pggen VS go

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

There used to be the GO FIPS branch :

https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...

But it looks dead.

And it looks like https://github.com/golang-fips/go as well.

I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:

- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644

- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412

Here are some excerpts from the latest Go survey [1]:

- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."

- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."

I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.

[1]: https://go.dev/blog/survey2024-h1-results

Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements

Make sure you have Go installed https://go.dev/.

The Go Programming Language

When Go first shipped, it was already well-documented that the only stable ABI on some platforms was via dynamic libraries (such as libc) provided by said platforms. Go knowingly and deliberately ignored this on the assumption that they can get away with it. And then this happened:

https://github.com/golang/go/issues/16606

If that's not "getting burned", I don't know what is. "Trying to provide a nice feature" is an excuse, and it can be argued that it is a valid one, but nevertheless they knew that they were using an unstable ABI that could be pulled out from under them at any moment, and decided that it's worth the risk. I don't see what that has to do with "not being as broadly compatible as they had hoped", since it was all known well in advance.