Tasks¶
Async¶
Use async()
from your code to quickly offload tasks to the Cluster
:
from django_q.tasks import async, result
# create the task
async('math.copysign', 2, -2)
# or with import and storing the id
import math.copysign
task_id = async(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('math.modf', 2.5, hook='hooks.print_result')
# hooks.py
def print_result(task):
print(task.result)
async()
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.
save¶
Overrides the result backend’s save setting for this task.
timeout¶
Overrides the cluster’s timeout 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.
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('math.modf', 2.5, q_options=opts)
Please not 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 Q to run in synchronous mode for testing using the sync option.
Async Iterable¶
If you have an iterable object with arguments for a function, you can use async_iter()
to async them with a single command:
# Async Iterable example
from django_q.tasks import async_iter, result
# set up a list of arguments for math.floor
iter = [i for i in range(100)]
# async iter them
id=async_iter('math.floor',iter)
# wait for the collated result for 1 second
result_list = result(id, wait=1000)
This will individually queue 100 tasks to the worker cluster, which will save their results in the cache backend for speed. Once all the 100 results are in the cache, they are collated into a list and saved as a single result in the database. The cache results are then cleared. Needs the Django cache framework.
Groups¶
You can group together results by passing async()
the optional group
keyword:
# result group example
from django_q.tasks import async, result_group
for i in range(4):
async('math.modf', i, group='modf')
# wait until the group has 4 results
result = result_group('modf', count=4)
print(result)
[(0.0, 0.0), (0.0, 1.0), (0.0, 2.0), (0.0, 3.0)]
Note that the same can be achieved much faster with async_iter()
Take care to not limit your results database too much and call delete_group()
before each run, unless you want your results to keep adding up.
Instead of result_group()
you can also use fetch_group()
to return a queryset of Task
objects.:
# fetch group example
from django_q.tasks import fetch_group, count_group, result_group
# count the number of failures
failure_count = count_group('modf', failures=True)
# only use the successes
results = fetch_group('modf')
if failure_count:
results = results.exclude(success=False)
results = [task.result for task in successes]
# this is the same as
results = fetch_group('modf', failures=False)
results = [task.result for task in successes]
# and the same as
results = result_group('modf') # filters failures by default
Getting results by using result_group()
is of course much faster than using fetch_group()
, but it doesn’t offer the benefits of Django’s queryset functions.
Note
Calling Queryset.values
for the result on Django 1.7 or lower will return a list of encoded results.
If you can’t upgrade to Django 1.8, use list comprehension or an iterator to return decoded results.
You can also access group functions from a task result instance:
from django_q.tasks import fetch
task = fetch('winter-speaker-alpha-ceiling')
if task.group_count() > 100:
print(task.group_result())
task.group_delete()
print('Deleted group {}'.format(task.group))
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, result
# cache the result for 10 seconds
id = async('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, 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('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()
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, fetch
# create a synchronous task
task_id = async('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()
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
you can also configure sync as a global override.
Connection pooling¶
Django Q 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()
a lot though, it can help to set up a broker to reuse for async()
:
# broker connection economy example
from django_q.tasks import async
from django_q.brokers import get_broker
broker = get_broker()
for i in range(50):
async('math.modf', 2.5, broker=broker)
Tip
If you are using django-redis and the redis broker, you can configure Django Q to use its connection pool.
Reference¶
-
async
(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
- timeout (int) – Overrides global cluster timeout.
- save (bool) – Overrides global save setting for this task.
- sync (bool) – If set to True, async 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
-
async_iter
(func, args_iter, **kwargs)¶ Runs iterable arguments against the cache backend and returns a single collated result. Accepts the same options as
async()
excepthook
.Parameters: Returns: The uuid of the task
Return type:
-
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: int
-
result_group
(group_id, failures=False, wait=0, count=None, cached=False)¶ Returns the results of a task group
Parameters: Returns: a list of results
Return type:
-
fetch_group
(group_id, failures=True, wait=0, count=None, cached=False)¶ Returns a list of tasks in a group
Parameters: Returns: a list of
Task
Return type:
-
count_group
(group_id, failures=False, cached=False)¶ Counts the number of task results in a group.
Parameters: Returns: the number of tasks or failures in a group
Return type:
-
delete_group
(group_id, tasks=False, cached=False)¶ Deletes a group label from the database.
Parameters: Returns: the numbers of tasks affected
Return type:
-
delete_cached
(task_id, broker=None)¶ Deletes a task from the cache backend
Parameters: - task_id – 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
.