I'm not sure I would call this relatively easy even without joins. Without joins Google Cloud Firestore does exactly what you're describing. The initial query runs against the DB then the client gets a filtered stream of updates to only that query. Its distributed and scales logarithmically with the number of queries, as it doesn't need to keep the query results in memory/materialized.

The fun part of this problem is that it's really inverted from most traditional database literature out there. Mostly the problem is you have a query and need to find the matching rows. To make updates efficient you need to do this the other way around - if there is a row (that has changed) find all the matching queries.

With joins (or any data dependant query where you can't tell a row is in the result set without looking at other data) you need to keep the query results materialized otherwise you can't have enough information without going back to disk or keeping everything in memory, which isn't really feasible in most cases.

Source: I worked on Google Cloud Firestore from it's launch until 2020 and was the on responsible for the current implementation and data structures of how changes get broadcasted to subscribed queries.