Skimming the original article, I didn't really understand why the author didn't discuss "WITH" CTEs (for SQL newbies, common table expressions, see https://modern-sql.com/feature/with ) as alternative composition mechanisms.
Or even SQL views. But your ergonomics comment makes sense to me.
side note: man i hate how people write sql, drives me nuts how wild west it is syntax wise out in the world. i could wax poetic on how its stinky but when it comes to sql, people are already locked in on how they write sql so they are absolutely unwilling to entertain new approaches there. im literally the only one dying on this hill but using caps all over the place when syntax highlighting exists in 2026 is wild. sql might be the one place in the universe where commas on the left in your select statement makes sense, `on` clause join keys deserve to be on the next line and tabbed in under the thing they join to otherwise you're stuffing join tablename alias on alias.id = b.id (this wouldnt be such an eyesore but people are very not-verbose with their table aliases, so eye scanning tablename/alias/join keys kinda sux)
Say you want to combine a few sets with dynamic where clauses then intersect a couple other sets. For example, you have a "products" API that lets the user pick from a bunch of different filters
It's pretty easy to composite all that together with a decent ORM
Also migration management and having a programmatic DB schema to object schema mapping is very convenient
I do tend to see a lot of bastardized queries, though from treating ORM objects like they're native in memory objects (N+1, using programming functions where SQL equiv would have been more efficient, pulling data back only to dump it into the where clause of another query)
The sweet spot is an ORM that embraces dropping down into raw SQL where needed, instead of forcing bad half-complete DSL everywhere.
>Raw SQL is great for simple queries, but gets old quick when you are dealing with "higher order" parameterized queries (...)
>The sweet spot is an ORM that embraces dropping down into raw SQL where needed {...)
So basically you said that ORM are great only for the sweet spot of "mildy complex query". Because Raw SQL is great for simple query, and can be invoked when the ORM is not enough for highly complex query.
So I'd stick with my strategy of mastering raw SQL. I never felt the need of switching tool specifically for mildy difficult query. These are usually boring repetitive stuff than you can usually abstract away with a stored procedure (or in a external parametrized SQL script if your development guideline is to avoid storing any business logic in the database)
PS : But of course ORM is still very relevant if your application aim to be compatible with different database vendors and you are ok to never optimize query yourself directly beyond what your ORM vendor can provide.
That's the pattern I've seen the most with ORM setups these days. That or dropping performance heavy sql into stored procedures but in the end it's all a matrix of ease of use/maintainability in some scenarios vs full control and performance tuning and what makes sense for that use case.
ORM has substantial utility beyond just abstracting across different backends (I personally think that is one of the worst reasons to use an ORM, migrating or joining across entire production database systems is not something you ever take lightly).
> So I'd stick with my strategy of mastering raw SQL. I never felt the need of switching tool specifically for mildy difficult query.
FWIW this attitude reminds me of devs who insist Haskell or Emacs whatever is the one true technology that can do everything. Hard to work with. They build ivory towers, complicated systems that only the author can enter.
SQL is great but has its limits. Same for ORMs. Sweet spot is in the gray zone.
Tabs or spaces though?
Lots of things that are "just" libraries could also reasonably be thought of as eDSLs.
{"data": [
{"id":1,"value":"foo"},
{"id":2,"value":"bar"},
...
]}
CREATE TABLE my_documents(doc JSONB); SELECT t.id,t.value FROM my_documents CROSS JOIN LATERAL jsonb_to_recordset(doc #> '{data}') AS t(id INTEGER, value TEXT);
id | value
----+-------
1 | foo
2 | bar
IIRC it's really only in a LATERAL JOIN because laterals are the only production rules that let you alias a function call (jsonb_to_recordset()) with explicitly declared column types.
However, it working as a lateral join is critical as you need the function to fire for every row.
Right, what I mean is, for functions in the standard expression position, e.g. `SELECT COALESCE(..., 0)`, those functions will also fire for every row. jsonb_to_recordset however needs to know the schema of the table it will output, and the only way syntactically to declare the column types output by a function returning a recordset is in LATERAL clauses [0].