# 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.

### cluster

The name of the cluster. Only useful if you are using [alternative queues](https://django-q2.readthedocs.io/en/master/cluster.html#multiple-queues).

### 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, cluster=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.

    - 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()`

    - cluster – Optional cluster name if using alternative queues

    - 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. -1 for indefinite, but be sure the result will not be None otherwise it will wait indefinitely!

    - 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:

    - task_id (str) – the uuid or name of the task

    - wait (int) – optional milliseconds to wait for a result. -1 for indefinite

    - 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

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

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 on `Task.success` is `True`.

class Failure

  A proxy model of `Task` with the queryset filtered on `Task.success` is `False`.

class AsyncTask(func, *args, **kwargs)

  A class wrapper for the `async_task()` function.

  Parameters:

    - func (object) – The task function to execute

    - args (tuple) – The arguments for the task function

    - kwargs (dict) – Keyword arguments for the task function, including async_task options

  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