Forgive my ignorance, but that is the whole point of working with a relational database. If cannot use JOINS then this solves only a very limited use case.
I'm thinking about the application layer. If you have an application that writes data to a table, it's typical to run multiple instances of that application to support scale and reliability requirements.
If I send a write to one instance, how does it communicate and synchronise that write with the other application instances?
I ask because this can be a tricky thing to do, especially when consensus is required, as consensus algorithms such as Raft/Paxos require a number of network roundtrips which will introduce latency, and actually account for much of that latency in the database examples given in some cases.
See it as a simple function that can do oldResults+event=newResults like shown in the big image on top of the readme.
I must admit, with limitations like this I’m struggling to figure out the use cases for this.
Edit: so I guess this is easier using the change subscriptions you mention in other comments. That does mean many subscribers, but hopefully that’s minimal load. This has the trade-off that it’s now eventually consistent, but I suppose that’s not a problem for many high read applications.
I’m still feeling like this could be solved in a simpler way with just simple data structures and a pub sub mechanism. Now I think of it, we do a similar thing with Redis for one service, and a custom Python server/pipeline in another, but we’ve never felt the need for this sort of thing.
Do you have more details about specific applications/use cases, and why this is better than alternatives?