psq

Cloud Pub/Sub Task Queue for Python

GoogleCloudPlatform
198
42
Python

psq - Cloud Pub/Sub Task Queue for Python.

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… Note:: this is not an official Google product, experimental or otherwise and
is provided without support. It is intended as a sample library for
demonstrating a set of use cases for Google Cloud Pub/Sub. The official
Pub/Sub client library should be used for production applications.

psq is an example Python implementation of a simple distributed task
queue using Google Cloud Pub/Sub <https://cloud.google.com/pubsub/>__.

psq requires minimal configuration and relies on Cloud Pub/Sub to
provide scalable and reliable messaging.

psq is intentionally similar to rq <http://python-rq.org/>__ and
simpleq <https://github.com/rdegges/simpleq>, and takes some
inspiration from celery <http://www.celeryproject.org/> and this blog post <http://jeffknupp.com/blog/2014/02/11/a-celerylike-python-task-queue-in-55-lines-of-code/>__.

Installation

Install via pip <https://pypi.python.org/pypi/pip>__:

::

pip install psq

Prerequisites

  • A project on the Google Developers Console <https://console.developers.google.com>__.
  • The Google Cloud SDK <https://cloud.google.com/sdk>__ installed
    locally.
  • You will need the Cloud Pub/Sub API enabled <https://console.developers.google.com/flows/enableapi?apiid=datastore,pubsub>__
    on your project. The link will walk you through enabling the API.
  • You will need to run gcloud auth before running these examples so
    that authentication to Google Cloud Platform services is handled
    transparently.

Usage

First, create a task:

… code:: python

def adder(a, b):
    return a + b

Then, create a pubsub client and a queue:

… code:: python

from google.cloud import pubsub_v1
import psq


project = 'your-project-id'

publisher = pubsub_v1.PublisherClient()
subscriber = pubsub_v1.SubscriberClient()

q = psq.Queue(publisher, subscriber, project)

Now you can enqueue tasks:

… code:: python

from tasks import adder

q.enqueue(adder)

In order to get task results, you have to configure storage:

… code:: python

from google.cloud import pubsub_v1
from google.cloud import datastore
import psq


project = 'your-project-id'

publisher = pubsub_v1.PublisherClient()
subscriber = pubsub_v1.SubscriberClient()
ds_client = datastore.Client()

q = psq.Queue(
    publisher, subscriber, project,
    storage=psq.DatastoreStorage(ds_client))

With storage configured, you can get the result of a task:

… code:: python

r = q.enqueue(adder, 5, 6)
r.result() # -> 11

You can also define multiple queues:

… code:: python

fast = psq.Queue(publisher, subscriber, project, 'fast')
slow = psq.Queue(publisher, subscriber, project, 'slow')

Things to note

Because psq is largely similar to rq, similar rules around tasks
apply. You can put any Python function call on a queue, provided:

  • The function is importable by the worker. This means the
    __module__ that the function lives in must be importable.
    Notably, you can’t enqueue functions that are declared in the
    main module - such as tasks defined in a file that is run
    directly with python or via the interactive interpreter.
  • The function can be a string, but it must be the absolutely importable path
    to a function that the worker can import. Otherwise, the task will fail.
  • The worker and the applications queuing tasks must share exactly the
    same source code.
  • The function can’t depend on global context such as global variables,
    current_request, etc. Pass any needed context into the worker at
    queue time.

Delivery guarantees


Pub/sub guarantees your tasks will be delivered to the workers, but
``psq`` doesn't presently guarantee that a task completes execution or
exactly-once semantics, though it does allow you to provide your own
mechanisms for this. This is similar to Celery's
`default <http://celery.readthedocs.org/en/latest/faq.html#faq-acks-late-vs-retry>`__
configuration.

Task completion guarantees can be provided via late ack support. Late
ack is possible with Cloud Pub/sub, but it currently not implemented in
this library. See `CONTRIBUTING.md`_.

There are many approaches for exactly-once semantics, such as
distributed locks. This is possible in systems such as
`zookeeper <http://zookeeper.apache.org/doc/r3.1.2/recipes.html#sc_recipes_Locks>`__
and `redis <http://redis.io/topics/distlock>`__.

Running a worker
----------------

Execute ``psqworker`` in the *same directory where you tasks are
defined*:

::

    psqworker.py config.q

``psqworker`` only operates on one queue at a time. If you want a server
to listen to multiple queues, use something like
`supervisord <http://supervisord.org/>`__ to run multiple ``psqworker``
processes.

Broadcast queues
----------------

A normal queue will send a single task to a single worker, spreading
your tasks over all workers listening to the same queue. There are also
broadcast queues, which will deliver a copy of the task to *every*
worker. This is useful in situations where you want every worker to
execute the same task, such as installing or upgrading software on every
server.

.. code:: python

    broadcast_q = psq.BroadcastQueue(publisher, subscriber, project)

    def restart_apache_task():
        call(["apachectl", "restart"])

    broadcast_q.enqueue(restart_apache_task)

Broadcast queues provide an implementation of the solution described in
`Reliable Task Scheduling on Google Compute
Engine <https://cloud.google.com/solutions/reliable-task-scheduling-compute-engine>`__.

*Note*: broadcast queues do not currently support any form of storage
and do not support return values.

Retries
-------

Raising ``psq.Retry`` in your task will cause it to be retried.

.. code:: python

    from psq import Retry

    def retry_if_fail(self):
        try:
            r = requests.get('http://some.flaky.service.com')
        except Exception as e:
            logging.error(e)
            raise Retry()

Flask & other contexts
----------------------

You can bind an extra context manager to the queue.

.. code:: python

    app = Flask(__name__)

    q = psq.Queue(extra_context=app.app_context)

This will ensure that the context is available in your tasks, which is
useful for things such as database connections, etc.:

.. code:: python

    from flask import current_app

    def flasky_task():
        backend = current_app.config['BACKEND']

Bypassing workers for testing
-----------------------------

During unit tests you most certainly don't want to spin up workers, but instead
execute the enqueued functions immediately and synchronously. To do this, pass
`asynchronous=False` to the Queue's constructor (default is True). Also, you don't have
to provide a publisher, subscriber or project arguments in this case,
just pass None for all them to the queue.

.. code:: python

    q = psq.Queue(None, None, project=None, asynchronous=False)
    r = q.enqueue(adder, 1, 2) # Will be run immediately



Ideas for improvements
----------------------

-  some sort of storage solution for broadcast queues.
-  Memcache/redis value store.
-  @task decorator that adds a delay/defer function.
-  Task chaining / groups / chords.
-  Late ack.
-  Gevent worker.
-  batch support for queueing.

Contributing changes
--------------------

-  See `CONTRIBUTING.md`_

Licensing
---------

- Apache 2.0 - See `LICENSE`_

.. _LICENSE: https://github.com/GoogleCloudPlatform/psq/blob/master/LICENSE
.. _CONTRIBUTING.md: https://github.com/GoogleCloudPlatform/psq/blob/master/CONTRIBUTING.md

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