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A basic Celery on Heroku guide

An example project and a basic guide showing how to run Django/Celery on Heroku

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A basic Celery on Heroku guide

This guide starts right where the "Getting Started with Django on Heroku" ends. It's assumed you have a basic and empty Django project. As a result, we'll have a basic Django/Celery site that enqueues immediate and periodic tasks.

Basic preparations

First of all, let's actually install Celery and add it to our requirements list (as we had just started, let's just overwrite requirements.txt here):

$ pip install celery django-celery
$ pip freeze > requirements.txt

The django-celery package will be eventually outdated and integrated into Celery itself, but for a time being it's still required, as it provides database models to store task results and a database-driven periodic task scheduler, so we won't have to implement our own.

We'll mostly proceed according to official Django/Celery tutorial, taking some additional steps to accommodate for Heroku's specifics.

As tutorial says, let's touch our adding the following snippet:

import djcelery

And include "djcelery" into INSTALLED_APPS tuple.

AMQP broker

One option is to use AMQP-based broker (RabbitMQ). In my perception, it's the most popular option in Celery community.

Let's add it to our Heroku app:

$ heroku addons:add cloudamqp
Adding cloudamqp on secure-fortress-6930... done, v6 (free)
Use `heroku addons:docs cloudamqp` to view documentation.

The first tier ("Little Lemur") is free but limited to 3 concurrent connections. Those should be enough for the single-worker setup, but any additional workers would require larger tiers.

There's also rabbitmq-bigwig addon, but unfortunately, Celery does not support separate consumer and producer connections (there's a yet-unfulfilled feature request for it), so I'm not sure how to deal with it.

We'll need some settings:

BROKER_URL = os.environ.get("CLOUDAMQP_URL", "django://")

CELERY_ACCEPT_CONTENT = ["json", "msgpack"]
CELERYBEAT_SCHEDULER = 'djcelery.schedulers.DatabaseScheduler'

if BROKER_URL == "django://":
    INSTALLED_APPS += ("kombu.transport.django",)

There are a few options that aren't covered by Celery tutorial.

Please, be aware that even though documentation say 1 should be a good choice, Celery may try to open more than 3 connections. In particular, this may happen when multiple requests happen to a website and both try to queue a task. Another option is to set BROKER_POOL_LIMIT = None to disable pool at all and create new connections on demand. This will have some performance penalties (a new TCP connection will be made, and this takes time), but may help in case of tight limits.

When working with "bare" Celery, without Django we'd also have to configure result backend - the place where task results would be stored. In our case, djcelery does the setup automatically, so we don't have to worry about this.

Redis broker

Another option would be a Redis-based broker. AMQP is great, but three connections are barely enough - it's a really tight limitation. RedisToGo addon allows for 10 connections, so we may consider using it instead. Both RabbitMQ and Redis brokers are considered stable and fully featured.

Let's install the addon and Python module for Redis:

$ heroku addons:add rediscloud
Adding rediscloud on secure-fortress-6930... done, v10 (free)
Use `heroku addons:docs rediscloud` to view documentation.

$ echo 'redis==2.10.3' >> requirements.txt
$ pip install redis==2.10.3

Note: we could've achieved the same effect by installing not just celery but celery[redis] at the very first step of this tutorial. On a command line, a quotes may be neccessary (i.e. pip install 'celery[redis]' djcelery) since some shells may try to interpret the square brackets.

Then, do the same editing as with CloudAMQP setup (see the above section), with the sole exception of using another environment variable name for BROKER_URL:

BROKER_URL = os.environ.get("REDISCLOUD_URL", "django://")

Unfortunately, Celery seems to be quite hungly for connections. If the Celery gets over the limit, you may also consider trying setting those with

    "max_connections": 2,

Here, the max_connections option limits the actual number of Redis connections. The issue is, BROKER_POOL_LIMIT controls the number of underlying library (called Kombu)'s connection objects in the pool, but Redis transport can use several actual redis connections to emulate AMQP channels.

Another option that seems to work better for me in such resource-constrained environment is not using pool at all by setting


This way connections will be open only when necessary. Not really good performance-wise, since a new connection has to be established every time it's needed, but this approach can be helpful in some cases.

That's the all differences between broker setup. Since we're using database backend to store results and brokers do not need to persist any information you can switch back and forth without any problems.

Continue with broker setup

It's a good time to commit. And since djcelery app has some models, let's also do syncdb:

$ git add helloworld/ requirements.txt
$ git commit -m 'Added basic Celery support. Nothing useful yet.'
[master 65d3525] Added basic Celery support. Nothing useful yet.
 2 files changed, 26 insertions(+)
$ git push heroku master
-----> Installing dependencies with pip
       Installing collected packages: amqp, anyjson, billiard, celery, django-celery, kombu, pytz
$ heroku run python syncdb
Running `python syncdb` attached to terminal... up, run.3774
Operations to perform:
  Synchronize unmigrated apps: djcelery
  Apply all migrations: admin, auth, contenttypes, sessions
Synchronizing apps without migrations:
  Creating tables...
    Creating table celery_taskmeta
    Creating table celery_tasksetmeta
    Creating table djcelery_intervalschedule
    Creating table djcelery_crontabschedule
    Creating table djcelery_periodictasks
    Creating table djcelery_periodictask
    Creating table djcelery_workerstate
    Creating table djcelery_taskstate
  Installing custom SQL...
  Installing indexes...
Running migrations:
  No migrations to apply.

Until now, all we've did, except for obtaining BROKER_URL wasn't service-dependent. Now, for a Heroku-specific thing. For now, we had only told Heroku to run a website. We also have to run Celery worker and scheduler.

There are three options on how we could achieve this. I'll describe pros and cons of them but it's up to you to chose which one you like the most.

Three-dyno setup

A most nice-looking but money-hungry approach would be to declare a separate worker and a scheduler dynos. In Procfile that would be two additional entries:

worker: python celery worker --loglevel=info
celery_beat: python celery beat --loglevel=info

This way, when we'll need additional workes, we could scale with a simple heroku ps:scale worker=N command.

A compromise: two-dyno setup

Another way is to spawn just a single additional dyno instead of two shown in previous option is to add the following new entry to the Procfile:

main_worker: python celery worker --beat --loglevel=info

Here, to save on dynos count I've used --beat option to run celerybeat scheduler and worker in a same process. In such setup we must be sure there's only one instance of the main_worker (thus, the name), so do not scale it.

Staying in a free tier with a single dyno

The third approach is to save money on the start by not using the second dyno at all. From a Procfile we'll start a process manager that would run multiple processes for us. This just can't scale at all (any attempts to scale would give unpredictable results), but we could easily revise this at a later time.

The only issue is, since this will be the web dyno, it will be killed ("sleeping" in Heroku terms) if no requests happen within one hour. Since we have a scheduler, we could probably work around this limitation by sending an HTTP request to ourselves, though.

Let's consider we've added Celery worker to Procfile using one of the above methods. Some suggest to use Foreman, but in this tutorial I'll stick to Python-only and use Foreman's clone, Honcho.

$ echo 'honcho==0.5.0' >> requirements.txt
$ pip install honcho==0.5.0

As with the three-dyno setup, we'll need a workers declared in a Procfile. Then we'll swap the file with a "proxy" one:

$ echo 'worker: python celery worker --loglevel=info' >> Procfile
$ echo 'celery_beat: python celery beat --loglevel=info' >> Procfile
$ git mv Procfile Procfile.real
$ echo 'web: env > .env; env PYTHONUNBUFFERED=true honcho start -f Procfile.real 2>&1' > Procfile

The PYTHONUNBUFFERED variable is there for logging. By default python has a bit aggressive policy on stream buffering that in some cases may hinder our logs. This has some performance implications, but allows us to be sure whatever subprocesses write to stdout will make it to the logs.

Let's push this to production and see whenever everything runs well:

$ git add requirements.txt Procfile Procfile.real
$ git commit -m 'Use Honcho to run multiple processes in a single dyno.'

[master 5c3d926] Use Honcho to run multiple processes in a single dyno.
 3 files changed, 5 insertions(+), 1 deletion(-)
 create mode 100644 Procfile.real
$ git push heroku master
$ heroku logs -t | cut -c34-
heroku[api]: Deploy 5c3d926 by
heroku[api]: Release v8 created by
slug-compiler]: Slug compilation finished
heroku[web.1]: State changed from up to starting
heroku[web.1]: Stopping all processes with SIGTERM
app[web.1]: [2014-11-16 20:43:56 +0000] [2] [INFO] Shutting down: Master
app[web.1]: [2014-11-16 20:43:56 +0000] [2] [INFO] Handling signal: term
app[web.1]: [2014-11-16 20:43:56 +0000] [7] [INFO] Worker exiting (pid: 7)
heroku[web.1]: Starting process with command `... honcho start -f Procfile.real 2>&1`
heroku[web.1]: Process exited with status 0
app[web.1]: 20:43:57 web.1         | started with pid 5
app[web.1]: 20:43:57 worker.1      | started with pid 7
app[web.1]: 20:43:57 celery_beat.1 | started with pid 9
app[web.1]: 20:43:58 web.1         | [...] [6] [INFO] Starting gunicorn 19.1.1
app[web.1]: 20:43:58 web.1         | [...0] [6] [INFO] Listening at: (6)
app[web.1]: 20:43:58 web.1         | [...] [6] [INFO] Using worker: sync
app[web.1]: 20:43:58 web.1         | [...] [26] [INFO] Booting worker with pid: 26
heroku[web.1]: State changed from starting to up
app[web.1]: 20:43:58 celery_beat.1 | celery beat v3.1.16 (Cipater) is starting.
app[web.1]: 20:44:00 worker.1      | [...: WARNING/MainProcess] celery@... ready

As you may see, another downside of this approach is messy logging. I had to manually clean up the above output for documentation purposes. Unfortunately, it seems this can't be fixed without hacking on Honcho's code to tune the output format and installing a fork instead of the official version from PyPI, which is out of scope of this guide.

Celery essentials

Now, we're done with the basic setup so let's actually write some tasks and their management code.

First of all, let's create A simple task that'd fetch an URL and return a status code would look as following:

from celery import task
import requests

def fetch_url(url, **kwargs):
    A simple task that fetches the provided URL and returns a tuple
    with the HTTP status code and binary response body (if any)
    r = requests.get(url, **kwargs)
    return (r.status_code, r.content)

For convenience I have used requests module here, so let's install it and add to our requirements.txt.

$ echo 'requests==2.4.3' >> requirements.txt
$ pip install requests==2.4.3
$ git add helloworld/ requirements.txt
$ git commit -m 'Added a module with a simple fetch_url task'
[master 2bf607d] Added a module with a simple fetch_url task
 2 files changed, 12 insertions(+)
 create mode 100644 helloworld/

In the actual example project there's also a second task called echo that just returns its sole argument's value. This was done to keep the form's task selection widget populated and not just having a lone "fetch_url" entry.

The only thing that's left is a management code. It's a bit lengthy to cite the whole thing in documentation, so I'll highlight just the most important parts.

Conveniently, django-celery already provides us with all the necessary models to schedule tasks, so we won't need to create any on our own.

Enqueuing the task to run immediately is as simple as a one-liner:

  async_result = tasks.fetch_url.delay("")
  task_id =   # For the next example

The returned AsyncResult has a lot of useful methods and properties that allow to see what's going on with the task and fetch the results when they are available. Please refer to Celery documentation for those.

To obtain the result at a later time, given only ID string we could use the following code:

  async_result = tasks.fetch_url.AsyncResult(task_id)

To schedule a task we'll essentially just need to put things into a database, in a manner similar to the following code:

# Schedule for every 15 minutes on any hour.
# Please, note this is not an interval, but a pattern
# that matches ??:00, ??:15, ??:30 and ??:45.
schedule, _unused = CrontabSchedule.objects.get_or_create(
    minute="*/15", hour="*", day_of_week="*", 
    day_of_month="*", month_of_year="*"

# Actual scheduling
periodic_task = PeriodicTask(

CrontabSchedules are fixed to exact time values. That is, a schedule of "*/15 * * *" means not "every 15 minutes" but "any hour, when minutes are multiple of 15." This may be not the most convenient option, but fortunately there's IntervalSchedule class. To use it with a PeriodicTask instead of crontab argument use interval one:

schedule, _unused = IntervalSchedule.objects.get_or_create(every="hours", period=1)

periodic_task = PeriodicTask(

The IntervalSchedule model has two fields, every and period. The former is a a CharField with choices defined in djcelery.models.PERIOD_CHOICES tuple. Possible values and are ranging from "microseconds" to "days". The latter is just an IntegerField.

When periodic tasks are queued, their IDs are not available, though, so those tasks shouldn't return anything useful and instead keep this in a database. While this is probably out of scope of a basic guide, if, at some time, you would need to work around this limitation, you could subclass djcelery.schedulers.DatabaseScheduler, implement your own apply_entry method that'd keep the IDs and specify this custom class in project's CELERYBEAT_SCHEDULER setting.

The code in actual project is more complex than the crude examples shown above, but it's essentially a boilerplate code that handles the form and renders the templates.

The actual project contains:

To try the project, one can do the following:

$ git clone
$ cd heroku-djcelery-example
$ heroku create
$ heroku addons:add rediscloud
$ git push heroku master
$ heroku run python syncdb
$ heroku open

And, optionally, for local development and debugging create a virtualenv and run pip install -r requirements.txt to setup all the necessary dependencies.

That's it. Hope this guide helped!