Django¶
Huey comes with special integration for use with the Django framework. The integration provides:
- Configuration of huey via the Django settings module.
- Running the consumer as a Django management command.
- Auto-discovery of
tasks.py
modules to simplify task importing. - Properly manage database connections.
Supported Django versions are the officially supported at https://www.djangoproject.com/download/#supported-versions
Setting things up¶
To use huey with Django, the first step is to add an entry to your project’s
settings.INSTALLED_APPS
:
# settings.py
# ...
INSTALLED_APPS = (
# ...
'huey.contrib.djhuey', # Add this to the list.
# ...
)
The above is the bare minimum needed to start using huey’s Django integration. If you like, though, you can also configure both Huey and the consumer using the settings module.
Note
Huey settings are optional. If not provided, Huey will default to using Redis running on localhost:6379 (standard setup).
Configuration is kept in settings.HUEY
, which can be either a dictionary or
a Huey
instance. Here is an example that shows all of the supported
options with their default values:
# settings.py
HUEY = {
'name': settings.DATABASES['default']['NAME'], # Use db name for huey.
'result_store': True, # Store return values of tasks.
'events': True, # Consumer emits events allowing real-time monitoring.
'store_none': False, # If a task returns None, do not save to results.
'always_eager': settings.DEBUG, # If DEBUG=True, run synchronously.
'store_errors': True, # Store error info if task throws exception.
'blocking': False, # Poll the queue rather than do blocking pop.
'backend_class': 'huey.RedisHuey', # Use path to redis huey by default,
'connection': {
'host': 'localhost',
'port': 6379,
'db': 0,
'connection_pool': None, # Definitely you should use pooling!
# ... tons of other options, see redis-py for details.
# huey-specific connection parameters.
'read_timeout': 1, # If not polling (blocking pop), use timeout.
'max_errors': 1000, # Only store the 1000 most recent errors.
'url': None, # Allow Redis config via a DSN.
},
'consumer': {
'workers': 1,
'worker_type': 'thread',
'initial_delay': 0.1, # Smallest polling interval, same as -d.
'backoff': 1.15, # Exponential backoff using this rate, -b.
'max_delay': 10.0, # Max possible polling interval, -m.
'utc': True, # Treat ETAs and schedules as UTC datetimes.
'scheduler_interval': 1, # Check schedule every second, -s.
'periodic': True, # Enable crontab feature.
'check_worker_health': True, # Enable worker health checks.
'health_check_interval': 1, # Check worker health every second.
},
}
Alternatively, you can simply set settings.HUEY
to a Huey
instance and do your configuration directly. In the example below, I’ve also
shown how you can create a connection pool:
# settings.py -- alternative configuration method
from huey import RedisHuey
from redis import ConnectionPool
pool = ConnectionPool(host='my.redis.host', port=6379, max_connections=20)
HUEY = RedisHuey('my-app', connection_pool=pool)
Running the Consumer¶
To run the consumer, use the run_huey
management command. This command
will automatically import any modules in your INSTALLED_APPS
named
tasks.py. The consumer can be configured using both the django settings
module and/or by specifying options from the command-line.
Note
Options specified on the command line take precedence over those specified in the settings module.
To start the consumer, you simply run:
$ ./manage.py run_huey
In addition to the HUEY.consumer
setting dictionary, the management command
supports all the same options as the standalone consumer. These options are
listed and described in the Options for the consumer
section.
For quick reference, the most important command-line options are briefly listed here.
-w
,--workers
- Number of worker threads/processes/greenlets. Default is 1, but most applications should use at least 2.
-k
,--worker-type
- Worker type, must be “thread”, “process” or “greenlet”. The default is thread, which provides good all-around performance. For CPU-intensive workloads, process is likely to be more performant. The greenlet worker type is suited for IO-heavy workloads. When using greenlet you can specify tens or hundreds of workers since they are extremely lightweight compared to threads/processes. See note below on using gevent/greenlet.
Note
Due to a conflict with Django’s base option list, the “verbose” option is
set using -V
or --huey-verbose
. When enabled, huey logs at the
DEBUG level.
For more information, read the Options for the consumer section.
Using gevent¶
When using worker type greenlet, it’s necessary to apply a monkey-patch
before any libraries or system modules are imported. Gevent monkey-patches
things like socket
to provide non-blocking I/O, and if those modules are
loaded before the patch is applied, then the resulting code will execute
synchronously.
Unfortunately, because of Django’s design, the only way to reliably apply this
patch is to create a custom bootstrap script that mimics the functionality of
manage.py
. Here is the patched manage.py
code:
#!/usr/bin/env python
import os
import sys
# Apply monkey-patch if we are running the huey consumer.
if 'run_huey' in sys.argv:
from gevent import monkey
monkey.patch_all()
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "conf")
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv)
How to create tasks¶
The task()
and periodic_task()
decorators can be
imported from the huey.contrib.djhuey
module. Here is how you might define
two tasks:
from huey import crontab
from huey.contrib.djhuey import periodic_task, task
@task()
def count_beans(number):
print('-- counted %s beans --' % number)
return 'Counted %s beans' % number
@periodic_task(crontab(minute='*/5'))
def every_five_mins():
print('Every five minutes this will be printed by the consumer')
Tasks that execute queries¶
If you plan on executing queries inside your task, it is a good idea to close
the connection once your task finishes. To make this easier, huey provides a
special decorator to use in place of task
and periodic_task
which will
automatically close the connection for you.
from huey import crontab
from huey.contrib.djhuey import db_periodic_task, db_task
@db_task()
def do_some_queries():
# This task executes queries. Once the task finishes, the connection
# will be closed.
@db_periodic_task(crontab(minute='*/5'))
def every_five_mins():
# This is a periodic task that executes queries.
DEBUG and Synchronous Execution¶
When settings.DEBUG = True
, tasks will be executed synchronously just like
regular function calls. The purpose of this is to avoid running both Redis and
an additional consumer process while developing or running tests. If, however,
you would like to enqueue tasks regardless of whether DEBUG = True
, then
explicitly specify always_eager=False
in your huey settings:
# settings.py
HUEY = {
'name': 'my-app',
# Other settings ...
'always_eager': False,
}
Configuration Examples¶
This section contains example HUEY
configurations.
# Redis running locally with four worker threads.
HUEY = {
'name': 'my-app',
'consumer': {'workers': 4, 'worker_type': 'thread'},
}
# Redis on network host with 64 worker greenlets and connection pool
# supporting up to 100 connections.
from redis import ConnectionPool
pool = ConnectionPool(
host='192.168.1.123',
port=6379,
max_connections=100)
HUEY = {
'name': 'my-app',
'connection': {'connection_pool': pool},
'consumer': {'workers': 64, 'worker_type': 'greenlet'},
}
It is also possible to specify the connection using a Redis URL, making it easy to configure this setting using a single environment variable:
HUEY = {
'name': 'my-app',
'url': os.environ.get('REDIS_URL', 'redis://localhost:6379/?db=1')
}
Alternatively, you can just assign a Huey
instance to the HUEY
setting:
from huey import RedisHuey
HUEY = RedisHuey('my-app')