Materialize https://materialize.io/ Incremental update/materialization of database views with joins and aggregates is super interesting. It enables listening to data changes, not just on a row level, but on a view level. It's an approach that may completely solve the problem of cache invalidation of relational data. Imagine a memcache server, except it now also guaranties consistency. In addition, being able to listen to changes could make live-data applications trivial to make, even with filters, joins, whatever.

Similarly, someone is developing a patch for postgres that implements incrementally updating/materializing views[1]. I haven't tried it so I can't speak of its performance or the state of the project, but according to the postgres wiki page on the subject [2] it seems to support some joins and aggregates, but probably not something that would be recommended for production use.

[1] https://www.postgresql-archive.org/Implementing-Incremental-... [2] https://wiki.postgresql.org/wiki/Incremental_View_Maintenanc...

They're marketing it in the OLAP space right now, but at some point I'd like to try integrating it with a web framework I've been working on.[1][2] It'd be a more powerful version of firebase's real-time queries. Firebase's queries don't let you do joins; you basically can just filter over a single table at a time. So you have to listen to multiple queries and then join the results by hand on the frontend. Doesn't work if you're aggregating over a set of entities that's too large to send to the client (or that the client isn't authorized to see).

[1] https://findka.com/blog/migrating-to-biff/ [2] https://github.com/jacobobryant/biff