SqlKata Query Builder VS pggen

SqlKata is your friend.

Don't know if can be a good pick for the no-ORM requirement but I would take a look at SqlKata which is a nice query builder + execution engine, built on top of Dapper

Not really an ORM. But I have been enjoying SqlKata recently. Works with Dapper but helps reduce SQL strings and makes things like pagination really easy. Also nice for dynamic filters.

That is basically the description of an object mapper, with all the guarantees of an object mapper :). It seems if you actually use the query builder as such, no guarantees exist.

I'm pretty picky regarding query builders and ORM's, to the extent of having written several of them over the years, in different languages (both dynamic and strong typed, unfortunately closed-source). I'm a strong advocate of schema-first design, and usually a query builder will allow you to design your queries explicitly, but having some internal behaviors (such as string concatenation, identifier quoting and automatic in-order separation of parameters and values to be bound) taken care of. As good examples of this, I'd mention golang's goqu (https://github.com/doug-martin/goqu) and - to some extent - C# SqlKata (https://sqlkata.com/). Following my frustrations with Python ORMs, I built my own toy project, sort-of-in-beta, called rickdb (https://github.com/oddbit-project/rick_db).

Less about the exact syntax and more about the tool, for example: https://github.com/sqlkata/querybuilder. I just chose that since it was on top of a search but the idea is the same. Your code generates raw SQL, so it's 100% interchangeable with writing SQL yourself however the builder library deals with the syntax, proper ordering, quoting, full attribute names, etc. Some such libraries even let you define your schema in code to make your SQL generation type safe.

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