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 don’t even mention the 2 main advantages:
- ORM lets you to use plain objects over untyped strings. I take typed anything over untyped anything, everyday
- ORM lets you to use multiple database backends. For ex, you don’t need to spawn a local postgres server, then clean/migrate it after each test suit, you can just use in-memory sqlite for that. OK this has some gotchas, but that’s a massive improvement in productivity
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
I was about to write the same thing. Really the object thing is the whole reason to use ORMs.
Using plain SQL is a compatibility and migration nightmare in medium and bigger sized projects. If anything using plain SQL is just bad software design at least in an OOP context.
Not to mention refactoring support…
There seems to be a trend of new (old) developers who find that strong typing is nothing more than a nuisance.
History repeating itself in the IT world. I don’t wanna be around to support the systems that inherit these guys.
Yeah, that’s great, until you need to conditionally compose a query. Suddenly your pre baked queries are not enough. So you either:
- create your own shitty ORM based on string concatenation
- create your own shitty ORM
- or use a well supported ORM, those almost always support query composition and native queries
You write like it’s ORM vs native. ORMs let you write native queries and execute them while also doing all the tedious work for you such as:
- mapping results to model objects
- SQL injection safety
- query composition
- connection builders
- transaction management
So if you love native queries write native queries in an ORM which will do all the tedious shit for you.
mapping results to model objects
I agree. If you have a relational database and an object-oriented programming language you’re going to have to map data one way or another.
That being said, using object-oriented doesn’t necessarily mean the data abstraction needs to be objects too. Python is object-oriented yet Pandas is a very popular relational abstraction for it.
SQL injection safety
Parameterized queries are native to the database engine. They’re going to be available regardless what you use on the client side.
(Well, if the database implements them… having flashbacks to back when MySQL didn’t, and it taught a couple of generations of programmers extremely bad “sanitization” practices.)
query composition
Check out the active record pattern. It’s a thin layer over SQL that lets you put together a query programatically (and nothing more).
connection builders
This is very database specific and many ORMs don’t do a great job of it. If anything this is a con for ORMs not a pro.
transaction management
Again, very hit and miss. Each database has particular quirks and you need to know so much about them to use transactions effectively that it negates any insulation that the ORM provides.
Composable querying/pushdown is nice but transaction management is huge. It’s not an easy task to correctly implement a way to share transactions between methods and between repository classes. But the alternative is, your transactions are limited to individual methods (or you don’t use them, and you risk leaving your database in an inconsistent state without manual cleanup).
Agree 100%. Especially when you’re doing more complicated queries, working with ORM adds so much complexity and obfuscation. In my experience, if you’re doing much of anything outside CRUD, they add more work than they save.
I also tend to doubt their performance claims. Especially when you can easily end up mapping much more data when using a ORM than you need to.
I think ORMs are a great example of people thinking absolutely everything needs to be object oriented. OO is great for a lot of things and I love using it, but there are also places where it creates more problems than it solves.
- manapropos ( @manapropos@lemmy.basedcount.com ) 12•10 months ago
I had a job where we used Spring Boot with JPA on the backend. It was nice because you could just use the ORM methods for basic CRUD functionality. But on the other hand it has this @Query annotation we used for whenever we wanted to write our own queries. Probably against best practice but it worked well enough
The SQL generation is great. It means you can quickly get up and running. If the orm is well designed it should perform well for the majority of queries.
The other massive bonus is the object mapping. This can be an absolute pain in the ass. Especially between datasets and classes.
I find SQL to be easy enough to write without needing generation. It is very well documented, and it is very declarative and English-like. More than any ORM, imo.
I don’t c#'s EF is brilliant
dbContext.Products.Where(p => p.Price < 50).GroupBy(p => p.Category.Id).ToArray()
p.* FROM Products p WHERE p.Price < 50 GROUP BY p.Category_Id``` Meanwhile the ORM is probably generating something stupid and unnecessarily slow like this: ```SELECT p.*, c.* FROM Products p JOIN Category c USING (Category_Id) WHERE p.Price < 50 GROUP BY c.Category_Id``` Now stop using goddamn capital letters in your table and field names. And get off my lawn!No it creates the first one. You can actually use a .Select to grab only the fields you want as well.
If I added .Include(p => p.Category) it would also populate the Category property. At the point it would have to do the join.
Also the table and field names can be specified via attributes or the fluent model builder. Those are the C# object and property names.
They’re nice if they also migrate your db schema. That way you define your schema once and use it both to setup your db and interact with it via code. I do write raw sql for more complex queries, e.g. when there’s recursion.
I really like ORMs when they are well designed. With a bad API though, it hurts me to use them over a general query string.
I built a small driver for ArangoDB that just uses AQL behind the scenes because it’s so much easier to manage.
Since working with SQLAlchemy a lot (specifically it’s SQL compiler, not it’s ORM), I don’t want to work with SQL any other way. I want to have the possibility to extract column definitions into named variables, reuse queries as columns in other queries, etc. I don’t want to concatenate SQL strings ever again.
Having a DSL or even a full language which compiles to SQL is clearly the superior way to work with SQL.
I wish SQL was standardized across vendors.
It is standardized. But some vendors are assholes (or incompetent).
Cough… oracle.
I find SQL, especially prepared statements, to be essentially a function call with a string of text containing identifiers that get swapped out, then a bunch of arguments that get swapped in.
Perhaps there are better SQL libraries that make this more fluent.
But that is so close to an ORM, I feel like I might as well use an ORM.
TL;DR you can’t be an expert at every aspect of coding, so I let the big boys handle SQL and don’t torture the world with my abysmal SQL code.
I’ve seen enough bad SQL to claim you’re wrong (I write bad SQL myself, so if you write SQL like I do, you’re bad at it).
Seriously, the large majority of devs write terrible SQL and don’t know how to optimise queries in any way. They just mash together a query with whichever
JOINthey learned first.NATURAL JOIN? Sure, don’t mind if I do! Might end up being aLEFT JOIN,RIGHT JOIN, orINNER JOIN, but at least I got my data back right? Off the top of your head, do you know all the joins that exist, when to use which one, and which ones are aliases for another? Do you know how to write optimalJOINs when querying data with multiple relations?When writing similar queries, do you think most are going to copy-paste something that worked and adapt it? What if you find out that it could be optimised? Then you’ll have to search for all queries that look somewhat similar and fix those.
When you create an index for a table, are you going to tell me you are going to read up on the different types each time to make sure you’re using the one that makes sense? Postgres has 6, MySQL only has 2 tbf depending on storage engine, but what about other DBs? If you write something for one DB and a client or user wants to host it with another, what will your code look like afterwards?
Others have brought up models in code, so that’s already discussed, but what about migrations? Do you think it’s time well-spent writing every single migration yourself? I had the distinct pleasure of having to deal with hand-written migrations that were copy-pasted and modified columns that had nothing to do with the changed models, weren’t in a transaction, failed half-way through, and tracking down which migration had actually failed. These were seasoned developers who completely forgot to put any migration in transactions. They had to learn the hard way.
ORMs introduce a different API for making SQL queries, with the aim to make it easier.
I wouldn’t say that, but instead, that they strive to keep everything contained in one language/stack/deployment workflow, with the benefit of code reusability (for instance, it’s completely idiotic, if you ask me, that your models’ definition and validation code get duplicated in 3 different application layers (front/API/DB) in as many different languages.
ORMs are not a 100% solution, but do wonders for the first 98% while providing escape hatches for whatever weird case you might encounter, and are overall a net positive in my book. Moreover, while I totally agree that having DB/storage-layer knowledge is super valuable, SQL isn’t exactly a flawless language and there’s been about 50 years of programming language research since it was invented.
You, my friend, should try EdgeDB. A database and an ORM in one.
When you change the data model, you can get to 100%, which you say is impossible for ORMs
This is a project I am already keeping a close eye on, but I would rather qualify it as a “better SQL” than as an alternative to your typical (framework’s) ORM. For instance, it won’t morph CRUD operations and data migrations into a language/stack that’s native to the rest of the project (and by extension, imply learning another language/stack/set of tools…)
I find ORMs exist best in a mid-sized project, most valuable in a CQRS context.
For anything small, they massively over complicate the architecture. For the large enterprise systems, they always seem to choke on an already large and complex domain.
So a mid size project, maybe with less than a hundred or so data objects works best with an ORM. In that way, they’ve also been most productive mainly for the CUD of the CRUD approach. I’d rather write my domain logic with the speed and safety of an ORM during writes, but leverage the flexibility and expressiveness of SQL when I’m crafting efficient read queries.
Completely agree. Most ORMs focus on hiding SQL away (for good reasons, such as portability and type safety), but I wish there were more approaching it in the reverse. That is, have the user write schemas, queries and migrations in SQL, and generate models with typesafe APIs in return. I’m only aware of SQLDelight in this space, but it’s such a great idea to have the source of truth be actual SQL, and a build time generator and validator working alongside you.
sqlboiler does this in Go
I absolutely prefer using an ORM for querying but I’m definitely never letting the ORM create the schema for me. I will always do that myself and generate the ORM definitions from SQL, and I will never use an ORM that doesn’t have that as an option.
I don’t like traditional ORM’s but I really enjoy using jooq. Statically typed queries are great with this library.
jOOQ is really the best of both worlds. Just enough of an ORM to make trivial CRUD operations trivial, but for anything beyond that, the full expressive power of SQL with added compile-time type safety.
And it’s maintained by a super helpful project lead, too.