I saw the reference to “apps like Figma” and as one of the people that worked on Framer’s (also a canvas based app) database which is also local+multiplayer I find it hard to imagine how to effectively synchronize canvas data with a relational database like Postgres effectively. Users will frequently work on thousands of nodes in parallel and perform dragging updates that occur at 60 FPS and should at least be propagated to other clients frequently.
Does Instant have a way to merge many frequent updates into fewer Postgres transactions while maintaining high frequency for multiplayer?
Regardless this is super cool for so many other things where you’re modifying more regular app data. Apps often have bugs when attempting to synchronize data across multiple endpoints and tend to drift over time when data mutation logic is spread across the code base. Just being able to treat the data as one big object usually helps even if it seems to go against some principles (like microservices but don’t get me started on why that fails more often than not due to the discipline it requires).
Good point on the update frequency, I believe it is a must to batch the requests and responds for any of this type of lib/service to work in a production environment, a performance report/comparison is still required for ppl to get the idea if this is good to support their business model.
About the synchronized data though I think it's not about the database but the data types designed to sync the data? I worked on multiple-player canvas games and we didn't really care that much about relational db or document db, they worked both fine. I would love to know what's the difference and the challanges.
Does Instant have a way to merge many frequent updates into fewer Postgres transactions while maintaining high frequency for multiplayer?
Regardless this is super cool for so many other things where you’re modifying more regular app data. Apps often have bugs when attempting to synchronize data across multiple endpoints and tend to drift over time when data mutation logic is spread across the code base. Just being able to treat the data as one big object usually helps even if it seems to go against some principles (like microservices but don’t get me started on why that fails more often than not due to the discipline it requires).