Cluster

.. py:currentmodule:: django_q

Django Q uses Python's multiprocessing module to manage a pool of workers that will handle your tasks. Start your cluster using Django's manage.py command::

$ python manage.py qcluster

You should see the cluster starting ::

10:57:40 [Q] INFO Q Cluster-31781 starting.
10:57:40 [Q] INFO Process-1:1 ready for work at 31784
10:57:40 [Q] INFO Process-1:2 ready for work at 31785
10:57:40 [Q] INFO Process-1:3 ready for work at 31786
10:57:40 [Q] INFO Process-1:4 ready for work at 31787
10:57:40 [Q] INFO Process-1:5 ready for work at 31788
10:57:40 [Q] INFO Process-1:6 ready for work at 31789
10:57:40 [Q] INFO Process-1:7 ready for work at 31790
10:57:40 [Q] INFO Process-1:8 ready for work at 31791
10:57:40 [Q] INFO Process-1:9 monitoring at 31792
10:57:40 [Q] INFO Process-1 guarding cluster at 31783
10:57:40 [Q] INFO Process-1:10 pushing tasks at 31793
10:57:40 [Q] INFO Q Cluster-31781 running.

Stopping the cluster with ctrl-c or either the SIGTERM and SIGKILL signals, will initiate the :ref:stop_procedure::

16:44:12 [Q] INFO Q Cluster-31781 stopping.
16:44:12 [Q] INFO Process-1 stopping cluster processes
16:44:13 [Q] INFO Process-1:10 stopped pushing tasks
16:44:13 [Q] INFO Process-1:6 stopped doing work
16:44:13 [Q] INFO Process-1:4 stopped doing work
16:44:13 [Q] INFO Process-1:1 stopped doing work
16:44:13 [Q] INFO Process-1:5 stopped doing work
16:44:13 [Q] INFO Process-1:7 stopped doing work
16:44:13 [Q] INFO Process-1:3 stopped doing work
16:44:13 [Q] INFO Process-1:8 stopped doing work
16:44:13 [Q] INFO Process-1:2 stopped doing work
16:44:14 [Q] INFO Process-1:9 stopped monitoring results
16:44:15 [Q] INFO Q Cluster-31781 has stopped.

The number of workers, optional timeouts, recycles and cpu_affinity can be controlled via the :doc:configure settings.

Multiple Clusters

You can have multiple clusters on multiple machines, working on the same queue as long as:

They connect to the same :doc:broker<brokers>.
They use the same cluster name. See :doc:configure
They share the same SECRET_KEY for Django.

Using a Procfile

If you host on Heroku <https://heroku.com>__ or you are using Honcho <https://github.com/nickstenning/honcho>__ you can start the cluster from a :file:Procfile with an entry like this::

worker: python manage.py qcluster

Process managers

While you certainly can run a Django Q with a process manager like Supervisor <http://supervisord.org/>__ or Circus <https://circus.readthedocs.org/en/latest/>__ it is not strictly necessary. The cluster has an internal sentinel that checks the health of all the processes and recycles or reincarnates according to your settings or in case of unexpected crashes. Because of the multiprocessing daemonic nature of the cluster, it is impossible for a process manager to determine the clusters health and resource usage.

An example :file:circus.ini ::

[circus]
check_delay = 5
endpoint = tcp://127.0.0.1:5555
pubsub_endpoint = tcp://127.0.0.1:5556
stats_endpoint = tcp://127.0.0.1:5557

[watcher:django_q]
cmd = python manage.py qcluster
numprocesses = 1
copy_env = True

Note that we only start one process. It is not a good idea to run multiple instances of the cluster in the same environment since this does nothing to increase performance and in all likelihood will diminish it. Control your cluster using the workers, recycle and timeout settings in your :doc:configure

An example :file:supervisor.conf ::

[program:django-q]
command = python manage.py qcluster
stopasgroup = true

Supervisor's stopasgroup will ensure that the single process doesn't leave orphan process on stop or restart.

Reference

.. py:class:: Cluster

.. py:method:: start

Spawns a cluster and then returns

.. py:method:: stop

Initiates :ref:`stop_procedure` and waits for it to finish.

.. py:method:: stat

returns a :class:`Stat` object with the current cluster status.

.. py:attribute:: pid

The cluster process id.

.. py:attribute:: host

The current hostname

.. py:attribute:: sentinel

returns the :class:`multiprocessing.Process` containing the :ref:`sentinel`.

.. py:attribute:: timeout

The clusters timeout setting in seconds

.. py:attribute:: start_event

A :class:`multiprocessing.Event` indicating if the :ref:`sentinel` has finished starting the cluster

.. py:attribute:: stop_event

A :class:`multiprocessing.Event` used to instruct the :ref:`sentinel` to initiate the :ref:`stop_procedure`

.. py:attribute:: is_starting

Bool. Indicating that the cluster is busy starting up

.. py:attribute:: is_running

Bool. Tells you if the cluster is up and running.

.. py:attribute:: is_stopping

Bool. Shows that the stop procedure has been started.

.. py:attribute:: has_stopped

Bool. Tells you if the cluster has finished the stop procedure