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.
You miss the major reason of an orm, abstract vendor specif syntax, i.e. dialect and derived languages such as pl sql, t-sql, etc.
Orm are supposed to allow you to be vendor agnostic
But then you get locked into the ORM’s much more highly specific syntax.
At least the differences across SQL variants are not THAT major from my experience. The core use cases are almost the same.
How many good orm do you have per language? 1? 2? Orm is practically locked once one chooses the language
Surely there’s more than 1 ORM that is at least used commonly enough to have a decent community for every major programming language. Just search the web for ORMs in python, JS, and Go and you’ll see what I mean.
Not even language choice is forever. I’ve seen more codebases change languages or frameworks than I have seen changing databases.
What if you change jobs, and now work with a different language or framework? What if you’re just helping out a sibling team in your company, and they use something different? Having to relearn a new ORM is annoying when you already know SQL.
I am not basing my argument on any of these things having a high likelihood of changing. The main point to me is that you’re abstracting an already high level and very well abstracted API, and the reasons presented don’t justify it (abstracting vendors but then locking you into a more specific vendor).
Sure, there are several. But, for instance, Python is pretty much only sqlalchemy. All others are not really common.
At the end with a single framework one can use several backends. That is pretty convient
Sqlalchemy is really nice too, though I haven’t used the 2.x series yet. I cannot stand the django ORM after using sqlalchemy.
I’m always curious about this particular feature/argument. From the aspect of “i can unit test easier because the interface is abstracted, so I can test with no database.” Great. (though there would be a debate on time saved with tests versus live production efficiency lost on badly formed automatic SQL code)
For anything else, I have to wonder how often applications have actual back-end technologies change to that degree. “How many times in your career did you actually replace MSSQL with Oracle?” Because in 30 years of professional coding for me, it has been never. If you have that big of a change, you are probably changing the core language/version and OS being hosted on, so everything changes.
If you are building software where the customer is the deployer being flexible on what database can be used is a pretty big step. Without it could turn off potential customers that have already existing infastructure.
Some of us have had to support multiple database targets. So I don’t know about changing a database in a running application, but a good abstraction has made it easier to extend support and add clients when we could quickly and easily add new database providerz
Working in a data intensive context, I saw such migrations very often, from and to oracle, ms sql, postgres, sas, exasol, hadoop, parquet, Kafka. Abstraction, even further than orms, is extremely helpful.
Unfortunately in most real case scenarios companies don’t value abstraction, because it takes time that cannot be justified in PI plannings and reviews. So people write it as it is quicker, and migrations are complete re write. A lot of money, time and resources wasted to reinvent the wheel.
Truth is that who pays doesn’t care, otherwise they’d do it differently. They deserve the waste of money and resources.
On the other hand, now that I think of it, I’ve never seen a real impacting OS migration. Max os migration I’ve seen is from centos or suse to rhel… In the field I work on, non unix OSes are always a bad choice anyway
Well, developing on SQLite and deploying to Postgres is a much more common scenario than migrating your data from one DBMS to another.
Yeah, I have my own stuff that lets me do MSSQL, DynamoDB, REST/HATEAOS, regular Hash Maps, and some obscure databases (FilePro).
I throw them in a tree structure and perform depth-first searches for resources. Some of them have stuff for change data capture streaming as well, (eg:
SQLNotifications,DynamoDB Stream,WebSockets).DynamoDB was a rough one to optimize because I have to code to pick the best index. You don’t do that with SQL.
The code on backend is the same as frontend, but a different tree. Frontend queries against REST and a cache layer. Backend queries against anything, REST included.