Tasks
async_task()
Use async_task()
from your code to quickly offload tasks to the Cluster
:
from django_q.tasks import async_task, result
# create the task
async_task('math.copysign', 2, -2)
# or with import and storing the id
import math.copysign
task_id = async_task(copysign, 2, -2)
# get the result
task_result = result(task_id)
# result returns None if the task has not been executed yet
# you can wait for it
task_result = result(task_id, 200)
# but in most cases you will want to use a hook:
async_task('math.modf', 2.5, hook='hooks.print_result')
# hooks.py
def print_result(task):
print(task.result)
async_task()
can take the following optional keyword arguments:
hook
The function to call after the task has been executed. This function gets passed the complete Task
object as its argument.
group
A group label. Check Groups for group functions.
save
Overrides the result backend’s save setting for this task.
timeout
Overrides the cluster’s timeout setting for this task.
See retry for details how to set values for timeout.
ack_failure
Overrides the cluster’s ack_failures setting for this task.
sync
Simulates a task execution synchronously. Useful for testing. Can also be forced globally via the sync configuration option.
cached
Redirects the result to the cache backend instead of the database if set to True
or to an integer indicating the cache timeout in seconds.
e.g. cached=60
. Especially useful with large and group operations where you don’t need the all results in your
database and want to take advantage of the speed of your cache backend.
broker
A broker instance, in case you want to control your own connections.
task_name
Optionally overwrites the auto-generated task name.
q_options
None of the option keywords get passed on to the task function.
As an alternative you can also put them in
a single keyword dict named q_options
. This enables you to use these keywords for your function call:
# Async options in a dict
opts = {'hook': 'hooks.print_result',
'group': 'math',
'timeout': 30}
async_task('math.modf', 2.5, q_options=opts)
Please note that this will override any other option keywords.
Note
For tasks to be processed you will need to have a worker cluster running in the background using python manage.py qcluster
or you need to configure Django Q2 to run in synchronous mode for testing using the sync option.
AsyncTask
Optionally you can use the AsyncTask
class to instantiate a task and keep everything in a single object.:
# AsyncTask class instance example
from django_q.tasks import AsyncTask
# instantiate an async task
a = AsyncTask('math.floor', 1.5, group='math')
# you can set or change keywords afterwards
a.cached = True
# run it
a.run()
# wait indefinitely for the result and print it
# don't let the task return `None` or it will wait indefinitely
print(a.result(wait=-1))
# change the args
a.args = (2.5,)
# run it again
a.run()
# wait max 10 seconds for the result and print it
print(a.result(wait=10))
1
2
Once you change any of the parameters of the task after it has run, the result is invalidated and you will have to AsyncTask.run()
it again to retrieve a new result.
Cached operations
You can run your tasks results against the Django cache backend instead of the database backend by either using the global cached setting or by supplying the cached
keyword to individual functions.
This can be useful if you are not interested in persistent results or if you run large group tasks where you only want the final result.
By using a cache backend like Redis or Memcached you can speed up access to your task results significantly compared to a relational database.
When you set cached=True
, results will be saved permanently in the cache and you will have to rely on your backend’s cleanup strategies (like LRU) to
manage stale results.
You can also opt to set a manual timeout on the results, by setting e.g. cached=60
. Meaning the result will be evicted from the cache after 60 seconds.
This works both globally or on individual async executions.:
# simple cached example
from django_q.tasks import async_task, result
# cache the result for 10 seconds
id = async_task('math.floor', 100, cached=10)
# wait max 50ms for the result to appear in the cache
result(id, wait=50, cached=True)
# or fetch the task object
task = fetch(id, cached=True)
# and then save it to the database
task.save()
As you can see you can easily turn a cached result into a permanent database result by calling save()
on it.
This also works for group actions:
# cached group example
from django_q.tasks import async_task, result_group
from django_q.brokers import get_broker
# set up a broker instance for better performance
broker = get_broker()
# Async a hundred functions under a group label
for i in range(100):
async_task('math.frexp',
i,
group='frexp',
cached=True,
broker=broker)
# wait max 50ms for one hundred results to return
result_group('frexp', wait=50, count=100, cached=True)
If you don’t need hooks, that exact same result can be achieved by using the more convenient async_iter()
.
Synchronous testing
async_task()
can be instructed to execute a task immediately by setting the optional keyword sync=True
.
The task will then be injected straight into a worker and the result saved by a monitor instance:
from django_q.tasks import async_task, fetch
# create a synchronous task
task_id = async_task('my.buggy.code', sync=True)
# the task will then be available immediately
task = fetch(task_id)
# and can be examined
if not task.success:
print('An error occurred: {}'.format(task.result))
An error occurred: ImportError("No module named 'my'",)
Note that async_task()
will block until the task is executed and saved. This feature bypasses the broker and is intended for debugging and development.
Instead of setting sync
on each individual async_task
you can also configure sync as a global override.
Connection pooling
Django Q2 tries to pass broker instances around its parts as much as possible to save you from running out of connections.
When you are making individual calls to async_task()
a lot though, it can help to set up a broker to reuse for async_task()
:
# broker connection economy example
from django_q.tasks import async_task
from django_q.brokers import get_broker
broker = get_broker()
for i in range(50):
async_task('math.modf', 2.5, broker=broker)
Tip
If you are using django-redis and the redis broker, you can configure Django Q2 to use its connection pool.
Reference
- async_task(func, *args, hook=None, group=None, timeout=None, save=None, sync=False, cached=False, broker=None, q_options=None, **kwargs)
Puts a task in the cluster queue
- Parameters
func (object) – The task function to execute
args (tuple) – The arguments for the task function
hook (object) – Optional function to call after execution
group (str) – An optional group identifier
save (bool) – Overrides global save setting for this task.
ack_failure (bool) – Overrides the global ack_failures setting for this task.
sync (bool) – If set to True, async_task will simulate a task execution
cached – Output the result to the cache backend. Bool or timeout in seconds
broker – Optional broker connection from
brokers.get_broker()
q_options (dict) – Options dict, overrides option keywords
kwargs (dict) – Keyword arguments for the task function
- Returns
The uuid of the task
- Return type
- result(task_id, wait=0, cached=False)
Gets the result of a previously executed task
- Parameters
- Returns
The result of the executed task
- fetch(task_id, wait=0, cached=False)
Returns a previously executed task
- Parameters
- Returns
A task object
- Return type
Changed in version 0.2.0.
Renamed from get_task
- queue_size()
Returns the size of the broker queue. Note that this does not count tasks currently being processed.
- Returns
The amount of task packages in the broker
- Return type
- delete_cached(task_id, broker=None)
Deletes a task from the cache backend
- Parameters
task_id (str) – the uuid of the task
broker – an optional broker instance
- class Task
Database model describing an executed task
- id
An
uuid.uuid4()
identifier- name
The name of the task as a humanized version of the
id
Note
This is for convenience and can be used as a parameter for most functions that take a task_id. Keep in mind that it is not guaranteed to be unique if you store very large amounts of tasks in the database.
- func
The function or reference that was executed
- hook
The function to call after execution.
- args
Positional arguments for the function.
- kwargs
Keyword arguments for the function.
- result
The result object. Contains the error if any occur.
- started
The moment the task was created by an async command
- stopped
The moment a worker finished this task
- success
Was the task executed without problems?
- time_taken()
Calculates the difference in seconds between started and stopped.
Note
Time taken represents the time a task spends in the cluster, this includes any time it may have waited in the queue.
- group_result(failures=False)
Returns a list of results from this task’s group. Set failures to
True
to include failed results.- group_count(failures=False)
Returns a count of the number of task results in this task’s group. Returns the number of failures when
failures=True
- group_delete(tasks=False)
Resets the group label on all the tasks in this task’s group. If
tasks=True
it will also delete the tasks in this group from the database, including itself.- classmethod get_result(task_id)
Gets a result directly by task uuid or name.
- classmethod get_result_group(group_id, failures=False)
Returns a list of results from a task group. Set failures to
True
to include failed results.- classmethod get_task(task_id)
Fetches a single task object by uuid or name.
- classmethod get_task_group(group_id, failures=True)
Gets a queryset of tasks with this group id. Set failures to
False
to exclude failed tasks.- classmethod get_group_count(group_id, failures=False)
Returns a count of the number of tasks results in a group. Returns the number of failures when
failures=True
- classmethod delete_group(group_id, objects=False)
Deletes a group label only, by default. If
objects=True
it will also delete the tasks in this group from the database.
- class Success
A proxy model of
Task
with the queryset filtered onTask.success
isTrue
.
- class Failure
A proxy model of
Task
with the queryset filtered onTask.success
isFalse
.
- class AsyncTask(func, *args, **kwargs)
A class wrapper for the
async_task()
function.- Parameters
- id
The task unique identifier. This will only be available after it has been
run()
- started
Bool indicating if the task has been run with the current parameters
- func
The task function to execute
- args
A tuple of arguments for the task function
- kwargs
Keyword arguments for the function. Can include any of the optional async_task keyword attributes directly or in a q_options dictionary.
- broker
Optional
Broker
instance to use- sync
Run this task inline instead of asynchronous.
- save
Overrides the global save setting.
- hook
Optional function to call after a result is available. Takes the result
Task
as the first argument.- group
Optional group identifier
- cached
Run the task against the cache result backend.
- run()
Send the task to a worker cluster for execution
- result(wait=0)
The task result. Always returns None if the task hasn’t been run with the current parameters.
- param int wait
the number of milliseconds to wait for a result. -1 for indefinite
- fetch(wait=0)
Returns the full
Task
result instance.- param int wait
the number of milliseconds to wait for a result. -1 for indefinite
- result_group(failures=False, wait=0, count=None)
Returns a list of results from this task’s group.
- param bool failures
set this to
True
to include failed results- param int wait
optional milliseconds to wait for a result or count. -1 for indefinite
- param int count
block until there are this many results in the group
- fetch_group(failures=True, wait=0, count=None)
Returns a list of task results from this task’s group
- param bool failures
set this to
False
to exclude failed tasks- param int wait
optional milliseconds to wait for a task or count. -1 for indefinite
- param int count
block until there are this many tasks in the group