Configuration is handled via the Q_CLUSTER dictionary in your

# example
    'name': 'myproject',
    'workers': 8,
    'recycle': 500,
    'timeout': 60,
    'compress': True,
    'save_limit': 250,
    'queue_limit': 500,
    'cpu_affinity': 1,
    'label': 'Django Q',
    'redis': {
        'host': '',
        'port': 6379,
        'db': 0, }

All configuration settings are optional:


Used to differentiate between projects using the same broker. On most broker types this will be used as the queue name. Defaults to 'default'.


Tasks are encrypted. When a worker encounters a task it can not decrypt, it will be discarded or failed.


The number of workers to use in the cluster. Defaults to CPU count of the current host, but can be set to a custom number. [1]


Set the daemon flag when spawning workers. You may need to disable this flag if your worker needs to spawn child process but be carefull with orphaned child processes in case of sudden termination of the main process. Defaults to True.


The number of tasks a worker will process before recycling . Useful to release memory resources on a regular basis. Defaults to 500.


The number of seconds a worker is allowed to spend on a task before it’s terminated. Defaults to None, meaning it will never time out. Set this to something that makes sense for your project. Can be overridden for individual tasks.


The number of seconds a broker will wait for a cluster to finish a task, before it’s presented again. Only works with brokers that support delivery receipts. Defaults to 60 seconds.


Compresses task packages to the broker. Useful for large payloads, but can add overhead when used with many small packages. Defaults to False


Limits the amount of successful tasks saved to Django.
  • Set to 0 for unlimited.
  • Set to -1 for no success storage at all.
  • Defaults to 250
  • Failures are always saved.


Guard loop sleep in seconds, must be greater than 0 and less than 60.


When set to True this configuration option forces all async() calls to be run with sync=True. Effectively making everything synchronous. Useful for testing. Defaults to False.


This does not limit the amount of tasks that can be queued on the broker, but rather how many tasks are kept in memory by a single cluster. Setting this to a reasonable number, can help balance the workload and the memory overhead of each individual cluster. Defaults to workers**2.


The label used for the Django Admin page. Defaults to 'Django Q'


The default behavior for schedules that didn’t run while a cluster was down, is to play catch up and execute all the missed time slots until things are back on schedule. You can override this behavior by setting catch_up to False. This will make those schedules run only once when the cluster starts and normal scheduling resumes. Defaults to True.


Connection settings for Redis. Defaults:

# redis defaults
    'redis': {
        'host': 'localhost',
        'port': 6379,
        'db': 0,
        'password': None,
        'socket_timeout': None,
        'charset': 'utf-8',
        'errors': 'strict',
        'unix_socket_path': None

For more information on these settings please refer to the Redis-py documentation


If you are already using django-redis for your caching, you can take advantage of its excellent connection backend by supplying the name of the cache connection you want to use instead of a direct Redis connection:

# example django-redis connection
    'name': 'DJRedis',
    'workers': 4,
    'timeout': 90,
    'django_redis': 'default'


Django Q uses your SECRET_KEY to encrypt task packages and prevent task crossover. So make sure you have it set up in your Django settings.


If you want to use Disque as your broker, set this to a list of available Disque nodes and each cluster will randomly try to connect to them:

# example disque connection
    'name': 'DisqueBroker',
    'workers': 4,
    'timeout': 60,
    'retry': 60,
    'disque_nodes': ['', '']

Django Q is also compatible with the Tynd Disque addon on Heroku:

# example Tynd Disque connection
import os

    'name': 'TyndBroker',
    'workers': 8,
    'timeout': 30,
    'retry': 60,
    'bulk': 10,
    'disque_nodes': os.environ['TYND_DISQUE_NODES'].split(','),
    'disque_auth': os.environ['TYND_DISQUE_AUTH']


Optional Disque password for servers that require authentication.


Connection settings for IronMQ:

# example IronMQ connection

    'name': 'IronBroker',
    'workers': 8,
    'timeout': 30,
    'retry': 60,
    'queue_limit': 50,
    'bulk': 10,
    'iron_mq': {
        'host': '',
        'token': 'Et1En7.....0LuW39Q',
        'project_id': '500f7b....b0f302e9'

All connection keywords are supported. See the iron-mq library for more info


To use Amazon SQS as a broker you need to provide the AWS region and credentials:

# example SQS broker connection

    'name': 'SQSExample',
    'workers': 4,
    'timeout': 60,
    'retry': 90,
    'queue_limit': 100,
    'bulk': 5,
    'sqs': {
        'aws_region': 'us-east-1',
        'aws_access_key_id': 'ac-Idr.....YwflZBaaxI',
        'aws_secret_access_key': '500f7b....b0f302e9'

Please make sure these credentials have proper SQS access.

Amazon SQS only supports a bulk setting between 1 and 10, with the total payload not exceeding 256kb.


If you want to use Django’s database backend as a message broker, set the orm keyword to the database connection you want it to use:

# example ORM broker connection

    'name': 'DjangORM',
    'workers': 4,
    'timeout': 90,
    'retry': 120,
    'queue_limit': 50,
    'bulk': 10,
    'orm': 'default'

Using the Django ORM backend will also enable the Queued Tasks table in the Admin.

If you need better performance , you should consider using a different database backend than the main project. Set orm to the name of that database connection and make sure you run migrations on it using the --database option.


To use MongoDB as a message broker you simply provide the connection information in a dictionary:

# example MongoDB broker connection

    'name': 'MongoDB',
    'workers': 8,
    'timeout': 60,
    'retry': 70,
    'queue_limit': 100,
    'mongo': {
            'host': '',
            'port': 27017

The mongo dictionary can contain any of the parameters exposed by pymongo’s MongoClient If you want to use a mongodb uri, you can supply it as the host parameter.


When using the MongoDB broker you can optionally provide a database name to use for the queues. Defaults to default database if available, otherwise django-q


You can use a custom broker class for your cluster workers:

# example Custom broker class connection

    'name': 'Custom',
    'workers': 8,
    'timeout': 60,
    'broker_class: ''

Make sure your CustomBroker class inherits from either the base Broker class or one of its children.


Sets the number of messages each cluster tries to get from the broker per call. Setting this on supported brokers can improve performance. Especially HTTP based or very high latency servers can benefit from bulk dequeue. Keep in mind however that settings this too high can degrade performance with multiple clusters or very large task packages.

Not supported by the default Redis broker. Defaults to 1.


Sets the queue polling interval for database brokers that don’t have a blocking call. Currently only affects the ORM and MongoDB brokers. Defaults to 0.2 (seconds).


For some brokers, you will need to set up the Django cache framework to gather statistics for the monitor. You can indicate which cache to use by setting this value. Defaults to default.


Switches all task and result functions from using the database backend to the cache backend. This is the same as setting the keyword cached=True on all task functions. Instead of a bool this can also be set to the number of seconds you want the cache to retain results. e.g. cached=60


You can disable the scheduler by setting this option to False. This will reduce a little overhead if you’re not using schedules, but is most useful if you want to temporarily disable all schedules. Defaults to True


You can redirect worker exceptions directly to your Rollbar dashboard by installing the python notifier with pip install rollbar and adding this configuration dictionary to your config:

# rollbar config
    'rollbar': {
        'access_token': '32we33a92a5224jiww8982',
        'environment': 'Django-Q'

Please check the Pyrollbar configuration reference for more options. Note that you will need a Rollbar account and access token to use this feature.


Sets the number of processor each worker can use. This does not affect auxiliary processes like the sentinel or monitor and is only useful for tweaking the performance of very high traffic clusters. The affinity number has to be higher than zero and less than the total number of processors to have any effect. Defaults to using all processors:

# processor affinity example.

4 processors, 4 workers, cpu_affinity: 1

worker 1 cpu [0]
worker 2 cpu [1]
worker 3 cpu [2]
worker 4 cpu [3]

4 processors, 4 workers, cpu_affinity: 2

worker 1 cpu [0, 1]
worker 2 cpu [2, 3]
worker 3 cpu [0, 1]
worker 4 cpu [2, 3]

8 processors, 8 workers, cpu_affinity: 3

worker 1 cpu [0, 1, 2]
worker 2 cpu [3, 4, 5]
worker 3 cpu [6, 7, 0]
worker 4 cpu [1, 2, 3]
worker 5 cpu [4, 5, 6]
worker 6 cpu [7, 0, 1]
worker 7 cpu [2, 3, 4]
worker 8 cpu [5, 6, 7]

In some cases, setting the cpu affinity for your workers can lead to performance improvements, especially if the load is high and consists of many repeating small tasks. Start with an affinity of 1 and work your way up. You will have to experiment with what works best for you. As a rule of thumb; cpu_affinity 1 favors repetitive short running tasks, while no affinity benefits longer running tasks.


The cpu_affinity setting requires the optional psutil module.

Psutil does not support cpu affinity on OS X at this time.


[1]Uses multiprocessing.cpu_count() which can fail on some platforms. If so , please set the worker count in the configuration manually or install psutil to provide an alternative cpu count method.