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`. - 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 `__ or you are using `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 `__ or `Circus `__ 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