xo VS goqu

Thanks for sharing your thoughts! I see that it works best with Postgresql. The other commenter mentioned https://github.com/xo/xo for MySql which might work well.

Thanks for your comment and question @onionisfruit. Top-notch handle too!

>> What are your plans for models and persistence?

I haven't worked out all the details, but it's going to be some blend of https://github.com/xo/xo and https://sqlc.dev/.

Design goals:

1. High-level, type-safe "ORM" that's generated from your database schema.

Something like that? https://github.com/xo/xo

I'm a big fan of the database first code generator approach to talking to an SQL database, so much so that I wrote pggen[1] (not to be confused with pggen[2], as far as I can tell a sqlc fork, which I just recently learned about).

I'm a really big partisan of this approach, but I think I'd like to play the devil's advocate here and lay out some of the weaknesses of both a database first approach in general and sqlc in particular.

All database first approaches struggle with SQL metaprogramming when compared with a query builder library or an ORM. For the most part, this isn't an issue. Just writing SQL and using parameters correctly can get you very far, but there are a few times when you really need it. In particular, faceted search and pagination are both most naturally expressed via runtime metaprogramming of the SQL queries that you want to execute.

Another drawback is poor support from the database for this kind of approach. I only really know how postgres does here, and I'm not sure how well other databases expose their queries. When writing one of these tools you have to resort to tricks like creating temporary views in order infer the argument and return types of a query. This is mostly opaque to the user, but results in weird stuff bubbling up to the API like the tool not being able to infer nullability of arguments and return values well and not being able to support stuff like RETURNING in statements. sqlc is pretty brilliant because it works around this by reimplementing the whole parser and type checker for postgres in go, which is awesome, but also a lot of work to maintain and potentially subtlety wrong.

A minor drawback is that you have to retrain your users to write `x = ANY($1)` instead of `x IN ?`. Most ORMs and query builders seem to lean on their metaprogramming abilities to auto-convert array arguments in the host language into tuples. This is terrible and makes it really annoying when you want to actually pass an array into a query with an ORM/query builder, but it's the convention that everyone is used to.

There are some other issues that most of these tools seem to get wrong, but are not impossible in principle to deal with for a database first code generator. The biggest one is correct handling of migrations. Most of these tools, sqlc included, spit out the straight line "obvious" go code that most people would write to scan some data out of a db. They make a struct, then pass each of the field into Scan by reference to get filled in. This works great until you have a query like `SELECT * FROM foos WHERE field = $1` and then run `ALTER TABLE foos ADD COLUMN new_field text`. Now the deployed server is broken and you need to redeploy really fast as soon as you've run migrations. opendoor/pggen handles this, but I'm not aware of other database first code generators that do (though I could definitely have missed one).

Also the article is missing a few more tools in this space. https://github.com/xo/xo. https://github.com/gnormal/gnorm.

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

I've used https://github.com/xo/xo, extended it with some custom functions for templating, extended the templates themselves, and can now generate CRUD for anything in the database, functions for common select queries based on the indices that exist in the database, field filtering and scanning, updates for subsets of fields including some atomic operations, etc. The sky is the limit honestly. It has allowed me to start with something approximating a statically generated ORM and extend it with any features I want as time goes on. I also write .extra.go files along side the generated .xo.go files to extend the structs that are generated with custom logic and methods to convert data into response formats.

I like the approach of starting with the database schema and generating code to reflect that. I define my schema in sql files and handle database migrations using https://github.com/golang-migrate/migrate.

If you take this approach, you can mostly avoid exposing details about the SQL driver being used, and since the driver is mostly used by a few templates, swapping drivers doesn't take much effort.

Here is an example of the MySQL Django tables generated by GORM https://github.com/xo/xo/tree/master/examples/django/mysql

I use https://github.com/doug-martin/goqu to create SQL queries. In the hope we can make it a bit more 'platform independant'

This one I have less of an opinion on. It depends on what kind of users you are targeting. The rabbit hole here is deep and requires real thought on how you want to structure your library.

You may want to look at Goqu, which is a very mature library offering much more comprehensive query builder functionality.

I'm happy with goqu: https://github.com/doug-martin/goqu

This is the approach I've taken took, though instead of writing new helpers, I've preferred to use goqu. I've only been using the query building features, but it is excellent for it.

https://github.com/doug-martin/goqu for building SQL queries. Supports MySQL and Postgres at least - super handy!

This looks better than typical ORMs, but still not giving me what I want.

I want query objects to be composable, and mutable. That lets you do things like this: http://btubbs.com/postgres-search-with-facets-and-location-a.... sqlc would force you to write a separate query for each possible permutation of search features that the user opts to use.

I like the "query builder" pattern you get from Goqu. https://github.com/doug-martin/goqu

github.com/doug-martin/goqu - SQL query builder. Don't like ORMs in general, didn't like GORM. The API is a bit verbose but it does the job and supports tons of SQL features including database specific ones.

Depends on what you mean by ORM. Does query builders qualify as ORM? I tried GORM and didn't like it. Currently using goqu on several projects and like it very much. Use it as plain SQL builder for pgx connector. I have all the power of SQL but without fiddling with strings and bindings. Don't mind scanning manually without reflection.

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).