For us, we started off with a world where each service communicates to each other only via RabbitMQ, so all fully async. So theoretically each service should be able to be down for however it likes with no impact to anyone else, then it comes back up and starts processing messages off its queue and no one is the wiser.
Our data is mostly append-only, or if it's being changed, there is a theoretical final "correct" version of it that we should converge to. So to "get" data, you subscribe to messages about some state of things, and then each service is responsible for managing its own copy in its own db. This worked well enough until it didn't, and we had to start doing true-ups from time to time to keep things in sync, which was annoying, but not particularly problematic, as we design to assume everything is async and convergent.
The optimization (or compromise) we decided on, was that all of our services use the same db cluster, and that if the db cluster goes down, it means everything is down. Therefore, if we can assume the db is always up, even if a service is down, we consider it an acceptable constraint to provide a readonly view into other services db. Any writes are still sent async via MQ. This eliminates our syncing drifting problem, while still allowing for performant joins, which http apis are bad at and our system uses a lot of.
So then back to your original question, the way that this contract can break is via schema changes. So for us, since we use postgres, we created database views that we expose for reading. And postgres view updates are constrained that they must always be backwards compatible from a schema perspective. So then now our migration path is:
- service A has some table of data that you like to share
- write a migration to expose a view for service A
- write an update for service B to depend upon that view
- service B now needs some more data in that view
- write a db migration for service A that adds that missing data, but keeping the view fully backwards compatible