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Curious to hear battle stories from other teams using this.
Each can do the other, to a limited extent, but it becomes increasingly difficult with even small increases in complexity. For instance, you can do inferencing in SQL, but it is almost entirely manual in nature and not at all like the automatic forward-inferencing of Prolog. And yes, you can store data(facts) in Prolog, but it is not at all designed for the "storage, retrieval, projection and reduction of Trillions of rows with thousands of simultaneous users" that SQL is.
I even wanted to implement something like Logica at the moment, primarily trying to build a bridge through a virtual table in SQLite that would allow storing rules as mostly Prolog statements and having adapters to SQL storage when inference needs facts.
Google is pushing the new language Logica to solve the major flaws in SQL - https://news.ycombinator.com/item?id=29715957 - Dec 2021 (1 comment)
Logica, a novel open-source logic programming language - https://news.ycombinator.com/item?id=26805121 - April 2021 (98 comments)
The basic selling point is a compositional query language, so that over-time one may have a library of re-usable components. If anyone really has built such a library I'd love to know more about how it worked out in practice. It isn't obvious to me how those decorators are supposed to compose and abstract on first look.
Its also not immediately obvious to me how complicated your library of SQL has to be for this approach to make sense. Say I had a collection of 100 moderately complex and correlated SQL queries, and I was to refactor them into Logica, in what circumstances would it yield a substantial benefit versus (1) doing nothing, (2) creating views or stored procedures, (3) using DBT / M4 or some other preprocessor for generic abstraction.