been documented elsewhere so I’ll simply list the settings I used to get things working (Note: I’m assuming that you’re running a Debian-based Linux system). First of all I installed RabbitMQ to use the message queue system:

$ sudo apt-get install rabbitmq-server

Then I added a vhost and username and password for my Django app to RabbitMQ:

$ sudo rabbitmqctl add_user myapp myapp
$ sudo rabbitmqctl add_vhost myapp
$ sudo rabbitmqctl set_permissions -p myapp myapp ".*" ".*" ".*"

Then in my celeryconfig.py I set the following:

BROKER_HOST = "127.0.0.1"
BROKER_PORT = 5672 # default RabbitMQ listening port
BROKER_USER = "myapp"
BROKER_PASSWORD = "myapp"
BROKER_VHOST = "myapp"
CELERY_BACKEND = "amqp" # telling Celery to report the results back to RabbitMQ
CELERY_RESULT_DBURI = ""

To test that my setup was correct I ran:

$ ./manage.py celeryd -l INFO

[2012-01-27 12:29:01,344: WARNING/MainProcess]
/home/ram/dev/myapp/virtualenv/lib/python2.7/site-packages/djcelery/loaders.py:86: UserWarning: Using settings.DEBUG leads to a memory leak, never use this setting in production environments!
warnings.warn("Using settings.DEBUG leads to a memory leak, never "
[2012-01-27 12:29:01,344: WARNING/MainProcess]

-------------- [email protected] v2.4.6
---- **** -----
--- * *** * -- [Configuration]
-- * - **** --- . broker: amqp:[email protected]:5672//
- ** ---------- . loader: djcelery.loaders.DjangoLoader
- ** ---------- . logfile: [stderr]@INFO
- ** ---------- . concurrency: 8
- ** ---------- . events: OFF
- *** --- * --- . beat: OFF
-- ******* ----
--- ***** ----- [Queues]
-------------- . celery: exchange:celery (direct) binding:celery

[Tasks]
. REPORT_CREATE

[2012-01-27 12:29:01,399: INFO/PoolWorker-1] child process calling self.run()
[2012-01-27 12:29:01,401: INFO/PoolWorker-2] child process calling self.run()
[2012-01-27 12:29:01,403: INFO/PoolWorker-3] child process calling self.run()
[2012-01-27 12:29:01,405: INFO/PoolWorker-4] child process calling self.run()
[2012-01-27 12:29:01,406: INFO/PoolWorker-5] child process calling self.run()
[2012-01-27 12:29:01,408: INFO/PoolWorker-6] child process calling self.run()
[2012-01-27 12:29:01,409: INFO/PoolWorker-7] child process calling self.run()
[2012-01-27 12:29:01,410: INFO/PoolWorker-8] child process calling self.run()
[2012-01-27 12:29:01,411: WARNING/MainProcess] [email protected] has started.

At this point if you’re not familiar with writing Celery tasks then check out their tutorial on how to write Celery tasks for use by Celery daemon workers started above.

Deploying to production using Supervisord

In the production environment we need a reliable way of running the Celery daemon processes. Enter Supervisord. Essentially it’s a processes which in turn launches other processes you tell it to launch, and then monitors those child processes, restarting them if they die, etc. Here is what I did to set it up:

$ sudo easy_install supervisor

I created /etc/supervisord to hold all the configuration info:

$ sudo mkdir /etc/supervisord

I then edited /etc/supervisord/supervisord.conf:

[unix_http_server]
file=/tmp/supervisor.sock ; (the path to the socket file)

[supervisord]
logfile=/var/log/supervisord/main.log ; (main log file;default $CWD/supervisord.log)
logfile_maxbytes=50MB ; (max main logfile bytes b4 rotation;default 50MB)
logfile_backups=10 ; (num of main logfile rotation backups;default 10)
loglevel=info ; (log level;default info; others: debug,warn,trace)
pidfile=/tmp/supervisord.pid ; (supervisord pidfile;default supervisord.pid)
nodaemon=false ; (start in foreground if true;default false)
minfds=1024 ; (min. avail startup file descriptors;default 1024)
minprocs=200 ; (min. avail process descriptors;default 200)
childlogdir=/var/log/supervisord ; ('AUTO' child log dir, default $TEMP)

[rpcinterface:supervisor]
supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface

[supervisorctl]
serverurl=unix:///tmp/supervisor.sock ; use a unix:// URL for a unix socket

[include]
files = /etc/supervisord/conf.d/*.conf

I created a folder called conf.d inside /etc/supervisord and created a file called myapp-celery.conf inside that:

[program:myapp-celery]
command=/home/ram/dev/myapp/virtualenv/bin/python /home/ram/dev/myapp/manage.py celeryd --loglevel=INFO
environment=PYTHONPATH=/home/ram/dev/myapp
directory=/home/ram/dev/myapp
user=www-data
numprocs=1
stdout_logfile=/var/log/celeryd/myapp.log
stderr_logfile=/var/log/celeryd/myapp.log
autostart=true
autorestart=true
startsecs=10
stopwaitsecs = 600
priority=998

Note: I’m running my Django app inside a virtual environment, which is why I specify the path to the Python interpreter in the above file.

I created the /var/log/supervisord and /var/log/celeryd log folders and setup the appropriate permissions on them to enable logging.

To run Supervisord I did the following:

$ supervisord -c /etc/supervisord/supervisord.conf

I checked that my Celery workers were active:

$ ps aux | grep celeryd
...
www-data 26655 0.5 0.3 210000 36248 ? Sl 12:49 0:02 /home/ram/dev/myapp/virtualenv/bin/python /home/ram/dev/myapp/manage.py celeryd --loglevel=INFO
www-data 26656 0.5 0.3 210012 36232 ? Sl 12:49 0:02 /home/ram/dev/myapp/virtualenv/bin/python /home/ram/dev/myapp/manage.py celeryd --loglevel=INFO
www-data 26671 0.0 0.2 103364 33012 ? S 12:49 0:00 /home/ram/dev/myapp/virtualenv/bin/python /home/ram/dev/myapp/manage.py celeryd --loglevel=INFO
...

To shut it down I did:

$ supervisorctl -c /etc/supervisord/supervisord.conf shutdown

Monitoring Supervisord

Supervisord will monitor our Celery workers and ensure they stay alive, but how will we ensure that Supervisord itself stays alive? Enter Monit. Monit does essentially the same thing as Supervisord except that it monitors child processes through pid files and other checks (e.g. checking that a web page loads in order to verify that Apache is running) and doesn’t directly own them (unlike Supervisord). Monit also installs itself as an init.d script which gets launched at server boot time. It comes with a web interface (with optional authentication) which lets you easily start, stop and restart any services it is monitoring along with providing memory and CPU usage statistics.

I installed Monit:

$ sudo apt-get install monit

I edited /etc/monit/monitrc and uncommented the lines relating to the web interface:

set httpd port 2812 and
# use address localhost # only accept connection from localhost
# allow localhost # allow localhost to connect to the server and
# allow admin:monit # require user 'admin' with password 'monit'
allow @monit # allow users of group 'monit' to connect (rw)
# allow @users readonly # allow users of group 'users' to connect readonly

Note: I configured it above such that it accepts authenticated connections from anywhere, whereby only local Linux users who belongs to the monit group can gain access..

Then I created /etc/monit/conf.d/supervisord.monit:

check process supervisord with pidfile /tmp/supervisord.pid
group supervisord
start program = "/usr/local/bin/supervisord -c /etc/supervisord/supervisord.conf"
stop program = "/usr/local/bin/supervisorctl -c /etc/supervisord/supervisord.conf shutdown"
if 5 restarts within 5 cycles then timeout

Restart Monit:

$ sudo /etc/init.d/monit restart

All done! I then created a monit Linux group and added myself to it. From then on I was able to visit http://localhost:2812 to see the status of the supervisord process and control it.

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