Too many ways to do async tasks with Plone

Triggering asynchronous tasks from Plone is hard, we hear. And that's actually quite surprising, given that, from its very beginning, Plone has been running on top of the first asynchronous web server written in Python, medusa.

Of course, there exist many, too many, different solutions to run asynchronous task with Plone:

  • plone.app.async is the only one in Plone-namespace, and probably the most criticized one, because of using ZODB to persist its task queue
  • netsight.async on the other hand being simpler by just executing the the given task outside Zope worker pool (but requiring its own database connection).
  • finally, if you happen to like Celery, Nathan Van Gheem is working on a simple Celery-integration, collective.celery, based on an earlier work by David Glick.

To add insult to injury, I've ended up developing a more than one method more, because of, being warned about plone.app.async, being hit hard by the opinionated internals of Celery, being unaware of netsight.async, and because single solution has not fit all my use cases.

I believe, my various use cases can mostly be fit into these categories:

  • Executing simple tasks with unpredictable execution time so that the execution cannot block all of the valuable Zope worker threads serving HTTP requests (amount of threads is fixed in Zope, because ZODB connection cached cannot be shared between simultaneous requests and one can afford only so much server memory per site).

    Examples: communicating to external services, loading an external RSS feed, ...

  • Queueing a lot of background tasks to be executed now or later, because possible results can be delivered asynchronously (e.g. user can return to see it later, can get notified about finished tasks, etc), or when it would benefit to be able to distribute the work between multiple Zope worker instances.

    Examples: converting files, encoding videos, burning PDFs, sending a lot of emails, ...

  • Communicating with external services.

    Examples: integration between sites or different systems, synchronizing content between sites, performing migrations, ...

For further reading about all the possible issues when queing asynchronous tasks, I'd recommend Whichert Akkermans' blog post about task queues.

So, here's the summary, from my simpliest approach solution to enterprise messaging with RabbitMQ:

ZPublisher stream iterator workers

class MyView(BrowserView):

    def __call__(self):
        return AsyncWorkerStreamIterator(some_callable, self.request)

I've already blogged earlier in detail about how to abuse ZPublisher's stream iterator interface to free the current Zope worker thread and process the current response outside Zope worker threads before letting the response to continue its way towards the requesting client (browser).

An example of this trick is a yet another zip-export add-on collective.jazzport. It exports Plone-folders as zip-files by downloading all those to-be-zipped files separately simply through ZPublisher (or, actually, using site's public address). It can also download files in parallel to use all the available load balanced instances. Yet, because it downloads files only after freeing the current Zope worker instance, it should not block any worker thread by itself (see its browser.py, and iterators.py).

There are two major limitations for this approach (common to all ZPublisher stream iterators):

  • The code should not access ZODB after the worker thread has been freed (unless a completely new connection with new cache is created).
  • This does not help installations with HAProxy or similar front-end proxy with fixed allowed simultaneous requests per Zope instance.

Also, of course, this is not real async, because it keeps the client waiting until the request is completed and cannot distribute work between Zope instances.


class MyView(BrowserView):

    def __call__(self):
            return futures.result('my_unique_key')
        except futures.FutureNotSubmittedError:
            futures.submit('my_unique_key', some_callable, 'foo', 'bar')
            return u'A placeholder value, which is never really returned.'

collective.futures was the next step from the previous approach. It provides a simple API for registering multiple tasks (which does not need to access ZODB) so that they will be executed outside the current Zope worker thread.

Once all the registered tasks have been executed, the same request will be queued for ZPublisher to be processed again, now with the responses from those registered tasks.

Finally, the response will be returned for the requesting like with any other requests.

collective.futures has the same issues as the previous approach (used in collective.jazzport), and it may also waste resources by processing certain parts of the request twice (like publish traverse).

We use this, for example, for loading external RSS feeds so that the Zope worker threads are freed to process other requests while we are waiting the external services to return us those feeds.


class MyView(BrowserView):

    def __call__(self):
        return u'Task queued, and a better view could now display a throbber.'

collective.taskqueue should be a real alternative for plone.app.async and netsight.async. I see it as a simple and opinionated sibling of collective.zamqp, and it should be able to handle all the most basic asynchrnous tasks where no other systems are involved.

collective.taskqueue provides one or more named asynchronously consumed task queues, which may contain any number of tasks: asynchronously dispatched simple requests to any traversable resources in Plone.

With out-of-the-box Plone (without any other add-ons or external services) it provides instance local volatile memory based task queues, which are consumed by the other one of the default two Zope worker threads. With redis, it supports persistent task queues with quaranteed delivery and distributed consumption. For example, you could have dedicated Plone instances to only consume those shared task queues from Redis.

To not sound too good to be true, collective.taskqueue does not have any nind of monitoring of the task queues out-of-the-box (only a instance-Z2.log entry with resulted status code for each consumed task is generated).


class MyView(BrowserView):

    def __call__(self):
        producer = getUtility(IProducer, name='my.asyncservice')
        producer.register()  # bind to successful transaction
        producer.publish({'title': u'My title'})
        return u'Task queued, and a better view could now display a throbber.'

Finally, collective.zamqp is a very flexible asynchronous framework and RabbitMQ integration for Plone, which I re-wrote from affinitic.zamqp before figuring out any of the previous approaches.

As the story behind it goes, we did use affinitic.zamqp at first, but because of its issues we had to start rewrite to make it more stable and compatible with newer AMQP specifications. At first, I tried to built it on top of Celery, then on top of Kombu (transport framework behind Celery), but at the end it had to be based directly on top of pika (0.9.4), a popular Python AMQP library. Otherwise it would have been really difficult to benefit from all the possible features of RabbitMQ and be compatible with other that Python based services.

collective.zamqp is best used for configuring and executing asynchronous messaging between Plone sites, other Plone sites and other AMQP-connected services. It's also possible to use it to build frontend messaging services (possibly secured using SSL) with RabbitMQ's webstomp server (see the chatbehavior-example). Yet, it has a few problems of its own:

  • it depends on five.grok
  • it's way too tighly integrated with pika 0.9.5, which makes upgrading the integration more difficult than necessary (and pika 0.9.5 has a few serious bugs related to synchronous AMQP connections, luckily not requird for c.zamqp)
  • it has a quite bit of poorly documented magic in how to use it to make all the possible AMQP messaging configurations.

collective.zamqp does not provide monitoring utilities of its own (beyond very detailed logging of messaging events). Yet, the basic monitoring needs can be covered with RabbitMQ's web and console UIs and RESTful APIs, and all decent monitoring tools should have their own RabbitMQ plugins.

For more detailed examples of collective.zamqp, please, see my related StackOverflow answer and our presentation from PloneConf 2012 (more examples are linked from the last slide).