Some backend libraries let you write SQL queries as they are and deliver them to the database. They still handle making the connection, pooling, etc.
ORMs introduce a different API for making SQL queries, with the aim to make it easier. But I find them always subpar to SQL, and often times they miss advanced features (and sometimes not even those advanced).
It also means every time I use a ORM, I have to learn this ORM’s API.
SQL is already a high level language abstracting inner workings of the database. So I find the promise of ease of use not to beat SQL. And I don’t like abstracting an already high level abstraction.
Alright, I admit, there are a few advantages:
- if I don’t know SQL and don’t plan on learning it, it is easier to learn a ORM
- if I want better out of the box syntax highlighting (as SQL queries may be interpreted as pure strings)
- if I want to use structures similar to my programming language (classes, functions, etc).
But ultimately I find these benefits far outweighed by the benefits of pure sql.
I too want my query results in an object, but thankfully libraries like sqlx for golang can do this without the extra overhead of an ORM. You give them a select query and they spit out hydrated objects.
As far as multiple DBs go, you can accomplish the same thing as long as you write ANSI standard SQL queries.
I’ve used ORMs heavily in the past and might still for a quick project or for the “command” side of a CQRS app. But I’ve seen too much bad performance once people move away from CRUD operations to reports via an ORM.
Even something as ubiquitous as JSON is not handled in the same way in different databases, same goes for Dates, and UUID. I am not even mentioning migrations scripts. As soon as you start writing raw SQL, I pretty sure you will hit a compatibility issue.
I was specifically talking about python, can’t argue with golang. OK you have a valid point for performance, gotta keep an eye on that. However, I am satisfied for our CRUD api