User Guide

To follow along with this guide you’ll need to install and run RabbitMQ and then set up a new virtual environment in which you’ll have to install Dramatiq and Requests:

$ pip install dramatiq[rabbitmq, watch] requests


As a quick and dirty example of a task that’s worth processing in the background, let’s write a function that counts the “words” at a particular URL:
import requests

def count_words(url):
  response = requests.get(url)
  count = len(response.text.split(" "))
  print(f"There are {count} words at {url!r}.")

There’s not a ton going on here. We just grab the response content at that URL and print out how many space-separated chunks there are in that response. Sure enough, running this in the interactive interpreter yields about what we expect:

>>> from count_words import count_words
>>> count_words("")
There are 338 words at ''.

To turn this into a function that can be processed asynchronously using Dramatiq, all we have to do is decorate it with actor:
import dramatiq
import requests
def count_words(url):
  response = requests.get(url)
  count = len(response.text.split(" "))
  print(f"There are {count} words at {url!r}.")

Like before, if we call the function in the interactive interpreter, it will run synchronously and we’ll get the same result out:

>>> count_words("")
There are 338 words at ''.

What’s changed is we’re now able to tell the function to run asynchronously by calling its send method:

>>> count_words.send("")
  args=('',), kwargs={}, options={},

Doing so immediately enqueues a message (via our local RabbitMQ server) that can be processed asynchronously but doesn’t actually run the function. In order to run it, we’ll have to boot up a Dramatiq worker.


Because all messages have to be sent over the network, any arguments you send to an actor must be JSON-encodable.


Dramatiq comes with a command line utility called, predictably, dramatiq. This utility is able to spin up multiple concurrent worker processes that pop messages off the queue and send them to actor functions for execution.

To spawn workers for our example, run the following command in a new terminal window:

$ env PYTHONPATH=. dramatiq count_words

This will spin up as many processes as there are CPU cores on your machine with 8 worker threads per process. Run dramatiq -h if you want to see a list of the available command line flags.

As soon as you run that command you’ll see log output along these lines:

[2017-06-27 13:03:09,675] [PID 13047] [MainThread] [dramatiq.MainProcess] [INFO] Dramatiq '0.5.0' is booting up.
[2017-06-27 13:03:09,817] [PID 13051] [MainThread] [dramatiq.WorkerProcess(1)] [INFO] Worker process is ready for action.
[2017-06-27 13:03:09,818] [PID 13052] [MainThread] [dramatiq.WorkerProcess(2)] [INFO] Worker process is ready for action.
[2017-06-27 13:03:09,818] [PID 13050] [MainThread] [dramatiq.WorkerProcess(0)] [INFO] Worker process is ready for action.
[2017-06-27 13:03:09,819] [PID 13053] [MainThread] [dramatiq.WorkerProcess(3)] [INFO] Worker process is ready for action.
[2017-06-27 13:03:09,821] [PID 13054] [MainThread] [dramatiq.WorkerProcess(4)] [INFO] Worker process is ready for action.
[2017-06-27 13:03:09,829] [PID 13056] [MainThread] [dramatiq.WorkerProcess(6)] [INFO] Worker process is ready for action.
[2017-06-27 13:03:09,832] [PID 13055] [MainThread] [dramatiq.WorkerProcess(5)] [INFO] Worker process is ready for action.
[2017-06-27 13:03:09,833] [PID 13057] [MainThread] [dramatiq.WorkerProcess(7)] [INFO] Worker process is ready for action.
There are 338 words at ''.

If you open your Python interpreter back up and send the actor some more URLs to process:

>>> urls = [
...   "",
...   "",
...   "",
... ]
>>> [count_words.send(url) for url in urls]
[Message(queue_name='default', actor_name='count_words', args=('',), kwargs={}, options={}, message_id='a99a5b2d-d2da-407b-be55-f2925266e216', message_timestamp=1498557998218),
 Message(queue_name='default', actor_name='count_words', args=('',), kwargs={}, options={}, message_id='0ec93dcb-2f9f-414f-99ec-7035e3b1ac5a', message_timestamp=1498557998218),
 Message(queue_name='default', actor_name='count_words', args=('',), kwargs={}, options={}, message_id='d3dd9799-1ea5-4b00-a70b-2cd6f6f634ed', message_timestamp=1498557998218)]

and then switch back to the worker terminal, you’ll see three new lines:

There are 467 words at ''.
There are 3962 words at ''.
There are 3589 words at ''.

At this point, you’re probably wondering what happens if you send the actor an invalid URL. Let’s try it:

>>> count_words.send("foo")

Error Handling

Dramatiq strives for at-least-once message delivery and assumes all actors are idempotent. When an exception occurs while a message is being processed, Dramatiq automatically enqueues a retry for that message with exponential backoff.

That last message we sent will cause something along these lines to be printed in your worker process:

[2017-06-27 13:11:22,059] [PID 13053] [Thread-8] [dramatiq.worker.WorkerThread] [WARNING] Failed to process message count_words('foo') with unhandled exception.
Traceback (most recent call last):
requests.exceptions.MissingSchema: Invalid URL 'foo': No schema supplied. Perhaps you meant http://foo?
[2017-06-27 13:11:22,062] [PID 13053] [Thread-8] [dramatiq.middleware.retries.Retries] [INFO] Retrying message 'a53a5a7d-74e1-48ae-a5a8-0b72af2a8708' as 'cc6a9b6d-873d-4555-a5d1-98d816775049' in 8104 milliseconds.

Dramatiq will keep retrying the message with longer and longer delays in between runs until we fix our code or for up to about 30 days from when it was first enqueued.

Change count_words to catch the missing schema error:
def count_words(url):
    response = requests.get(url)
    count = len(response.text.split(" "))
    print(f"There are {count} words at {url!r}.")
  except requests.exceptions.MissingSchema:
    print(f"Message dropped due to invalid url: {url!r}")

Then send SIGHUP to the main worker process to make the workers pick up the source code changes:

$ kill -s HUP 13047

Substitute the process ID of your own main process for 13047. You can find the PID by looking at the log lines from the worker starting up. Look for lines containing the string [dramatiq.MainProcess].

The next time your message is retried you should see:

Message dropped due to invalid url: 'foo'

Code Reloading

Sending SIGHUP to the workers every time you make a change is going to get old quick. Instead, you can run the command line utility with the --watch flag pointing to the folder it should watch for source code changes. It’ll reload the workers whenever Python files under that folder or any of its sub-folders change:

$ env PYTHONPATH=. dramatiq count_words --watch .


While this is a great feature to use when developing your code, avoid using it in production!

Message Retries

As mentioned in the error handling section, Dramatiq automatically retries failures with exponential backoff.

You can specify how failures should be retried on a per-actor basis. For example, if you want to limit the maximum number of retries for count_words you can pass the max_retries keyword argument to actor:
def count_words(url):

The following retry options are configurable on a per-actor basis:

Option Default Description
max_retries 20 The maximum number of times a message should be retried. None means the message should be retried indefinitely.
min_backoff 15 seconds The minimum number of milliseconds of backoff to apply between retries. Must be greater than 100 milliseconds.
max_backoff 7 days The maximum number of milliseconds of backoff to apply between retries. Must be less than or equal to 7 days.

Message Age Limits

Instead of limiting the number of times messages can be retried, you might want to expire old messages. You can specify the max_age of messages (given in milliseconds) on a per-actor basis:
def count_words(url):

Dead Letters

Once a message has exceeded its retry or age limits, it gets moved to the dead letter queue where it’s kept for up to 7 days and then automatically dropped from the message broker. From here, you can manually inspect the message and decide whether or not it should be put back on the queue.

Message Time Limits

In count_words, we didn’t set an explicit timeout for the outbound request which means that it can take a very long time to complete if the server we’re requesting is timing out. Dramatiq has a default actor time limit of 10 minutes, which means that any actor running for longer than 10 minutes is killed with a TimeLimitExceeded error.

You can control these time limits at the individual actor level by specifying the time_limit (in milliseconds) of each one:
def count_words(url):


While this will keep our actor from running forever, remember that you should take care to always specify a timeout for the request itself, and this is not a good way to handle request timeouts in production code.


Time limits are best-effort. They cannot cancel system calls or any function that doesn’t currently hold the GIL under CPython.

Handling Time Limits

If you want to gracefully handle time limits within an actor, you can wrap its source code in a try block and catch TimeLimitExceeded:

from dramatiq.middleware import TimeLimitExceeded
def long_running():
    launch_missiles()    # <- this will not run
  except TimeLimitExceeded:
    teardown_missiles()  # <- this will run

Scheduling Messages

You can schedule messages to run up to 7 days into the future by calling send_with_options on actors and providing a delay (in milliseconds):

>>> count_words.send_with_options(args=("",), delay=10000)
  args=('',), kwargs={},
  options={'eta': 1498560453548},

Keep in mind that your message broker is not a database. Scheduled messages should represent a small subset of all your messages.

Prioritizing Messages

Say your app has some actors that are higher priority than others: for example, actors that affect your UI and make users wait, or are otherwise user-facing, versus actors that aren’t. When choosing between two concurrent messages to run, Dramatiq will run the Message that belongs to the actor with the highest priority.

You can set an Actor’s priority via the priority keyword argument:  # 0 is the default
def generate_report(user_id):
def sync_order_to_warehouse(order_id):

That way if both generate_report and sync_order_to_warehouse are scheduled to run at the same time but there’s only capacity to run one of them, generate_report will always run first.

Although all positive integers represent valid priorities, if you’re going to use this feature, I’d recommend setting up constants for the various priorities you plan to use:

PRIO_LO = 100


The lower the numeric value, the higher priority! If you need a mnemonic to remember this, think of having a ticket with a number on it handed to you while waiting in line, perhaps at a government institution or deli counter.

Message Brokers

Dramatiq abstracts over the notion of a message broker and currently supports both RabbitMQ and Redis out of the box. By default, it’ll set up a RabbitMQ broker instance pointing at the local host.

RabbitMQ Broker

To configure the RabbitMQ host, instantiate a RabbitmqBroker and set it as the global broker as early as possible during your program’s execution:

import dramatiq
import pika

from dramatiq.brokers.rabbitmq import RabbitmqBroker

conn_parameters = pika.ConnectionParameters(
rabbitmq_broker = RabbitmqBroker(parameters=conn_parameters)

Make sure to disable heartbeats when defining your own connection parameters by passing them heartbeat_interval=0 since pika’s BlockingConnection does not handle heartbeats.

Redis Broker

To use Dramatiq with the Redis broker, create an instance of it and set it as the global broker as early as possible during your program’s execution:

import dramatiq

from dramatiq.brokers.redis import RedisBroker

redis_broker = RedisBroker(host="redis", port=6537)

Unit Testing

Dramatiq provides a StubBroker that can be used in unit tests so you don’t have to have a running RabbitMQ or Redis instance in order to run your tests. My recommendation is to use it in conjunction with pytest fixtures:
import os

from dramatiq.brokers.rabbitmq import RabbitmqBroker
from dramatiq.brokers.stub import StubBroker

if os.getenv("UNIT_TESTS") == "1":
    broker = StubBroker()
    broker = RabbitmqBroker()
import dramatiq
import pytest

from yourapp import broker

def stub_worker():
  worker = Worker(broker, worker_timeout=100)
  yield worker

Then you can inject and use those fixtures in your tests:

def test_count_words(stub_broker, stub_worker):

Because all actors are callable, you can of course also unit test them synchronously by calling them as you would normal functions.