Examples

Emails

Sending an email can take a while so why not queue it:

# Welcome mail with follow up example
from datetime import timedelta
from django.utils import timezone
from django_q.tasks import async_task, schedule
from django_q.models import Schedule


def welcome_mail(user):
    msg = 'Welcome to our website'
    # send this message right away
    async_task('django.core.mail.send_mail',
            'Welcome',
            msg,
            'from@example.com',
            [user.email])
    # and this follow up email in one hour
    msg = 'Here are some tips to get you started...'
    schedule('django.core.mail.send_mail',
             'Follow up',
             msg,
             'from@example.com',
             [user.email],
             schedule_type=Schedule.ONCE,
             next_run=timezone.now() + timedelta(hours=1))

    # since the `repeats` defaults to -1
    # this schedule will erase itself after having run

Since you’re only telling Django Q to take care of the emails, you can quickly move on to serving web pages to your user.

Signals

A good place to use async tasks are Django’s model signals. You don’t want to delay the saving or creation of objects, but sometimes you want to trigger a lot of actions:

# Message on object change
from django.contrib.auth.models import User
from django.db.models.signals import pre_save
from django.dispatch import receiver
from django_q.tasks import async_task

# set up the pre_save signal for our user
@receiver(pre_save, sender=User)
def email_changed(sender, instance, **kwargs):
    try:
        user = sender.objects.get(pk=instance.pk)
    except sender.DoesNotExist:
        pass  # new user
    else:
        # has his email changed?
        if not user.email == instance.email:
            # tell everyone
            async_task('tasks.inform_everyone', instance)

The task will send a message to everyone else informing them that the users email address has changed. Note that this adds almost no overhead to the save action:

# tasks.py
def inform_everyone(user):
    mails = []
    for u in User.objects.exclude(pk=user.pk):
        msg = 'Dear {}, {} has a new email address: {}'
        msg = msg.format(u.username, user.username, user.email)
        mails.append(('New email', msg,
                      'from@example.com', [u.email]))
    return send_mass_mail(mails)
# or do it async again
def inform_everyone_async(user):
    for u in User.objects.exclude(pk=user.pk):
        msg = 'Dear {}, {} has a new email address: {}'
        msg = msg.format(u.username, user.username, user.email)
        async_task('django.core.mail.send_mail',
                'New email', msg, 'from@example.com', [u.email])

Of course you can do other things beside sending emails. These are just generic examples. You can use signals with async to update fields in other objects too. Let’s say this users email address is not just on the User object, but you stored it in some other places too without a reference. By attaching an async action to the save signal, you can now update that email address in those other places without impacting the the time it takes to return your views.

Reports

In this example the user requests a report and we let the cluster do the generating, while handling the result with a hook.

# Report generation with hook example
from django_q.tasks import async_task

# views.py
# user requests a report.
def create_report(request):
    async_task('tasks.create_html_report',
            request.user,
            hook='tasks.email_report')
# tasks.py
from django_q.tasks import async_task

# report generator
def create_html_report(user):
    html_report = 'We had a great quarter!'
    return html_report

# report mailer
def email_report(task):
    if task.success:
        # Email the report
        async_task('django.core.mail.send_mail',
                'The report you requested',
                task.result,
                'from@example.com',
                task.args[0].email)
    else:
        # Tell the admins something went wrong
        async_task('django.core.mail.mail_admins',
                'Report generation failed',
                task.result)

The hook is practical here, because it allows us to detach the sending task from the report generation function and to report on possible failures.

Haystack

If you use Haystack as your projects search engine, here’s an example of how you can have Django Q take care of your indexes in real time using model signals:

# Real time Haystack indexing
from .models import Document
from django.db.models.signals import post_save
from django.dispatch import receiver
from django_q.tasks import async_task

# hook up the post save handler
@receiver(post_save, sender=Document)
def document_changed(sender, instance, **kwargs):
    async_task('tasks.index_object', sender, instance, save=False)
    # turn off result saving to not flood your database
# tasks.py
from haystack import connection_router, connections

def index_object(sender, instance):
    # get possible backends
    backends = connection_router.for_write(instance=instance)

    for backend in backends:
        # get the index for this model
        index = connections[backend].get_unified_index()\
            .get_index(sender)
        # update it
        index.update_object(instance, using=backend)

Now every time a Document is saved, your indexes will be updated without causing a delay in your save action. You could expand this to dealing with deletes, by adding a post_delete signal and calling index.remove_object in the async_task function.

Shell

You can execute or schedule shell commands using Pythons subprocess module:

from django_q.tasks import async_task, result

# make a backup copy of setup.py
async_task('subprocess.call', ['cp', 'setup.py', 'setup.py.bak'])

# call ls -l and dump the output
task_id=async_task('subprocess.check_output', ['ls', '-l'])

# get the result
dir_list = result(task_id)

In Python 3.5 the subprocess module has changed quite a bit and returns a subprocess.CompletedProcess object instead:

from django_q.tasks import async_task, result

# make a backup copy of setup.py
tid = async_task('subprocess.run', ['cp', 'setup.py', 'setup.py.bak'])

# get the result
r=result(tid, 500)
# we can now look at the original arguments
>>> r.args
['cp', 'setup.py', 'setup.py.bak']
# and the returncode
>>> r.returncode
0

# to capture the output we'll need a pipe
from subprocess import PIPE

# call ls -l and pipe the output
tid = async_task('subprocess.run', ['ls', '-l'], stdout=PIPE)
# get the result
res = result(tid, 500)
# print the output
print(res.stdout)

Instead of async_task() you can of course also use schedule() to schedule commands.

For regular Django management commands, it is easier to call them directly:

from django_q.tasks import async_task, schedule

async_task('django.core.management.call_command','clearsessions')

# or clear those sessions every hour

schedule('django.core.management.call_command',
     'clearsessions',
     schedule_type='H')

Groups

A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog

# Group example with Parzen-window estimation
import numpy

from django_q.tasks import async_task, result_group, delete_group

# the estimation function
def parzen_estimation(x_samples, point_x, h):
    k_n = 0
    for row in x_samples:
        x_i = (point_x - row[:, numpy.newaxis]) / h
        for row in x_i:
            if numpy.abs(row) > (1 / 2):
                break
        else:
            k_n += 1
    return h, (k_n / len(x_samples)) / (h ** point_x.shape[1])

# create 100 calculations and return the collated result
def parzen_async():
    # clear the previous results
    delete_group('parzen', cached=True)
    mu_vec = numpy.array([0, 0])
    cov_mat = numpy.array([[1, 0], [0, 1]])
    sample = numpy.random. \
        multivariate_normal(mu_vec, cov_mat, 10000)
    widths = numpy.linspace(1.0, 1.2, 100)
    x = numpy.array([[0], [0]])
    # async_task them with a group label to the cache backend
    for w in widths:
        async_task(parzen_estimation, sample, x, w,
                group='parzen', cached=True)
    # return after 100 results
    return result_group('parzen', count=100, cached=True)

Django Q is not optimized for distributed computing, but this example will give you an idea of what you can do with task Groups.

Alternatively the parzen_async() function can also be written with async_iter(), which automatically utilizes the cache backend and groups to return a single result from an iterable:

# create 100 calculations and return the collated result
def parzen_async():
    mu_vec = numpy.array([0, 0])
    cov_mat = numpy.array([[1, 0], [0, 1]])
    sample = numpy.random. \
        multivariate_normal(mu_vec, cov_mat, 10000)
    widths = numpy.linspace(1.0, 1.2, 100)
    x = numpy.array([[0], [0]])
    # async_task them with async_task iterable
    args = [(sample, x, w) for w in widths]
    result_id = async_iter(parzen_estimation, args, cached=True)
    # return the cached result or timeout after 10 seconds
    return result(result_id, wait=10000, cached=True)

Note

If you have an example you want to share, please submit a pull request on github.