That being said, if orms didn't force you to explicitly define your domain models about 60% of developers would simply never do it. And you would see differently structured, ad-hoc interfaces defined all over the code base completely entangled with whatever action they are trying to perform.

ORMs being a forcing function for domain modeling is enough benefit for me that it outweighs all of their obvious limitations.

I personally think that ORMs are good for management and simple CRUD cases, QueryBuilders are good for managing more complex queries while still being secure / type-safe and for everything else a thin database abstraction layer for native SQL queries with parameters / prepared statements is still required especially for performance use cases.

And then there’s the “now you have two problems” dynamic. You not only have to write high-performing queries, but you have to get the ORM to generate that query for you. And sometimes you don’t want objects. And the schema mapping has to track schema changes.

Just write the damned SQL, it’s not that difficult.

So was your ORM for Oak? Java didn't hit the public sphere until 1995 IIRC

I've written complicated stuff where an ORM isn't appropriate, but if I'm honest, a large fraction of what I've done in my career is just making boring software to automate menial clerical work, and ORMs are good enough for those kinds of projects.

The author basically says this in the first paragraph, but the title (and some of the language the author uses) implies that people should just use SQL.

It's a reasonable article pointing out some of the annoyances and problems of ORMs (especially in the Java world, where they tend to be overengineered) but there are still a lot of advantages to them if you are in an OO language and they used in a reasonable way.

ORMs build queries for you, but a query builder does not need to be an ORM.

[1] LLMs make these very easy to handle.

The choice of DB is arguably more important than the choice of backend language.

1. the functional/immutable nature of Elixir makes read and writes much more explicit and there is no need to magically track deep mutations of nested objects to translate them back into UPDATE/INSERT queries

2. Elixirs support for lisp-like macros allows for an ergonomic embedded query languages that is syntax and schema checked, mirrors raw SQL really well and, frees you from string-oriented query building

3. the query builder DSL addresses one of the main weaknesses of SQL query statements not being composable

4. The automatic conversion between JOINed tables (on the DB side) and nested structs (on the Elixir side) is done on the right abstraction level to work reliable and and being explicit enough to generate predictable queries.

I make use of table-valued db functions (IMO the most underrated feature of relational DBs) to define virtual relations/tables. I implement a set of CRUD db functions per entity. Then, on the app side, I define (or generate) DTO types representing these virtual relations. Finally, I use a custom ORM I wrote myself, which defines a general and consistent storage API, to talk to the db functions, using the DTO types.

The advantages of this approach are numerous, some include:

- I have full control of the SQL that goes into constructing the virtual table, I can leverage all the goodness of SQL here. I can even define multiple virtual relations per physical table, or read-only relations, etc, all by implementing the appropriate sets of CRUD db functions

- On the ORM side, I have all the goodness of static typing, a consistent API for all CRUD methods, a full fluent query DSL, etc

- Since, unlike tables or views, db functions can be passed arguments, i am able to layey all kinds of goodness on top of the basic CRUD actions, like audit info passing, custom upsert strategies, some level of record-based authorization, etc

But this architecture does require you to know and write SQL. IMO the value of ORMs do not lie in avoiding SQL; it lies in the capability to express consistent SQL at a higher level of abstraction, but you still need to understand your SQL.

ORMs that try to paper over all the differences fail miserably. They become super complicated and generally produce crap SQL.

ORMs also tend to oversimplify database design. They are just tables with primary keys, right? Who needs indices? Who needs to think about collation? God forbid anyone mentions physical organisation of the data!

Having said this, I do use a very small subset of SQLAlchemy (the bits I understand) in data pipelines.

The idea is that you like SQL, but it gets repetitive writing joins and accessor code. I had always hoped it would catch on as a pattern: no boilerplate, automatic mapping to objects in your code of any query (whether generated by the ORM or passed in as a raw query) and easy to override/dynamically build bits of the query as you pass the object around.

It's not a matter of "fractional speed difference" unless your database has very few entries. OR mismatch problems often like to appear shortly after your database starts to see any real use.

The only performant way to use an ORM is to use escape hatches everywhere. Alternatively, you can use an "ORM", something which calls itself an ORM while only doing superficial data mapping into dynamic or generated native (to your language) data structures. There are a _lot_ of these, most normal people call them query generators.

I stopped using ORMs around 2008 because they made the easy problems easier and the hard problems harder. I wanted to just write SQL and exploit all the power the DBMS has to offer instead of fighting with an abstraction layer, so I created Pyranid in 2015 and keep it actively updated.

A now defunct site discussing why ORM is a poor map.

Can anyone that has used ActiveRecord share their opinion?

> ...(although things like Postgres’ hstore can help)...

Back when this blog post was written, this advice would have been reasonable. Today, I don't know anyone reaching for hstore since the more featureful json support was added.

- "the pernicious use of foreign keys [...] links between classes are [...] foreign keys" ==> that just sounds like schema normalization, which is usually a good thing?

- "bending over backwards [...] to generate SQL that runs efficiently" ==> the huge majority of ORM-driven queries are "select * from table where id in ..."; for the queries that are more complicated than that, then yes use SQL! That's allowed!

Folks who dislike ORMs seem to have this false dichotomy that "the ORM _must_ be used for all queries", which is a self-imposed/unpractical restriction.

- "dual schema dangers" ==> he's exactly right that database should own the schema definition, but then just codegen the entities from the db schema? That's your singular source of truth, no drift. You can do this with Hibernate, ActiveRecord, Joist, many ORMs.

- "Identities" ==> ironically I think ORMs (that use the unit of work pattern) actually have net-better DX here b/c you can hook up a graph of entities with just references.

I.e. hook up a book to its author w/o knowing their ids yet, which explicitly avoids the annoyance he mentions of doing a partial commit/going to the db to figure out "what value should I INSERT into in the book.author_id column?" (but my author is new) in the middle of your business logic that just wants to "create books".

- transactions ==> agreed that "transactions via annotations" ala JPA/Hibernate are terrible, but afaiu all "internet scale" apps these days do reads outside of transactions, and just use op-locking during the singular flush/commit step to the db.

Disclaimer I am sure I won't change anyone's minds :-)

Edit: in the HN comments, we're debating "the best way to generate SQL", which is fine, but imo it overlooks the biggest value for ORMs: enforcing business invariants.

I.e. yes a simple INSERT is trivial is write, "why have the ORM to that!", but are you going to enforce the same business logic in the 10 places you do `INSERT authors` in your codebase? And if the answer is "I write an single `insertAuthor` abstraction to enforce this" then you're half-way to writing an adhoc half-specified, bug-riddled version of what a reactive ORM like Joist will do for you. [2] :-)

[1] https://joist-orm.io/

[2] https://joist-orm.io/modeling/why-entities/

This is exactly why I hate ORMs. As I always put it "ORMs make the easy stuff slightly easier, and they make the harder stuff way harder".

If you're just using an OEM for the "select * from table where ID in ...", then you're saving practically nothing by using an ORM - just learn to write SQL, because as you put it, you're going to have to use it anyway for places where it falls over. There are lighter weight options that do basic stuff like transaction management and binding result sets to object properties that are much less of a PITA than ORMs.

In practice I've seen people try to use the ORM features first for places that need complicated SQL (which is a reasonable assumption), only to waste a boatload of time before concluding the ORM makes stuff harder.

I.e. regardless of how easy it is to write `INSERT authors (...) VALUES (...)`, with an appropriately cute/ergonomic query builder to bind the variables/POJOs ... where does your business logic actually go?

Whenever you insert an author, are you always enforcing the same validation logic? Whenever you update a book, are you always updating the derived fields that need updated?

Getting the business rules right is "the actual hard stuff" imo, and nothing I've seen a query builder help with; it's always left as an exercise to the reader to reinvent their "business logic wrapped around POJOs" adhoc in their codebase.

Query builders like these are my personal favorite from a productivity perspective! The point of a query builder is to dynamically build SQL statements that have many subtle variations (do we want to filter by EmailID or PhoneID here? What about a subquery? Did the caller want all results, or just results where $field=X?). They're basically one level above string templating for SQL generation, and often have niceties around ser/de and transaction management as you mentioned.

Because they are primarily about query generation, it feels _very_ natural to pop off the hood and write raw queries directly when necessary. You can usually use the transaction management and ser/de parts with raw queries, too.

My personal favorite in this field is knex.js.

It's like people can't just let go.

Confused at what you’re evening trying to say here. Are you suggesting that 100 lines of application layer code is easier to understand than 15 lines of SQL?

ORM is ultimately SQL

Are you dumb or are you just pretending? I’m going to guess the former!

my experience is the exact opposite. People who love and advocate the merits of ORM insist that everything be executed through ORM because it introduces too much complexity for them to blend handwritten SQL with the ORM generated queries

There's no (good) ORM that doesn't let you simply put your own query in.

I get the first part, but not the second.

Preferring to use SQL rather than an ORM + SQL is all about understanding the subject matter, which is the data as it exists in the database.

> The tldr is if you're ever concatenating strings in order to build a query you're just doing what the entire job of orm is but rolling your own and chances are you'll end up with a bunch of bugs in how you handle well.... Everything.

Yeah, so basically don't do this, except when you have to, like concatenating placeholders for a variable size IN query.

There's some classes of applications where it's hard to write all the queries because there's all sorts of mix and match stuff happening. Those are pretty much doomed to poor performance if the tables are large, so I would rather not play on those teams. On the bright side, the limit of a small table gets bigger every ram generation, and table scans on nvme aren't so painful either.

https://the-php-bench.technex.us/runs/1

But the speed is irrelevant as long as it's good enough. Notice Laravel's Eloquent at the bottom of the list yet thousands of projects are being built with it regularly.

The tldr is if you're ever concatenating strings in order to build a query you're just doing what the entire job of orm is but rolling your own and chances are you'll end up with a bunch of bugs in how you handle well.... Everything.

Ok then!

I definitely don't agree with the "all queries must be executed through the ORM", and think that dogmatic stance has done a lot of damage to the ORM brand. :-/

From my experience, you are mistaken on that. Those queries mostly come with some joins, either necessary or not to represent the object, and that often could be avoided if the data wasn't mapped into some standard object.

The issue is, your lowest value queries are always this type, then you get the 10-20 in any code base that are 100x more complex, and they are the ones your end users care about the most.

You end up with a 80/20 principal in the wrong way, it's great at producing queries that represent 20% of the value of your app, and awful for the 80% that define the core value of it.

I've always heard a major selling point of ORMs is "You don't have to write the actual SQL anymore"

Because of that, I tend to not trust people who use ORMs to even know how to write queries by hand in the first place

My nuanced articulation is "you don't have to write the _boilerplate_ SQL for the 90% of just-do-some-CRUD endpoints in your enterprise SaaS application, but you 100% need to 'know SQL' for the last 5-10% of ~reporting/analytics queries that the ORM is going to mess up".

But that's just me

These are simply tools. The only wrong opinion is to believe that there’s a strict superiority of one over another. However, the content of this and other blogs can help people make informed decisions on when to reach for each tool.

2026: people respond with indignance that they should have to learn anything now that there's a shortcut

But the current shortcut du jour is pretty damn good at writing SQL.

I enjoy this article[0] about some of the persistent warts which will seemingly never change.

[0]https://www.geldata.com/blog/we-can-do-better-than-sql

Once you hit a certain level of complexity in your queries, you're better of with SQL. It's not that you can't do the query in the ORMs, but you're then looking at learning their special query language and those are never better nor easier to understand than just SQL. Those ORM query languages certainly aren't transferable across ORMs, but SQL frequently is. If you can query MariaDB with SQL, you can query SQLServer and PostgreSQL. The same can't be said for e.g. Django vs. Hibernate.

For the "give me all the entries, with this one property" ORMs a much quicker and easier to work with. Once you start needing to use subselect, multiple joins, weird ranges or constructing object with data from across tables, I'd rather just write the SQL myself.

We do programmers always need a library?

Program the damn thing.

There is nothing that an ORM can do to help with this sort of problem without reaching for the obvious escape hatch of arbitrary command text execution. The ability to map the tables to objects in my programming environment is a distracting clown show for this specific problem. What really matters is understanding the provider and its techniques for bulk loading records. No ORM will ever be able to touch these provider capabilities on their "happy" paths. At best you'll wind up using the ORM and a bunch of provider-specific SQL anyways.

ORMs for schema management is a stronger argument, but only in cases where the codebase/service has complete ownership over each respective database. Any kind of heterogenous workload says that ORM for schema management is a potential nightmare unless you do something like create a project that is only for migrating the schema, at which point I'd argue you could just maintain a source controlled folder of sql/shell scripts.

If you don't use an ORM, you'll end up with more boilerplate from mapping code with DTOs. The reason to use an ORM is dirty checking. It's hard to impose this kind of "state" with a relational database. But fundamentally, relational data doesn't fit well with OOP. In the end, you inevitably have to create a layer that absorbs this mismatch. Both approaches have their pros and cons anyway.

Isn't it just a matter of using it where it fits and not using it where it doesn't? I wonder if we really have to frame it as "never use this" or "always use that."

Actually, on second thought, I take it back. "Right tool for the right place" is harder. If you're on a team, it's probably better to just pick one: either don't use it at all, or use it everywhere. Because either way, friction is going to happen. My earlier thinking was too shallow.

So I think the ORM debate could be over

postgresql is a beast

I'm talking about my experience, not generalizing to all DBAs of course. And of course ORMs introduced performance issues, etc.

NoSQL for operational data storage is more efficient and cost effective.

ORMs were a regression test that exposed unnecessary complexity.