cpimhoff

All great advice. All this resonated with the team.

100% on decoupling it from the product. Our customers needed a headless BI solution and we needed a better internal framework, so the stars aligned for this first version being a little coupled. This kind of feedback is helpful, because it helps me advocate for capacity to decouple it fully!

Hashquery does have APIs to get the rendered SQL without executing it, so I think integration with any in-warehouse processing tool is possible, dbt likely being the most valuable.

Full materialization/interoperability with SQL is hard for any tool trying to encapsulate the semantics of the SQL. The intent of those tools is to encode concepts that are not possible to represent with static tables, so folding them back to a SQL-surface area will always be lossy. Having said that, we could certainly have a better story around it — materialization can still be useful even if it has some caveats.

> 1. How does this compare to dbt? If we're already using dbt, why migrate?

I actually think dbt and Hashquery work very well alongside one another!

dbt can help you normalize, clean, and materialize your data upstream, then Hashquery can be used to associate semantics to those output tables (measurements, synthesized attributes, common views) and query them.

So dbt can be the build/ETL part, and Hashquery can be the semantic layer/frontend for analytical queries.

> 2. Will you consider making a tool that tries to transpile SQL back to Hashquery models?

This is a really interesting idea!

I'm pretty skeptical we could make this technically feasible. Compilation from a higher level abstraction (Hashquery semantics) to a lower level abstraction is inherently lossy and can't really be done in reverse without a lot of noise.

Hashquery has a lot of escape hatches for raw SQL though if you need to access some functionality not yet implemented as part of the project. There's API to inline Hashquery structures inside of SQL fragments and visa versa.

The library itself provides tools for declaring data tables, columns, metrics, and relationships, then performing transformations (eg. queries) on top of them.

Reflecting an existing database isn't part of the library, but with the magic of "it's just Python" it's pretty easy to write a function that does so yourself:

    import hashquery as hq
    def reflect_db(url: str) -> list[hq.Model]:
      """
      Given a connection string,
      returns a list of Hashquery models
      for all the physical tables in
      the database.
      """
      models: list[hq.Model] = []

      reflection = some_reflection_lib.reflect_db(url)
      for table in reflection.tables:
        models.append(
          hq.Model()
          .with_source(url, table.name, table.schema)
          .with_attributes(*table.column_names)
        )
        # ...more logic for importing foreign keys or whatever else

      return models

> way beyond the capabilities of standard SQL

Maybe the more precise language would be "way beyond the _expressive_ capabilities of standard SQL". Ultimately Hashquery compiles into SQL for execution, in that way it's a bit of a transpiler.

One concrete example is funnel analysis. In SQL, an efficient funnel analysis on large data sets will span several hundred lines of pipelined queries; writing, reading, and re-parameterizing this is very challenging. In Hashquery, it's abstracted into a single function call: `model.funnel(*str[])`.

> AI and LLMs (coming soon)

Frankly? SEO just told us to drop "AI" _somewhere_ on the page for new `.dev` website. I agree it's a little silly to make it a top-line bullet point.

We do think Hashquery is better suited for coding co-pilot tools, as it's a known language, and you can establish API boundaries that an AI tool won't break into and futz with (good for writing queries on top of already defined business logic).

Bit of context here is that Hashquery started as an internal tool and as a way to power our headless BI offering, so it still has a few bits tied to our infrastructure we're working to fully separate.

For folks wanting to hack around more with Hashquery in the meantime, Hashboard accounts can be created for free and we don't have plans to charge folks just using the Hashquery stuff (but we probably will bother you for feedback ). You can sign up here https://hashboard.com/getAccess