Laurenz Albe, thanks for your answer.
Right now this data is in MariaDB, on separate databases (schema) but on one server. The solution in this situation is to have a cross-database query. (this is the status quo of our application)
Now our team has decided to migrate to Postgres. However, we realized that Postgres does not support cross-database queries. And if we want to do so, we should use FDW. So, we thought we might as well put databases on separate servers for scalability if we have to write more code. That's the reason behind this question.
But we're stuck at performance. In SQL Server and MariaDB, cross-database queries allow for neat separation of data while delivering good performance in the orchestration layer. You have separate databases, which allows for fine-grained management (different backup schedules, index recalculation, deployment, etc.) but at the same time you can write a query in your app, or in an orchestrator database (let's call it All) that is fast enough for millions of records.
However, we're stuck in this in Postgres. What solutions exist for this problem?
Regards
Saeed
On Wed, 2025-03-05 at 10:12 +0330, me nefcanto wrote:
> Adrian Klaver, thank you for the link. I asked the AI to create a query for me using FDW.
>
> The problem here is that it collects all of the product_id values from the ItemCategories table [...]
>
> That's not scalable. Is there a workaround for this?
Without having scrutinized the case in detail: if your data are organized in an
entity-attribute-value design and distributed across three databases, you cannot
expect to end up with efficient queries.
Perhaps you can extract the data and load them into a reasonably organized
single database. Such an ETL process might make the task much easier.
Yours,
Laurenz Albe
