crunchy-proxy VS xo

This is why I always recommend using a postgres proxy so you can point it to different database servers as you stand them up. AWS does this by default but GCP doesn’t yet, so you have to have your own Postgres proxy (e.g https://github.com/CrunchyData/crunchy-proxy) or move to an HA Postgres setup like Patroni that has streaming replication (https://github.com/zalando/patroni).

I use xo https://github.com/xo/xo . It generates CRUD queries by default so i don't have to write basic queries and it has option to write complex queries like sqlx. Only issue is it is not well documented.

SQL is an old, irregular language to work with, but it is better known than HCL and SQL already has it's own Pulumi/CDK in the form of every ORM with introspection (like Javascript's Prisma, Python's Django, Go's XO etc) and QueryBuilder (LINQ, Knex, etc) in whatever programming language you prefer. You probably already know it.

Sorry for the confusion, we're not generating the database client itself, more like generating an ORM around a database client. The ORM takes these database clients as dependencies. It's very similar to the way XO works with it's multi-database support: https://github.com/xo/xo/tree/master/_examples/northwind

i use xo . Reason is i prefer designing schema first and creating golang scaffolding later. Xo takes schema and gives me basic create/update/delete operations by default and i can also generate gocode for any sql queries that i write.

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.

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