A single machine solution is obviously a barrier to scaling (and safety, but that is another concern. In a graph database, having relations between the node is the point, that makes sharding a bit more complicated, because unless you store the entire graph on a single machine, you are forced to query across machine boundaries. And you can’t store a graph in a single machine, for the simple reason that it is unlikely that you can limit a graph to be that small. Think about the implications of Six Degrees of Separation for graph databases and it will be clear what the problem is. In real world graphs, everyone is connected to everyone. …. After spending some time thinking about it, I came to the conclusion that I can’t envision any general way to solve the problem. Oh, I can think of several ways of reduce the problem:Read the full article.
The solution most likely to be successful is limiting the depth of cross machine node searches. In many cases, that is acceptable, I think. If we put the depth limit on 3, we can still give pretty good answers in a reasonable time frame. But the only way this can be made to work is with good batching support.
- Batching cross machine queries so we only perform them at the close of each breadth first step.
- Storing multiple levels of associations (So “users/ayende” would store its relations but also “users/ayende”’s relation and “users/arik”’s relations).