And by "drop into", this typically means writing custom stitching code that stitches the SQL cursor results back into the models again. It's rarely straightforward.
The biggest selling point is a massive reduction in boilerplate code. Database agnostism is a feature that almost nobody ever uses, so who cares! Your proposed alternative is engine-specific SQL so you lose either way. At least with an ORM, you'd lose significantly less. You'd just have to deal with the places you used SQL. Which, in my experience, is pretty small and pretty specific.
> this typically means writing custom stitching code that stitches the SQL cursor results back into the models again.
I often feel like people who complain about ORMs have either actually never used one or used a poor one. As long as my query matches the structure of my object(s) I don't need any stitching code. And if they didn't match, I wouldn't write stitching code because that would be waste of time.
If I'm writing a custom query, I'm probably not looking to integrate into the model anyway -- if it's used for reporting I'd just take the results as is. If I'm writing a query specifically to get a matching model object (to manipulate) I'm going to get the whole model and no stitching would be required.
I haven't heard anyone talk seriously about database-agnosticism since the very early 2000s. Maybe some commercial products still try (choose MS or Oracle!), but it's rare nowadays.
The primary selling point of an ORM is that it abstracts marshaling/un-marshaling rows to/from entities. Instantiating and persisting entities to relational storage.
> And by "drop into", this typically means writing custom stitching code that stitches the SQL cursor results back into the models again. It's rarely straightforward.
That's not typical in most uses I've seen. Far more typical are things like:
- Go straight to SQL for reporting, since that's what SQL does. Useful in reporting contexts, and also for list/filter UI screens.
- Use raw SQL to query a list of entity IDs for updating based on some complex criteria. Iterate over the identifiers and perform whatever logic you need to before letting the ORM handle all the persistence concerns.
Do you use the same database engine for your unit and integration testing as you do production? I don't. I use sqlite for unit and local integration testing, and aurora-mysql for production.
As a side note, I quite literally can't use aurora-mysql for local unit and integration testing. It doesn't exist outside AWS.
You can use mysql though...
An integration test that doesn't use the same DB as production is unsatisfactory.
Integration tests should run against a test environment, otherwise, what integration are you testing? I don't see the value in writing integration tests that test the integration between my code and a one-off integration test DB that exists solely for the purpose of integration testing.
Yeah, don't do that.
That's a recipe for tests that don't catch edge cases.
Also if you’re using only the subset of MySQL that is supported by SQLite, you’re missing out on some real optimizations for bulk data loads like “insert into ignore...” and “insert...on duplicate key update...”. Besides that, the behavior of certain queries/data types/constraints are inconsistent between MySQL and every other database in existence.
Finally, you can’t really do performance testing across databases.
Queries look like:
SELECT u FROM ForumUser u WHERE (u.username = :name OR u.username = :name2) AND u.id = :id
Where ForumUser is your model.
https://www.doctrine-project.org/projects/doctrine-orm/en/2....
[0] https://github.com/martin-georgiev/postgresql-for-doctrine [1] https://www.doctrine-project.org/projects/doctrine-dbal/en/2...
1. If the suggested alternative is writing only SQL from the get-go, then why would one even care about database agnosticism?
2. A large part of SQL is compatible between databases, so this might be a non-issue.
3. You can always use specific in-database abstractions such as Views and Stored Procedures for those complicated bits.