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Echoing this as well, I worked for Influitive and was one of the original authours of apartment (sorry!) There are a lot of headaches involved with the "tenant

Echoing this as well, I worked for Influitive and was one of the original authours of apartment (sorry!) There are a lot of headaches involved with the "tenant per schema" approach. Certainly it was nice to never have to worry about the "customer is seeing data from another customer" bug (a death knell if you're in enterprisish B2B software), but it added so many problems: - Migrations become a very expensive and time-consuming process, and potentially fraught with errors. Doing continious-deployment style development that involves database schema changes is close to impossible without putting a LOT of effort into having super-safe migrations. - You'll run into weird edge cases due to the fact that you have an absolutely massive schema (since every table you have is multiplied by your number of tenants). We had to patch Rails to get around some column caching it was doing. - Cloud DB hosting often doesn't play nice with this solution. We continually saw weird performance issues on Heroku Postgres, particularly with backup / restores (Heroku now has warnings against this approach in their docs) - It doesn't get you any closer to horizontal scalability, since connecting to a different server is significantly different than connecting to another schema. - It will probably push the need for a dedicated BI / DW environment earlier than you would otherwise need it, due to the inability to analyze data cross-schema. I still think there's maybe an interesting approach using partioning rather than schemas that eliminates a lot of these problems, but apartment probably isn't the library to do it (for starters, migrations would be entirely different if partioning is used over schemas)

sitkack 6y ago
This leads me to believe that everything you mentioned is already subtly broken, it is the new DB/account model that just exposes it. Is there something between the two solutions or pieces that could be modified that collapse the problem? What about going with DB account per app account and using views to limit exposure to data. If user level views are applied before the business logic has access, then the death knell scenario should be avoided. Migrations seem hard regardless, they have to be automated anyway and have a verified rollback/undo mechanism. Code should be multiversion aware if you are doing live upgrades. I am personally a fan of read-only mode while doing the last phase of an ETL upgrade on the backend. Snapshot databases, run a bunch of Hive jobs and throw it back down on a new set of DBs. In the cases you outlined where it was dangerous or fragile, I think those systems were already that way, and that fragility is a system of a different problem. Fix that and lots of other stuff disappears.
- ryanbrunner 6y ago
  I agree that migrations are painful at the best of times, but dealing with the complexity of migrating a single database is far simpler than dealing with migrating hundreds of schemas: - Migrations will first of all just take longer - you're multiplying the number of schema changes by the number of tenants you have. - While in an ideal world migrations should be purely run within a transaction, occasionally performance considerations mandate that you run without DDL transactions - when some tenants fail and your migrations are in a partially completed state for some of your tenants and not others, it can be scary and painful. - In my experience, almost no one approaches data migrations in a way that is purely backwards compatible 100% of the time without exception. You certainly can, but there's a significant tax associated with this, and if you're in a traditional scoped environment, you can often get away with the potential for errors in the minuscule time that a schema change is operating (of course, some schema changes aren't run in minuscule times, but those are the ones you're more likely to plan for) Going read only during migrations is an interesting approach, but there's real business costs associated with that (particularly if your migration speed is multiplied by running it across tenants). I don't want to say that you should never isolate data on a schema level, but I do think it's something that shouldn't be a standard tool to reach for. For the vast majority of companies, the costs outweigh the benefits in my mind.
- bdcravens 6y ago
  A model I haven't fully fleshed out but which looked promising was a single source of truth, and then copying out to Docker instances of each tenant's database. Obviously the nature of data access may not make this practical, but would for our use case (end user is mostly read only, and most writes occur in background that we control)
itsthecourier 6y ago
Oh I want to add, I created an small tool called octoelephant, basically an akka client to parallel issue and merge a query result in thousands of DB. It queries let's say 2000 DBs in 10 secs for regular queries. Throw me a line if you're interested

sitkack 6y ago

This leads me to believe that everything you mentioned is already subtly broken, it is the new DB/account model that just exposes it. Is there something between the two solutions or pieces that could be modified that collapse the problem? What about going with DB account per app account and using views to limit exposure to data. If user level views are applied before the business logic has access, then the death knell scenario should be avoided. Migrations seem hard regardless, they have to be automated anyway and have a verified rollback/undo mechanism. Code should be multiversion aware if you are doing live upgrades. I am personally a fan of read-only mode while doing the last phase of an ETL upgrade on the backend. Snapshot databases, run a bunch of Hive jobs and throw it back down on a new set of DBs. In the cases you outlined where it was dangerous or fragile, I think those systems were already that way, and that fragility is a system of a different problem. Fix that and lots of other stuff disappears.

ryanbrunner 6y ago
I agree that migrations are painful at the best of times, but dealing with the complexity of migrating a single database is far simpler than dealing with migrating hundreds of schemas: - Migrations will first of all just take longer - you're multiplying the number of schema changes by the number of tenants you have. - While in an ideal world migrations should be purely run within a transaction, occasionally performance considerations mandate that you run without DDL transactions - when some tenants fail and your migrations are in a partially completed state for some of your tenants and not others, it can be scary and painful. - In my experience, almost no one approaches data migrations in a way that is purely backwards compatible 100% of the time without exception. You certainly can, but there's a significant tax associated with this, and if you're in a traditional scoped environment, you can often get away with the potential for errors in the minuscule time that a schema change is operating (of course, some schema changes aren't run in minuscule times, but those are the ones you're more likely to plan for) Going read only during migrations is an interesting approach, but there's real business costs associated with that (particularly if your migration speed is multiplied by running it across tenants). I don't want to say that you should never isolate data on a schema level, but I do think it's something that shouldn't be a standard tool to reach for. For the vast majority of companies, the costs outweigh the benefits in my mind.
bdcravens 6y ago
A model I haven't fully fleshed out but which looked promising was a single source of truth, and then copying out to Docker instances of each tenant's database. Obviously the nature of data access may not make this practical, but would for our use case (end user is mostly read only, and most writes occur in background that we control)

itsthecourier 6y ago

Oh I want to add, I created an small tool called octoelephant, basically an akka client to parallel issue and merge a query result in thousands of DB. It queries let's say 2000 DBs in 10 secs for regular queries. Throw me a line if you're interested