[openstack-dev] [Marconi] Why is marconi a queue implementation vs a provisioning API?

Stan Lagun slagun at mirantis.com
Wed Mar 19 07:09:47 UTC 2014

Kurt Griffiths,

Thanks for detailed explanation. Is there a comparison between Marconi and
existing message brokers anywhere that you can point me out?
I can see how your examples can be implemented using other brokers like
RabbitMQ. So why there is a need another broker? And what is wrong with
currently deployed RabbitMQ that most of OpenStack services are using
(typically via oslo.messaging RPC)?

On Wed, Mar 19, 2014 at 4:00 AM, Kurt Griffiths <
kurt.griffiths at rackspace.com> wrote:

> I think we can agree that a data-plane API only makes sense if it is
> useful to a large number of web and mobile developers deploying their apps
> on OpenStack. Also, it only makes sense if it is cost-effective and
> scalable for operators who wish to deploy such a service.
> Marconi was born of practical experience and direct interaction with
> prospective users. When Marconi was kicked off a few summits ago, the
> community was looking for a multi-tenant messaging service to round out
> the OpenStack portfolio. Users were asking operators for something easier
> to work with and more web-friendly than established options such as AMQP.
> To that end, we started drafting an HTTP-based API specification that
> would afford several different messaging patterns, in order to support the
> use cases that users were bringing to the table. We did this completely in
> the open, and received lots of input from prospective users familiar with
> a variety of message broker solutions, including more "cloudy" ones like
> SQS and Iron.io.
> The resulting design was a hybrid that supported what you might call
> "claim-based" semantics ala SQS and feed-based semantics ala RSS.
> Application developers liked the idea of being able to use one or the
> other, or combine them to come up with new patterns according to their
> needs. For example:
> 1. A video app can use Marconi to feed a worker pool of transcoders. When
> a video is uploaded, it is stored in Swift and a job message is posted to
> Marconi. Then, a worker claims the job and begins work on it. If the
> worker crashes, the claim expires and the message becomes available to be
> claimed by a different worker. Once the worker is finished with the job,
> it deletes the message so that another worker will not process it, and
> claims another message. Note that workers never "list" messages in this
> use case; those endpoints in the API are simply ignored.
> 2. A backup service can use Marconi to communicate with hundreds of
> thousands of backup agents running on customers' machines. Since Marconi
> queues are extremely light-weight, the service can create a different
> queue for each agent, and additional queues to broadcast messages to all
> the agents associated with a single customer. In this last scenario, the
> service would post a message to a single queue and the agents would simply
> list the messages on that queue, and everyone would get the same message.
> This messaging pattern is emergent, and requires no special routing setup
> in advance from one queue to another.
> 3. A metering service for an Internet application can use Marconi to
> aggregate usage data from a number of web heads. Each web head collects
> several minutes of data, then posts it to Marconi. A worker periodically
> claims the messages off the queue, performs the final aggregation and
> processing, and stores the results in a DB. So far, this messaging pattern
> is very much like example #1, above. However, since Marconi's API also
> affords the observer pattern via listing semantics, the metering service
> could run an auditor that logs the messages as they go through the queue
> in order to provide extremely valuable data for diagnosing problems in the
> aggregated data.
> Users are excited about what Marconi offers today, and we are continuing
> to evolve the API based on their feedback.
> Of course, app developers aren't the only audience Marconi needs to serve.
> Operators want something that is cost-effective, scales, and is
> customizable for the unique needs of their target market.
> While Marconi has plenty of room to improve (who doesn't?), here is where
> the project currently stands in these areas:
> 1. Customizable. Marconi transport and storage drivers can be swapped out,
> and messages can be manipulated in-flight with custom filter drivers.
> Currently we have MongoDB and SQLAlchemy drivers, and are exploring Redis
> and AMQP brokers. Now, the v1.0 API does impose some constraints on the
> backend in order to support the use cases mentioned earlier. For example,
> an AMQP backend would only be able to support a subset of the current API.
> Operators occasionally ask about AMQP broker support, in particular, and
> we are exploring ways to evolve the API in order to support that.
> 2. Scalable. Operators can use Marconi's HTTP transport to leverage their
> existing infrastructure and expertise in scaling out web heads. When it
> comes to the backend, for small deployments with minimal throughput needs,
> we are providing a SQLAlchemy driver as a non-AGPL alternative to MongoDB.
> For large-scale production deployments, we currently provide the MongoDB
> driver and will likely add Redis as another option (there is already a POC
> driver). And, of course, operators can provide drivers for NewSQL
> databases, such as VelocityDB, that are very fast and scale extremely
> well. In Marconi, every queue can be associated with a different backend
> cluster. This allows operators to scale both up and out, according to what
> is most cost-effective for them. Marconi's app-level sharding is currently
> done using a lookup table to provide for maximum operator control over
> placement, but I personally think it would be great to see this opened up
> so that we can swap in other types of drivers, such as one based on hash
> rings (TBD).
> 3. Cost-effective. The Marconi team has done a lot of work to (1) provide
> several dimensions for scaling deployments that can be used according to
> what is most cost-effective for a given use case, and (2) make the Marconi
> service as efficient as possible, including time spent optimizing the
> transport layer (using Falcon in lieu of Pecan, reducing the work that the
> request handlers do, etc.), and tuning the MongoDB storage driver (the
> SQLAlchemy driver is newer and we haven't had the chance to tune it yet,
> but are planning to do so during Juno). Turnaround on requests is in the
> low ms range (including dealing with HTTP), not the usec range, but that
> works perfectly well for a large class of applications. We've been
> benchmarking with Tsung for quite a while now, and we are working on
> making the raw data more accessible to folks outside our team. I'll try to
> get some of the latest data up on the wiki this week.
> Marconi was originally incubated because the community believed developers
> building their apps on top of OpenStack were looking for this kind of
> service, and it was a big missing gap in our portfolio. Since that time,
> the team has worked hard to fill that gap.
> Kurt
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Sincerely yours
Stanislav (Stan) Lagun
Senior Developer
35b/3, Vorontsovskaya St.
Moscow, Russia
Skype: stanlagun
slagun at mirantis.com
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