There's a middle way which is very powerful: SQL views (just SQL queries; no triggers or procedures)
Here's a powerful mindset trick: think of SQL views as an sort of a REST API, but whose access language is SQL and not HTTP, and that returns data in a table rather than JSON (hierarchical).
I once tried to build a REST API to a database, and someone told me I already had a battle-tested and highly performant API that outperformed REST at scale -- it's called SQL. A SQL view is a dynamic lens into the underlying tables, so even if the underlying tables/schemas were to change, your consumers don't care as long as they can access the SQL View.
SQL views are also composable: you can build SQL views on top of other SQL views, and any changes made in the base views are propagated throughout. Need to add/transform a field? Do it in the view. Need pull in auxiliary data? Bring it in through a JOIN in the view. I've built many systems by composing SQL views and they're very maintainable and very flexible. They're kind of like function compositions but on tabular data.
The rule of thumb is: always access a database through a view, never the underlying raw tables. In computer science, a great many maintainability issues are alleviated through a layer of abstraction/indirection, and SQL views provide exactly that.
This centralization of the core logic becomes especially powerful if the database is accessed from multiple consumers (webapps, analytics backends, Tableau, ML tools, etc.) The "API" remains consistent throughout.
Do you have any example code that shows how this works? I get what you’re saying intuitively but example code will help me bring it to table.
What about cross cutting concerns? I’ve found stored procedures to be a performant solution here. By version controlling them, and limiting to pure functions, I found them quite maintainable. Would you instead just define a new view, or extend an existing one, or refactor into a separate view that’s then joined into the existing views?
I haven’t delved as far as views, admittedly. One app featured a bit of complicated logic and eschewing the ORM in favour of raw SQL helped (instead of getting tangled up in Demeter chains). Despite new developers, who have used purely ORM for years, shitting their pants at the raw SQL, both of us who worked on it felt it was the right call. We feel much better about leveraging more of the database in new projects.
In fact, when we took our experience to a Django project, my colleague wrote a Manager method in such a way that an ORM favouring developer questioned because it looked too much like SQL. But it was the obvious implementation to us after using raw SQL. And, after benchmarking, the most performant.
1. Let me try with a simple example. Suppose you have a fact table A with fields (ItemID, Item, Amt) where Amt is in USD. Rule of thumb is: don't expose A to the consumer; instead write a SQL View V_A and expose that instead:
CREATE VIEW V_A AS SELECT ItemID, Item, Amt FROM A
Then suppose a European counterpart wants to use the same API but needs the amounts to be in Euros. You can write another view: (in practice the conversion 0.92 shouldn't be a static number, this is just for illustration)
CREATE VIEW V_A_EURO AS SELECT ItemID, Item, Amt * 0.92 AS AmtEUR FROM V_A
Expose this to the Europeans. You can keep stacking views on top of other views. Your U.S. consumers will always see the data through the lens of V_A and your European consumers will always see it through V_A_Euro.
Suppose the underlying table A now changes. There's been a merger and the company now stops reporting currencies in USD, and everything is now in British Pounds so your DBA adds a field AmtGBP and starts populating that field instead. Amt still contains historical data, but moving forward the data in Amt will be NULLs; AmtGBP is the new internal baseline currency. From a VIEW perspective, all you have to do is:
ALTER VIEW V_A AS SELECT ItemID, Item, ISNULL(Amt, AmtGBP * 1.23) AS Amt FROM A
Your V_A and V_A_EURO consumers (could be Tableau, Excel, other SQL views, etc.) will still happily receive data per usual, unaware of the internal changes (the British are coming!) that have occurred. Contract kept.
Table A <- View V_A <- View V_A_Euro
2. Cross cutting concerns come in many forms so not sure if I can address. Stored Procedures are definitely an acceptable abstraction -- they accept parameters and can return tabular results just like VIEWs. They do however work in a procedural manner (like subroutines) and can produce side effects, which is sometimes necessary to accomplish very specific tasks. VIEWs on the other hand are more similar to pure functions (unless random number generation is involved) with no side effects. Because views are dynamic, they flex with your data and VIEW definitions.
There's another step that could be added there, too: After the ALTER VIEW, V could be slowly incrementally updated over however long you need to back-populate AmtGBP, and the views will continue to just work the whole time. Once done, V_A can be simplified to remove the ISNULL and Amt, then Amt dropped from V. That way you don't get build-up of cruft over the years, and the experience isn't interrupted for the migration.
(Possibly a bad idea for currency conversion for various reasons, but just wanted to mention it since this type of migration may be just right for other data)
Is there anything you recommend for handling SQL definitions in version control, development and production envs?
For production, I created a command on the app that loads the stored procedures into the DB idempotently on each deployment/configuration. This won’t work if the app server scales but allowed us to store stored procs in VC.
For development, we ran the command on each page load as a sort of hacky “live reload”. It didn’t work well (which highlighted the issue with scalability in production) because Postgres, fairly, doesn’t like parallel redefinitions of the same stored proc.
I’m not sure how best to automate this. For production, seems like a case of running a command once per DB server.
And in development, using a fs watcher that loads changes in.
But I don’t know, this is new territory for us and I couldn’t find anything out there to manage it within the context of a web framework. Perhaps I’m searching for the wrong thing.
Web frameworks like Rails/Django use the idea of migrations to make changes to the database. The idea is that you have a set of migration scripts like:
migrations/1765_create_table_users.sql
migrations/2891_store_procedure_x.sql
migrations/5892_change_store_procedure_x.sql
(.sql/.rb/.py, it doesn't matter).
And you have a "migrations" table in your database that contains the numbers of the migrations that have been run:
select * from migrations;
version
----------------
1765
2891
Every time you deploy to production automatically check which scripts in your db/migrations folder don't exist in the migrations table and run them. (In the current example, you'd run the 5892_change_store_procedure_x.sql that hasn't been run yet).
How to do with functions/procedures?
You commit the function definitions in a functions folder to your version system like:
db/functions/report_x.sql
CREATE or REPLACE function report_x() returns ...
When you change this file, nothing happens, you need to create a migration to re-run this code once. In rails migrations would be:
class UpdateReportXFun < ActiveRecord::Migration[5.2]
def up
execute File.read(
Rails.root.join('db','functions','report_x.sql')
)
end
end
Yeah, I’m aware of that, thank you. I was wondering if there was a way with a faster feedback loop and allowed for bug fixes without creating a new migration.
You don't need to write the migration until you're done. It's possible to have a very tight feedback loop in any case.
I'm doing a lot of work in a Rails codebase where I edit views/functions/procedures all the time. My setup is quite usable.
My current setup: I edit those .sql files and run them with psql in my local while developing (without writing any migration yet).
I have some like this running on one screen to make sure the modified files are executed by psql immediately as I change them (you could use `guard` too):
and I edit the db/functions/*.sql files freely, adding things, changing behaviour of functions and they are updated on the fly. (I can run tests -or try things in the browser- to verify my changes work as I expect).
--
Once I finish and I know everything is great, I just add the migration. The migration is simply an indicator of which files I've modified and to specify the right order to run them (which is useful if they are dependencies), like:
# migration
def up
execute File.read(function1_sql_file)
execute File.read(function2_sql_file)
end
I could have an alias that automates generating that migration but it's just 4 lines...
[ I'm also using pgTAP to write tests for functions, it's quite nice :) ]
Oh wow, now I see what you mean. Thank you! That’s work great. I wasn’t aware of ‘entr’ either, that’s exactly what I had in mind!
I’ll have a look at pgTAP too. Naturally we want to test in CI, I can see this working really well. I did look at myTAP too, since we have a few MySQL instances.
Agreed, and a good example of this is implementing search. You can define a view on top of your searchable entities that includes the urls to the entities, as well as searchable metadata (entity descriptions or whatever). So when you add new searchable items, you just update the view to include them and the code to select from the view doesn't change.
> highly performant API that outperformed REST at scale -- it's called SQL
You are conflating many disparate things here.
SQL is a language (DSL) for accessing data. REST is a protocol and a data transport method (one could surmise a way to do REST without HTTP, but when reasonable people refer to REST they mean HTTP (over TCP (over IP (etc.)))).
Even REST is not an API. You can't do anything with a GET or a POST without other abstractions built on top of that. So I don't understand how anyone could make performance claims beyond something like "HTTP is slow" and "binary transport is faster", with regards to SQL vs. REST/HTTP.
SQL does not define how you receive your data. Databases have different methods of sending SQL and responding to SQL. Oracle, MSSQL, MySQL, etc.
> This centralization of the core logic becomes especially powerful if the database is accessed from multiple consumers
That's the entire point of an API. Any API. REST APIs, even.
Right, and this is normal except that people take it too far, I love to find 10(!) depth nested views hiding table valued functions and scalar functions everywhere - you cant reason about the rat's nest created.
If you want to make simple views that expose useful nouns I am down with it, but I have seen it taken too far too many times.
Postgres has something similar called pgREST too. I think I would only adopt these kinds of interfaces if the consumer insists on accessing the data through a REST interface.
If you are building something from scratch, or your consumers don't have a hard requirement for going through REST, I would go directly to the database view.
Here's a powerful mindset trick: think of SQL views as an sort of a REST API, but whose access language is SQL and not HTTP, and that returns data in a table rather than JSON (hierarchical).
I once tried to build a REST API to a database, and someone told me I already had a battle-tested and highly performant API that outperformed REST at scale -- it's called SQL. A SQL view is a dynamic lens into the underlying tables, so even if the underlying tables/schemas were to change, your consumers don't care as long as they can access the SQL View.
SQL views are also composable: you can build SQL views on top of other SQL views, and any changes made in the base views are propagated throughout. Need to add/transform a field? Do it in the view. Need pull in auxiliary data? Bring it in through a JOIN in the view. I've built many systems by composing SQL views and they're very maintainable and very flexible. They're kind of like function compositions but on tabular data.
The rule of thumb is: always access a database through a view, never the underlying raw tables. In computer science, a great many maintainability issues are alleviated through a layer of abstraction/indirection, and SQL views provide exactly that.
This centralization of the core logic becomes especially powerful if the database is accessed from multiple consumers (webapps, analytics backends, Tableau, ML tools, etc.) The "API" remains consistent throughout.