pggen VS honeysql

Cornucopia is neat. I wrote a similar library in Go [1] so I'm very interested in comparing design decisions.

The pros of the generated code per query approach:

- App code is coupled to query outputs and inputs (an API of sorts), not database tables. Therefore, you can refactor your DB without changing app code.

- Real SQL with the full breadth of DB features.

- Real type-checking with what the DB supports.

The cons:

- Type mapping is surprisingly hard to get right, especially with composite types and arrays and custom type converters. For example, a query might return multiple jsonb columns but the app code wants to parse them into different structs.

- Dynamic queries don't work with prepared statements. Prepared statements only support values, not identifiers or scalar SQL sub-queries, so the codegen layer needs a mechanism to template SQL. I haven't built this out yet but would like to.

[1]: https://github.com/jschaf/pggen

pggen is another fantastic library in this genre, which specifically targets postgres. It is driven by pgx. Can not recommend enough.

> Do you write out 120 "INSERT" statements, 120 "UPDATE" statements, 120 "DELETE" statements as raw strings

Yes. For example: https://github.com/jschaf/pggen/blob/main/example/erp/order/....

> that is also using an ORM

ORM as a term covers a wide swathe of usage. In the smallest definition, an ORM converts DB tuples to Go structs. In common usage, most folks use ORM to mean a generic query builder plus the type conversion from tuples to structs. For other usages, I prefer the Patterns of Enterprise Application Architecture terms [1] like data-mapper, active record, and table-data gateway.

[1]: https://martinfowler.com/eaaCatalog/

You might like pggen (I’m the author) which only supports Postgres and pgx. https://github.com/jschaf/pggen

pggen occupies the same design space as sqlc but the implementations are quite different. Sqlc figures out the query types using type inference in Go which is nice because you don’t need Postgres at build time. Pggen asks Postgres what the query types are which is nice because it works with any extensions and arbitrarily complex queries.

Any reason to use sqlc over pggen ? If you use Postgres, it seems like the superior option.

Agree with your choices, except go-json which I never tried. pggen is fantastic. Love that library. The underlying driver, pgx, is also really well written.

You might like the approach I took with pggen[1] which was inspired by sqlc[2]. You write a SQL query in regular SQL and the tool generates a type-safe Go querier struct with a method for each query.

The primary benefit of pggen and sqlc is that you don't need a different query model; it's just SQL and the tools automate the mapping between database rows and Go structs.

[1]: https://github.com/jschaf/pggen

[2]: https://github.com/kyleconroy/sqlc

I created pggen a few weeks ago to create my preferred method of database interaction: I write real SQL queries and I use generated, type-safe Go interfaces to the queries. https://github.com/jschaf/pggen

I recall using korma way back I and I don’t recall it being terrible but I would say https://github.com/seancorfield/honeysql has very much superseded it by this point… (but I can see how that might not be obviously clear if one is to look at superficial metrics like GitHub stars for example…)

Joins can certainly work in a data format like YAML. For an example, see Honey SQL from the Clojure community [0] (though without something to contrast strings like Clojure's keywords, you miss out on the automatic parameterization).

You mentioned moving JOINs around, so I'll mention that if represented as structured data, you can move any of the top level components around, so you could more closely follow the "true order of SQL" [1]. For example, I would love to be able to put FROM before SELECT in all or almost all cases. There's also being able to share and add to something like a complicated WHERE clause, where essentially all programming languages have built-in facilities for robustly manipulating ordered and associative data compared to string manipulation, which is not well-suited for the task.

Now don't get me wrong, I don't particularly care for YAML (though it doesn't bother me that much), but as someone who's done their fair share of programmatic SQL creation and manipulation in strings, not having a native way to represent SQL as data is a mistake in my opinion.

0: https://github.com/seancorfield/honeysql#big-complicated-exa...

In Clojure-land, we are also using HoneySQL [1] which has similar characteristics. You are still working within SQL semantics so it's a bit more complicated, but we are doing great complicated things with just maps, no API necessary.

[1] https://github.com/seancorfield/honeysql

using honeysql we can make structured queries as well

As someone else already pointed out, next.jdbc is good for database connectivity (for Postgres and beyond). For composing the queries themselves, I strongly recommend Honey SQL. It lets you represent queries themselves as normal Clojure data structures, just vectors and maps.

Perhaps you're looking for a way of arranging SQL as an AST represented by data structures (or objects) that can be fed to a compiler. HoneySQL[0] is one such implementation of this idea and it makes your general transformation trivial for Clojure programs. You don't need to mess around with string concatenation because you have a predictable and extensible compiler for data structures (which are themselves easily composable/transformable/storable with Clojure) that you can trust to do the right thing. If you're using some weird database or need an esoteric syntax, extending the compiler to your clause is easy to do[1].

[0] https://github.com/seancorfield/honeysql

[1] https://github.com/seancorfield/honeysql#extensibility

https://github.com/seancorfield/honeysql (write SQL without having to write SQL)

I've used it from Clojure, via HoneySQL, so no ORM, no danger of SQL injection. It was really wonderful!

https://github.com/seancorfield/honeysql

I used it to quickly iterate on the development of migration SQL scripts for a MySQL DB, which was running in production on RDS.

I might have switched to H2 DB later, because that was more compatible with MariaDB, but I could use the same Clojure code, representing the SQL queries, because HoneySQL can emit different syntaxes.