Hi,
On Wed, Mar 5, 2025, 8:44 PM me nefcanto <sn.1361@xxxxxxxxx> wrote:
I once worked with a monolithic SQL Server database with more than 10 billion records and about 8 Terabytes of data. A single backup took us more than 21 days. It was a nightmare. Almost everybody knows that scaling up has a ceiling, but scaling out has no boundaries.
But then you did the backup incrementally correct?
That should not take the same amount of time...
Therefore I will never choose a monolithic database design unless it's a small project. But my examples are just examples. We predict 100 million records per year. So we have to design accordingly. And it's not just sales records. Many applications have requirements that are cheap data but vast in multitude. Consider a language-learning app that wants to store the known words of any learner. 10 thousand learners each knowing 2 thousand words means 20 million records. Convert that to 100 thousand learners each knowing 7 thousand words and now you almost have a billion records. Cheap, but necessary. Let's not dive into telemetry or time-series data.
Can you try and see if 1 server with 3 different databases will do?
Having 1 table per database per server is too ugly.
Also please understand - every databae is different. And so it works and operates differently. What work good in one may not work good in another...
Thank you.
We initially chose to break the database into smaller databases, because it seemed natural for our modularized monolith architecture. And it worked great for SQL Server. If you're small, we host them all on one server. If you get bigger, we can put heavy databases on separate machines.However, I don't have experience working with other types of database scaling. I have used table partitioning, but I have never used sharding.Anyway, that's why I asked you guys. However, encouraging me to go back to monolith without giving solutions on how to scale, is not helping. To be honest, I'm somehow disappointed by how the most advanced open source database does not support cross-database querying just like how SQL Server does. But if it doesn't, it doesn't. Our team should either drop it as a choice or find a way (by asking the experts who built it or use it) how to design based on its features. That's why I'm asking.One thing that comes to my mind, is to use custom types. Instead of storing data in ItemCategories and ItemAttributes, store them as arrays in the relevant tables in the same database. But then it seems to me that in this case, Mongo would become a better choice because I lose the relational nature and normalization somehow. What drawbacks have you experienced in that sense?RegardsSaeedOn Wed, Mar 5, 2025 at 7:38 PM Adrian Klaver <adrian.klaver@xxxxxxxxxxx> wrote:On 3/5/25 04:15, me nefcanto wrote:
> Dear Laurenz, the point is that I think if we put all databases into one
> database, then we have blocked our growth in the future.
How?
> A monolith database can be scaled only vertically. We have had huge
> headaches in the past with SQL Server on Windows and a single database.
> But when you divide bounded contexts into different databases, then you
> have the chance to deploy each database on a separate physical machine.
> That means a lot in terms of performance. Please correct me if I am wrong.
And you add the complexity of talking across machines, as well as
maintaining separate machines.
>
> Let's put this physical restriction on ourselves that we have different
> databases. What options do we have? One option that comes to my mind, is
> to store the ID of the categories in the Products table. This means that
> I don't need FDW anymore. And databases can be on separate machines. I
> first query the categories database first, get the category IDs, and
> then add a where clause to limit the product search. That could be an
> option. Array data type in Postgres is something that I think other
> RDBMSs do not have. Will that work? And how about attributes? Because
> attributes are more than a single ID. I should store the attribute key,
> alongside its value. It's a key-value pair. What can I do for that?
You seem to be going out of the way to make your life more complicated.
The only way you are going to find an answer is set up test cases and
experiment. My bet is a single server with a single database and
multiple schemas is where you end up, after all that is where you are
starting from.
>
> Thank you for sharing your time. I really appreciate it.
> Saeed
>
>
>
>
>
> On Wed, Mar 5, 2025 at 3:18 PM Laurenz Albe <laurenz.albe@xxxxxxxxxxx
> <mailto:laurenz.albe@xxxxxxxxxxx>> wrote:
>
> On Wed, 2025-03-05 at 14:18 +0330, me nefcanto wrote:
> > That means a solid monolith database. We lose many goodies with that.
> > As a real-world example, right now we can import a single database
> > from the production to the development to test and troubleshoot data.
>
> Well, can't you import a single schema then?
>
> > What if we host all databases on the same server and use FDW. What
> > happens in that case? Does it return 100 thousand records and join
> > in the memory?
>
> It will do just the same thing. The performance could be better
> because of the reduced latency.
>
> > Because in SQL Server, when you perform a cross-database query
> > (not cross-server) the performance is extremely good, proving that
> > it does not return 100 thousand ItemId from Taxonomy.ItemCategories
> > to join with ProductId.
> >
> > Is that the same case with Postgres too, If databases are located
> > on one server?
>
> No, you cannot perform cross-database queries without a foreign
> data wrapper. I don't see a reason why the statement shouldn't
> perform as well as in SQL Server if you use schemas instead of
> databases.
>
> Yours,
> Laurenz Albe
>
--
Adrian Klaver
adrian.klaver@xxxxxxxxxxx
