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.

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.

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.

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.

You can also use an Iter instance which can sometimes be more convenient:

from django_q.tasks import Iter

i = Iter('math.copysign')

# add some arguments
i.append(1, -1)
i.append(2, -1)
i.append(3, -1)

# run it
i.run()

# get the results
print(i.result())

[-1.0, -2.0, -3.0]

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))

Chains¶

Sometimes you want to run tasks sequentially. For that you can use the async_chain() function:

# Async a chain of tasks
from django_q.tasks import async_chain, result_group

# the chain must be in the format
# [(func,(args),{kwargs}),(func,(args),{kwargs}),..]
group_id = async_chain([('math.copysign', (1, -1)),
                        ('math.floor', (1,))])

# get group result
result_group(group_id, count=2)

A slightly more convenient way is to use a Chain instance:

# Chain async
from django_q.tasks import Chain

# create a chain that uses the cache backend
chain = Chain(cached=True)

# add some tasks
chain.append('math.copysign', 1, -1)
chain.append('math.floor', 1)

# run it
chain.run()

print(chain.result())

[-1.0, 1]

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:	str

result(task_id, wait=0, cached=False)¶

Gets the result of a previously executed task

Parameters:	task_id (str) – the uuid or name of the task wait (int) – optional milliseconds to wait for a result cached (bool) – run this against the cache backend.
Returns:	The result of the executed task

fetch(task_id, wait=0, cached=False)¶

Returns a previously executed task

Parameters:	name (str) – the uuid or name of the task wait (int) – optional milliseconds to wait for a result cached (bool) – run this against the cache backend.
Returns:	A task object
Return type:	Task

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() except hook. See also the Iter class.

Parameters:	func (object) – The task function to execute args – An iterable containing arguments for the task function kwargs (dict) – Keyword arguments for the task function. Ignores `hook`.
Returns:	The uuid of the task
Return type:	str

async_chain(chain, group=None, cached=Conf.CACHED, sync=Conf.SYNC, broker=None)¶

Async a chain of tasks. See also the Chain class.

Parameters:	chain (list) – a list of tasks in the format [(func,(args),{kwargs}), (func,(args),{kwargs})] group (str) – an optional group name. cached (bool) – run this against the cache backend sync (bool) – execute this inline instead of asynchronous

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:	group_id (str) – the group identifier failures (bool) – set this to `True` to include failed results wait (int) – optional milliseconds to wait for a result or count count (int) – block until there are this many results in the group cached (bool) – run this against the cache backend
Returns:	a list of results
Return type:	list

fetch_group(group_id, failures=True, wait=0, count=None, cached=False)¶

Returns a list of tasks in a group

Parameters:	group_id (str) – the group identifier failures (bool) – set this to `False` to exclude failed tasks wait (int) – optional milliseconds to wait for a task or count count (int) – block until there are this many tasks in the group cached (bool) – run this against the cache backend.
Returns:	a list of `Task`
Return type:	list

count_group(group_id, failures=False, cached=False)¶

Counts the number of task results in a group.

Parameters:	group_id (str) – the group identifier failures (bool) – counts the number of failures if `True` cached (bool) – run this against the cache backend.
Returns:	the number of tasks or failures in a group
Return type:	int

delete_group(group_id, tasks=False, cached=False)¶

Deletes a group label from the database.

Parameters:	group_id (str) – the group identifier tasks (bool) – also deletes the associated tasks if `True` cached (bool) – run this against the cache backend.
Returns:	the numbers of tasks affected
Return type:	int

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 on Task.success is True.

class Failure¶: A proxy model of Task with the queryset filtered on Task.success is False.

class Iter(func=None, args=None, kwargs=None, cached=Conf.CACHED, sync=Conf.SYNC, broker=None)¶

An async task with iterable arguments. Serves as a convenient wrapper for async_iter() You can pass the iterable arguments at construction or you can append individual argument tuples.

param func: the function to execute

param args: an iterable of arguments.

param kwargs: the keyword arguments

param bool cached:

run this against the cache backend

param bool sync:

execute this inline instead of asynchronous

param broker: optional broker instance

append(*args)¶

Append arguments to the iter set. Returns the current set count.

param args: the arguments for a single execution

return: the current set count

rtype: int

run()¶

Start queueing the tasks to the worker cluster.

return: the task result id

result(wait=0)¶

return the full list of results.

param int wait: how many milliseconds to wait for a result

return: an unsorted list of results

fetch(wait=0)¶

get the task result objects.

param int wait: how many milliseconds to wait for a result

return: an unsorted list of task objects

length()¶

get the length of the arguments list

return int: length of the argument list

class Chain(chain=None, group=None, cached=Conf.CACHED, sync=Conf.SYNC)¶

A sequential chain of tasks. Acts as a convenient wrapper for async_chain() You can pass the task chain at construction or you can append individual tasks before running them.

param list chain:

a list of task in the format [(func,(args),{kwargs}), (func,(args),{kwargs})]

param str group:

an optional group name.

param bool cached:

run this against the cache backend

param bool sync:

execute this inline instead of asynchronous

append(func, *args, **kwargs)¶

Append a task to the chain. Takes the same arguments as async()

return: the current number of tasks in the chain

rtype: int

run()¶

Start queueing the chain to the worker cluster.

return: the chains group id

result(wait=0)¶

return the full list of results from the chain when it finishes. Blocks until timeout or result.

param int wait: how many milliseconds to wait for a result

return: an unsorted list of results

fetch(failures=True, wait=0)¶

get the task result objects from the chain when it finishes. Blocks until timeout or result.

param failures: include failed tasks

param int wait: how many milliseconds to wait for a result

return: an unsorted list of task objects

current()¶

get the index of the currently executing chain element

return int: current chain index

length()¶

get the length of the chain

return int: length of the chain