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

$ python 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 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 Configuration settings.

Multiple Clusters

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

  • They connect to the same broker.
  • They use the same cluster name. See Configuration
  • 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 Procfile with an entry like this:

worker: python 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 circus.ini

check_delay = 5
endpoint = tcp://
pubsub_endpoint = tcp://
stats_endpoint = tcp://

cmd = python 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 Configuration

An example supervisor.conf

command = python qcluster
stopasgroup = true

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


class Cluster

Spawns a cluster and then returns


Initiates Stop procedure and waits for it to finish.


returns a Stat object with the current cluster status.


The cluster process id.


The current hostname


returns the multiprocessing.Process containing the Sentinel.


The clusters timeout setting in seconds


A multiprocessing.Event indicating if the Sentinel has finished starting the cluster


A multiprocessing.Event used to instruct the Sentinel to initiate the Stop procedure


Bool. Indicating that the cluster is busy starting up


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


Bool. Shows that the stop procedure has been started.


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