migra VS pggen

Forr postgres, how does the schema diffing aspect compare to migra?

https://github.com/djrobstep/migra

I'm asking because, although migra is excellent and there are multiple migrations tools based on it (at least https://github.com/bikeshedder/tusker and https://github.com/blainehansen/postgres_migrator), issues are piling up but development seem to be slowing down

We’ve extended the CLI migration feature and added Dashboard support. Database migrations give you a way to update your database using version-controlled SQL files. We’ve built a lot of tooling around our migrations, including reparation, migration cleanup using the squash command, and diffing (using migra) to generate a new migration or to detect schema drift.

I've had a lot of success with: https://github.com/djrobstep/migra

Is this similar to migra? There's a tool written in Rust that calls it, postgres_migrator (there's also tusker)

If you wish to auto-generate migrations, there are declarative schema change tools available for most relational databases. I'm the creator of Skeema [1] which provides them for MySQL, but there are options for other DBs too [2][3][4].

Prisma's migration system actually partially copied Skeema's design, while giving credit in a rather odd fashion which really rubbed me the wrong way: "The workflow of working with temporary databases and introspecting it to determine differences between schemas seems to be pretty common, this is for example what skeema does." [5]

While I doubt I was the first person to ever use that technique, I absolutely didn't copy it from anywhere, and it was never "pretty common". I'm not aware of any other older schema change systems that work this way.

[1] https://www.skeema.io

[2] https://github.com/djrobstep/migra

[3] https://github.com/k0kubun/sqldef

[4] https://david.rothlis.net/declarative-schema-migration-for-s...

[5] https://github.com/prisma/prisma-engines/blob/6be410e/migrat...

The best solution I've ever seen is this Rust library https://github.com/cornucopia-rs/cornucopia

You write plain SQL for you schema (just a schema.sql is enough) and plain SQL functions for your queries. Then it generates Rust types and Rust functions from from that. If you don't use Rust, maybe there's a library like that for your favorite language.

Optionally, pair it with https://github.com/bikeshedder/tusker or https://github.com/blainehansen/postgres_migrator (both are based off https://github.com/djrobstep/migra) to generate migrations by diffing your schema.sql files, and https://github.com/rust-db/refinery to perform those migrations.

Now, if you have simple crud needs, you should probably use https://postgrest.org/en/stable/ and not an ORM. There are packages like https://www.npmjs.com/package/@supabase/postgrest-js (for JS / typescript) and probably for other languages too.

If you insist on an ORM, the best of the bunch is prisma https://www.prisma.io/ - outside of the typescript/javascript ecosystem it has ports for some other languages (with varying degrees of completion), the one I know about is the Rust one https://prisma.brendonovich.dev/introduction

I really liked the idea proposed in https://github.com/djrobstep/migra but haven’t used it yet.

I'd love to be able to be able to declaratively make changes directly in the table create commands instead of manually creating new migration scripts every time. I've found migra (we use PostgreSQL) and it seems to be exactly what I'm looking for. I'm curious about other people's experience and why things like Migra are the norm.

Migra should do it https://databaseci.com/docs/migra

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