celery-message-consumer

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Tool for using the bin/celery worker to consume vanilla AMQP messages (i.e. not Celery tasks)

While writing a simple consumer script <https://medium.com/python-pandemonium/building-robust-rabbitmq-consumers-with-python-and-kombu-part-1-ccd660d17271>__ using Kombu can be quite easy, the Celery worker provides many features around process pools, queue/routing connections etc as well as being known to run reliably over long term.

It seems safer to re-use this battle-tested consumer than try to write our own and have to learn from scratch all the ways that such a thing can fail.

Usage

.. code:: bash

pip install celery-message-consumer

Handlers

In your code, you can define a message handler by decorating a python
function, in much the same way as you would a Celery task:

.. code:: python

    from event_consumer import message_handler

    @message_handler('my.routing.key')
    def process_message(body):
        # `body` has been deserialized for us by the Celery worker
        print(body)

    @message_handler(['my.routing.key1', 'my.routing.key2'])
    def process_messages(body):
        # you can register handler for multiple routing keys

    @message_handler('my.routing.*')
    def process_all_messages(body):
        # or wildcard routing keys, if using a 'topic' exchange

Like a Celery task, the module it is defined in must actually get
imported at some point for the handler to be registered.

A queue (in fact, three queues - see below) will be created to receive
messages matching the routing key.

Celery
~~~~~~

Elsewhere in your code you will need to instantiate a Celery app and
apply our custom 'ConsumerStep' which hooks our message handlers into
the worker. If you are already using Celery *as Celery* in your project
then you probably want separate Celery apps for tasks and for the
message consumer.

.. code:: python

    from celery import Celery
    from event_consumer.handlers import AMQPRetryConsumerStep

    main_app = Celery()

    consumer_app = Celery()
    consumer_app.steps['consumer'].add(AMQPRetryConsumerStep)

You likely will want separate config for each app. See
`Celery docs <http://docs.celeryproject.org/en/latest/userguide/application.html#configuration>`__.

In the config for your message consumer app, add the modules containing
your decorated message handler functions to ``CELERY_IMPORTS``, exactly
as you would for Celery tasks - this ensures they get imported and
registered when the worker starts up.

Then from the command-line, run the Celery worker just like you usually
would, attaching to the consumer app:

.. code:: bash

    bin/celery worker -A myproject.mymodule:consumer_app

Configuration

Settings are intended to be configured primarily via a python file, such as your existing Django settings.py or Celery celeryconfig.py. To bootstrap this, there are a couple of env vars to control how config is loaded:

• EVENT_CONSUMER_APP_CONFIG should be an import path to a python module, for example: EVENT_CONSUMER_APP_CONFIG=django.conf.settings
• EVENT_CONSUMER_CONFIG_NAMESPACE Sets the prefix used for loading further config values from env and config file. Defaults to EVENT_CONSUMER.

See source of event_consumer/conf/ for more details.

Some useful config keys (all of which are prefixed with EVENT_CONSUMER_ by default):

• SERIALIZER this is the name of a Celery serializer name, e.g. 'json'. The consumer will only accept messages serialized in this format.
• QUEUE_NAME_PREFIX if using default queue name (routing-key) then this prefix will be added to the queue name. If you supply a custom queue name in the handler decorator the prefix will not be applied.
• MAX_RETRIES defaults to 4 (i.e. 1 attempt + 4 retries = 5 strikes)
• BACKOFF_FUNC takes a function (int) -> float which returns the retry delay (in seconds) based on current retry counter for the message.
• ARCHIVE_EXPIRY time in milliseconds to keep messages in the "archive" queue, after which the exchange will delete them. Defaults to 24 days.
• USE_DJANGO set to True if your message handler uses the Django db connection, so that the worker is able to cope with the dreaded "current transaction is aborted" error and continue.
• EXCHANGES if you need your message handlers to connect their queues to specific exchanges then you can provide a dict like:

.. code:: python

EXCHANGES = {
    # a reference name for this config, used when attaching handlers
    'default': {  
        'name': 'data',  # actual name of exchange in RabbitMQ
        'type': 'topic',  # an AMQP exchange type
    },
    'other': {
        ...
    },
    ...
}

The 'default' config will be used... by default. You can attach handler to a specific exchange when decorating:

.. code:: python

@message_handler('my.routing.key', exchange='other')
def process_message(body):
    pass

Queue layout

While all of the broker, exchange and queue naming is configurable (see source code) this project implements a very specific queue pattern.

Briefly: for each routing key it listens to, the consumer sets up three queues and a 'dead-letter exchange' (DLX).

#. The "main" message queue #. If any unhandled exceptions occur, and we have retried less than settings.MAX_RETRIES, the message will be put on the "retry" queue with a TTL. After the TTL expires, the DLX will put the message back on the main queue. #. If all retries are exhausted (or PermanentFailure is raised) then the consumer will put the message on the "archive" queue. This gives opportunity for someone to manually retry the archived messages, perhaps after a code fix has been deployed.

| You will of course note that this is totally different and separate from Celery's own task.retry mechanism. | Pros: matches pattern we were already using for non-Celery, non-Python apps, "archive" queue provides an extra safety net. | Cons: Relies on RabbitMQ-specific feature, more queues (more complicated).

Compatibility

Only RabbitMQ transport is supported.

We depend on Celery and Kombu. Their versioning seems to be loosely in step so that Celery 3.x goes with Kombu 3.x and Celery 4.x goes with Kombu 4.x. We test against both v3 and v4.

Django is not required, but when used we have some extra integration which is needed if your event handlers use the Django db connection. This must be enabled if required via the settings.USE_DJANGO flag.

Running the tests

py.test (single combination of dependency versions)

It's also possible to run the tests locally, allowing for debugging of
errors that occur.

We rely on some RabbitMQ features for our retry queues so we need a
rabbit instance to test against. A ``docker-compose.yml`` file is
provided.

.. code:: bash

    docker-compose up -d
    export BROKER_HOST=0.0.0.0

(adjust the last line to suit your local Docker installation)

The ``rabbitmqadmin`` web UI is available to aid in debugging queue issues:

.. code:: bash

    http://{BROKER_HOST}:15672/


You will need to create a virtualenv then
you can install everything via:

.. code:: bash
    pyenv virtualenv 3.11.1 celery-message-consumer
    pip install -r requirements-test.txt

Set an env to point to the target Django version's settings in the test
app (for Django-dependent tests) and for general app settings:

.. code:: bash

    export DJANGO_SETTINGS_MODULE=test_app.dj111.settings
    export EVENT_CONSUMER_APP_CONFIG=test_app.settings

Now we can run the tests:

.. code:: bash

    PYTHONPATH=. py.test -v -s --pdb tests/