Configuration

Configuration is handled via the Q_CLUSTER dictionary in your settings.py

# settings.py example
Q_CLUSTER = {
    'name': 'myproject',
    'workers': 8,
    'recycle': 500,
    'timeout': 60,
    'compress': True,
    'save_limit': 250,
    'queue_limit': 500,
    'cpu_affinity': 1,
    'label': 'Django Q',
    'redis': {
        'host': '127.0.0.1',
        'port': 6379,
        'db': 0, }
}

All configuration settings are optional:

name

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

Note

Tasks are signed. When a worker encounters a task with an invalid signature, it will be discarded or failed.

workers

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]

daemonize_workers

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

recycle

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

max_rss

The maximum resident set size in kilobytes before a worker will recycle and release resources. Useful for limiting memory usage. Only supported on platforms that implement the python resource module or install the psutil module. Defaults to None.

timeout

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.

See retry for details how to set values for timeout and retry.

ack_failures

When set to True, also acknowledge unsuccessful tasks. This causes failed tasks to be considered as successful deliveries, thereby removing them from the task queue. Can also be set per-task by passing the ack_failure option to async_task(). Defaults to False.

max_attempts

Limit the number of retry attempts for failed tasks. Set to 0 for infinite retries. Defaults to 0

retry

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.

The value must be bigger than the time it takes to complete longest task, i.e. timeout must be less than retry value and all tasks must complete in less time than the selected retry time. If this does not hold, i.e. the retry value is less than timeout or less than it takes to finish a task, Django-Q will start the task again if the used broker supports receipts.

For example, with the following code

# settings.py
Q_CLUSTER = {
   'retry': 5
   'workers': 4,
   'orm': 'default',
}

# example.py

from django_q.tasks import async_task

async_task('time.sleep', 22)

First, time.sleep is called by the first worker. After 5 seconds second worker will also call time.sleep because retry time has exceeded and the broker return the task again for the cluster. After 21 seconds from the call to async_task all four workers are running the time.sleep(22) call and there is one retry in queue; tasks are started after 0, 5, 10, 15 and 20 seconds after the async_task was called. After 22 seconds the first worker completes and the task is acknowledged in the broker and the task is not added to task queue anymore but the task that was already in the run queue will run also. So in this example, time.sleep was called 5 times.

Note also that the above issue might cause all workers to run the same long running task preventing new tasks from starting shortly after the task has been started by async_task. In this case the retry time handling could cause the task that has not been started by any worker to be put on work queue again (even multiple times).

compress

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

save_limit

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_cycle

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

sync

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

queue_limit

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.

label

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

catch_up

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.

redis

Connection settings for Redis. Defaults:

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

It’s also possible to use a Redis connection URI:

Q_CLUSTER = {
    'redis': 'redis://h:asdfqwer1234asdf@ec2-111-1-1-1.compute-1.amazonaws.com:111'
}

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

django_redis

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
Q_CLUSTER = {
    'name': 'DJRedis',
    'workers': 4,
    'timeout': 90,
    'django_redis': 'default'
}

Tip

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

disque_nodes

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
Q_CLUSTER = {
    'name': 'DisqueBroker',
    'workers': 4,
    'timeout': 60,
    'retry': 60,
    'disque_nodes': ['127.0.0.1:7711', '127.0.0.1:7712']
}

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

# example Tynd Disque connection
import os

Q_CLUSTER = {
    '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']
}

disque_auth

Optional Disque password for servers that require authentication.

iron_mq

Connection settings for IronMQ:

# example IronMQ connection

Q_CLUSTER = {
    'name': 'IronBroker',
    'workers': 8,
    'timeout': 30,
    'retry': 60,
    'queue_limit': 50,
    'bulk': 10,
    'iron_mq': {
        'host': 'mq-aws-us-east-1.iron.io',
        'token': 'Et1En7.....0LuW39Q',
        'project_id': '500f7b....b0f302e9'
    }
}

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

sqs

To use Amazon SQS as a broker you need to provide the AWS region and credentials either via the config, or any other boto3 configuration method:

# example SQS broker connection

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

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.

orm

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

Q_CLUSTER = {
    '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.

mongo

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

# example MongoDB broker connection

Q_CLUSTER = {
    'name': 'MongoDB',
    'workers': 8,
    'timeout': 60,
    'retry': 70,
    'queue_limit': 100,
    'mongo': {
            'host': '127.0.0.1',
            '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.

mongo_db

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

broker_class

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

# example Custom broker class connection

Q_CLUSTER = {
    'name': 'Custom',
    'workers': 8,
    'timeout': 60,
    'broker_class: 'myapp.broker.CustomBroker'
}

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

bulk

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.

poll

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).

cache

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.

cached

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

scheduler

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

error_reporter

You can redirect worker exceptions directly to various error reporters (for example Rollbar or Sentry) by installing Django Q with the necessary extras.

To enable installed error reporters, you must provide the configuration settings required by an error reporter extension:

# error_reporter config--rollbar example
Q_CLUSTER = {
    'error_reporter': {
        'rollbar': {
            'access_token': '32we33a92a5224jiww8982',
            'environment': 'Django-Q'
        }
    }
}

For more information on error reporters and developing error reporting plugins for Django Q, see errors.

cpu_affinity

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.

Note

The cpu_affinity setting requires the optional psutil module.

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

Footnotes

[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.