The cluster monitor shows live information about all the Q clusters connected to your project.

Start the monitor with Django’s command:

$ python qmonitor



Shows the hostname of the server this cluster is running on.


The cluster Id. Same as the cluster process ID or pid.


Current state of the cluster:

  • Starting The cluster is spawning workers and getting ready.
  • Idle Everything is ok, but there are no tasks to process.
  • Working Processing tasks like a good cluster should.
  • Stopping The cluster does not take on any new tasks and is finishing.
  • Stopped All tasks have been processed and the cluster is shutting down.


The current number of workers in the cluster pool.


Task Queue counts the number of tasks in the queue [1]

If this keeps rising it means you are taking on more tasks than your cluster can handle. You can limit this by settings the queue_limit in your cluster configuration, after which it will turn green when that limit has been reached. If your task queue is always hitting its limit and your running out of resources, it may be time to add another cluster.


Result Queue shows the number of results in the queue. [1]

Since results are only saved by a single process which has to access the database. It’s normal for the result queue to take slightly longer to clear than the task queue.


Reincarnations shows the amount of processes that have been reincarnated after a recycle, sudden death or timeout. If this number is unusually high, you are either suffering from repeated task errors or severe timeouts and you should check your logs for details.


Uptime the amount of time that has passed since the cluster was started.

Press q to quit the monitor and return to your terminal.


If you just want to see a one-off summary of your cluster stats you can use the qinfo management command:

$ python qinfo

All stats are summed over all available clusters.

Task rate is calculated over the last 24 hours and will show the number of tasks per second, minute, hour or day depending on the amount. Average execution time (Avg time) is calculated in seconds over the last 24 hours.

Since some of these numbers are based on what is available in your tasks database, limiting or disabling the result backend will skew them.


You can check the status of your clusters straight from your code with the Stat class:

from django_q.monitor import Stat

for stat in Stat.get_all():
    print(stat.cluster_id, stat.status)

# or if you know the cluster id
cluster_id = 1234
stat = Stat.get(cluster_id)
print(stat.status, stat.workers)


class Stat

Cluster status object.


Id of this cluster. Corresponds with the process id.


Time Of Birth


Shows the number of seconds passed since the time of birth


The number of times the sentinel had to start a new worker process.


String representing the current cluster status.


The number of tasks currently in the task queue. [1]


The number of tasks currently in the result queue. [1]


The pid of the pusher process


The pid of the monitor process


The pid of the sentinel process


A list of process ids of the workers currently in the cluster pool.


Returns true or false depending on any tasks still present in the task or result queue.

classmethod get(cluster_id, r=redis_client)

Gets the current Stat for the cluster id. Takes an optional redis connection.

classmethod get_all(r=redis_client)

Returns a list of Stat objects for all active clusters. Takes an optional redis connection.


[1](1, 2, 3, 4) Uses multiprocessing.Queue.qsize() which is not implemented on OS X and always returns 0.