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 import async, schedule, Schedule

def welcome_mail(user):
    msg = 'Welcome to our website'
    # send this message right away
    # and this follow up email in one hour
    msg = 'Here are some tips to get you started...'
             'Follow up',
             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.


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 import async

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


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 import async

# views.py
# user requests a report.
def create_report(request):
# tasks.py
from django_q import async

# 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
              'The report you requested',
        # Tell the admins something went wrong
              'Report generation failed',

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


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 import async

# hook up the post save handler
@receiver(post_save, sender=Document)
def document_changed(sender, instance, **kwargs):
    async('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()\
        # 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 function.


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 import async, result_group,\
    count_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):
            k_n += 1
    return h, (k_n / len(x_samples)) / (h ** point_x.shape[1])

# create 100 calculations and send them to the cluster
def parzen_async():
    # clear the previous results
    delete_group('parzen', tasks=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 them with a group label and a hook
    for w in widths:
        async(parzen_estimation, sample, x, w,
         group='parzen', hook=parzen_hook)

# wait for 100 results to return and print it.
def parzen_hook(task):
    if count_group('parzen') == 100:

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


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