I’m looking for a distributed message queue that will support millions of queues, with each queue handling tens of messages per second.
The messages will be small (tens of bytes), and I don’t expect the queues to get very long–on the order of tens of messages per queue at maximum, but when the system is humming along, the queues should stay fairly empty.
I’m not sure how many nodes to expect in the cluster–probably depends on the specific solution, but if I had to guess, I would say ten nodes. I would prefer that queues were relatively resilient to individual node failures within the cluster, but a few lost messages here and there won’t make me lose sleep.
Does such a message queue exist? Seems like most of the field is optimized toward handling hundreds of queues with high throughput. But what is SQS built on? Surely not magic.
Update:
By request, it may indeed help to shed light on my problem domain. (I’d left details out before so as not to muddy the waters.) I’m experimenting with distributed cellular automata, with an initial target of a million cells in simulation. In some CA models, it’s useful to add an event model, so that a cell can send events to its neighbors. Hence, a million queues, each with one consumer and 8 or so producers.
Costs are a concern for now, as I’m funding the experiments myself. (Thus Amazon’s SQS is probably out of reach.)
From your description, it looks like OMG’s Data Distribution Service could be a good fit. It is related to message queueing technologies, but I would rather call it a distributed data management infrastructure. It is completely distributed and supports advanced features that give you a lot of control over how the data is distributed, by means of a rich set of Quality of Service settings.
Not knowing much about your problem, I could guess what an approach might be. DDS is about distributing the state of strongly-typed data-items, as structures with typed attributes. You could create a data-type describing the state of an automaton. One of its attributes could be an ID uniquely identifying the automaton in the system. If possible, that would be assigned according to a scheme such that every automaton knows what the ID’s of its neighbors are (if they are present). Each automaton would publish its state as needed, resulting in a distributed data-space containing the current state of all automatons. DDS supports so-called partitioning of that data-space. If you took advantage of that, then each of the nodes in your machine would be responsible for a well-defined subset of all automatons. Communication over the wire would only happen for those automatons neighboring a different partition. Since automatons know the ID’s of their neighbors, they would be able to query the data-space for the states of the automatons it’s interested in.
It is a bit hard to explain without a white board, but the end-result would be a single instance (which are a sort of very light-weight message queues) for most automatons, and two or three instances for those automatons at the border of a partition. If you had ten nodes and one million automata, then each node would have to be able to hold administration for approximately hundred thousand automata. I have seen systems being built with DDS of that scale, and larger, with tens of updates per second for each instance. The nice thing is that this technology scales well with the number of nodes, so you could bring down the resource load per node by adding more nodes.
If this is a research project, then you might even be able to use a commercial product without charge. Just google on dds research license.