Logica – Declarative logic programming language for data

Datalog is not really a query language, actually. But it is relational, like SQL, so it lets you express relations between "facts" (the rows) inside tables. But it is more general, because it also lets you express relations between tables themselves (e.g. this "table" is built from the relationship between two smaller tables), and it does so without requiring extra special case semantics like VIEWs.

Because of this, it's easy to write small fragments of Datalog programs, and then stick it together with other fragments, without a lot of planning ahead of time, meaning as a language it is very compositional. This is one of the primary reasons why many people are interested in it as a SQL alternative; aside from your typical weird SQL quirks that are avoided with better language design (which are annoying, but not really the big picture.)

If I understand you correctly, you can easily get the same with ephemeral models in dbt or CTEs generally?

> Because of this, it's easy to write small fragments of Datalog programs, and then stick it together with other fragments, without a lot of planning ahead of time, meaning as a language it is very compositional.

This can be a benefit in some cases, I guess, but how can you guarantee correctness with flexibility involved?

With SQL, I get either table or column level lineage with all modern tools, can audit each upstream output before going into a downstream input. In dbt I have macros which I can reuse everywhere.

It's very compositional while at the same time perfectly documented and testable at runtime.

Could you share a more specific example or scenario where you have seen Datalog/ Logica outperform a modern SQL setup?

Generally curious.

I am not at all familiar with the Logica/Datalog/Prolog world.

It also frames your thinking about defining what you want rather than how to get it.

If you really want to see the power of these kinds of languages look up Einstein's puzzle solved with prolog. The solution just magically comes out by entering the constraints of the puzzle.

Since Logica is translated to SQL it should benefit from all the query optimistic goodness that went into the SQL engine that runs the resulting queries.

I personally see the disadvantages of SQL in that it is not really modular, you cannot have libraries, tests and such.

Disclosure: I wrote Mangle (the link goes to the Mangle repo), another datalog, different way of extending, no SQL translation but an engine library.

But: "Logica compiles to SQL".

With the caveat that it only kind of does, since it seems constrained to three database engines, probably the one they optimise the output to perform well on, one where it usually doesn't matter and one that's kind of mid performance wise anyway.

In light of that quote it's also weird that they mention that they are able to run the SQL they compiled to "in interactive time" on a rather large dataset, which they supposedly already could with SQL.

Arguably I'm not very good with Datalog and have mostly used Prolog, but to me it doesn't look much like a Datalog. Predicates seems to be variadic with named parameters, making variables implicit at the call site so to understand a complex predicate you need to hop away and look at how the composite predicates are defined to understand what they return. Maybe I misunderstand how it works, but at first glance that doesn't look particularly attractive to me.

Can you put arithmetic in the head of clauses in Datalog proper? As far as I can remember, that's not part of the language. To me it isn't obvious what this is supposed to do in this query language.

You can bolt on any number of 3rd party features or extensions to get some extra thing, that goes for any tool in the world. The point of something like Datalog is that it can express a similar class of relational programs that SQL can, but with a smaller set of core ideas. "Do more with less."

> I guess, but how can you guarantee correctness with flexibility involved?

How do you guarantee the correctness of anything? How do you know any SQL query you write is correct? Well, as the author, you typically have a good idea. The point of being compositional is that it's easier to stick together arbitrary things defined in Datalog, and have the resulting thing work smoothly.

Going back to the previous example, you can define any two "tables" and then just derive a third "table" from these, using language features that you already use -- to define relationships between rows. Datalog can define relations between rules (tables) and between facts (rows), all with a single syntactic/semantic concept. While SQL can only by default express relations between rows. Therefore, raw SQL is kind of "the bottom half" of Datalog, and to get the upper half you need features like CTEs, VIEWs, etc, and apply them appropriately. You need more concepts to cover both the bottom and top half; Datalog covers them with one concept. Datalog also makes it easy to express things like e.g. queries on graph structures, but again, you don't need extra features like CTEs for this to happen.

There are of course lots of tricky bits (e.g. optimization) but the general idea works very well.

> Could you share a more specific example or scenario where you have seen Datalog/ Logica outperform a modern SQL setup?

Again, Datalog is not about SQL. It's a logic programming language. You need to actually spend time doing logic programming with something like Prolog or Datalog to appreciate the class of things it can do well. It just so happens Datalog is also good for expressing relational programs, which is what you do in SQL.

Most of the times I'm doing logic programming I'm actually writing programs, not database queries. Trying to do things like analyze programs to learn facts about them (Souffle Datalog, "can this function ever call this other function in any circumstance?") or something like a declarative program as a decision procedure. For example, I have a prototype Prolog program sitting around that scans a big code repository, figures out all 3rd party dependencies and their licenses, then tries to work out whether they are compatible.

It's a bit like Lisp, in the sense that it's a core formulation of a set of ideas that you aren't going to magically adopt without doing it yourself a bunch. I could show you a bunch of logic programs, but without experience all the core ideas are going to be lost and the comparison would be meaningless.

For the record, I don't use Logica with SQL, but not because I wouldn't want to. It seems like a good approach. I would use Datalog over SQL happily for my own projects if I could. The reasons I don't use Logica for instance are more technical than anything -- it is a Python library, and I don't use Python.

> making variables implicit at the call site

What example are you looking at? The NewsData example for instance seems pretty understandable to me. It seems like for any given predicate you can either take the implicit name of the column or you can map it onto a different name e.g. `date: date_num` for the underlying column on gdelt-bq.gdeltv2.gkg.

Really it just seems like a way to make the grammar less complicated; the `name: foo` syntax is their way of expressing 'AS' clauses and `name:` is just a shorthand for `name: name`

> In light of that quote it's also weird that they mention that they are able to run the SQL they compiled to "in interactive time" on a rather large dataset, which they supposedly already could with SQL.

The query in question is run on BigQuery (which IIRC was the original and only target database for Logica), and in that setup you might do a query over 4TB of data but get a response in milliseconds due to partitioning, column compression, parallel aggregation, etc. This is actually really common for many queries. So, in that kind of setup the translation layer needs to be fast so it doesn't spoil the benefit for the end user. I think the statement makes complete sense, tbh. (This also probably explains why they wrote it in Python, so you could use it in Jupyter notebooks hooked up to BigQuery.)

"Anyone who know the system can easily learn it" he said with a sniff.

Yes, the similarity to Prolog lets you draw on a vast pool of Prolog programmers out there.

I mean, I studied a variety of esoteric languages in college and they were interesting (I can't remember if we got to prolog tbh but I know 1st logic pretty well and that's related). When I was thrown into a job with SQL, it's English language syntax made things really easy. I feel confident that knowing SQL wouldn't oppositely make learning Prolog easy (I remember Scala later and not being able to deal with it's opaque verbosity easily).

Basically, SQL syntax makes easy things easy. This gets underestimated a lot, indeed people seem to have contempt for it. I think that's a serious mistake.

The flip side of that is SQL makes hard things nearly impossible.

SQL doesn't have facilities for abstraction, and it doesn't compose, and this has consequences that I deal with daily.

The lack of abstract facilities makes it hard to construct complicated queries, it makes it hard to debug them, and it makes it hard refactor them.

Instead of writing more complicated SQL queries, developers lean on the host languages to coordinate SQL calls, using the host language's abstraction facilities to cover for SQL's inadequacies.

A good exercise might be converting it to the corresponding SQL and comparing the two for clarity.

I don't really get this kind of complaint in general I'm afraid. Many people can read and write, say, Hangul just fine -- and at the same time we don't expect random English speakers with no familiarity will be able to understand Korean conversations, or any syllabic writing systems in general. Programming language families/classes like logic programming are really no different.

> it's English language syntax made things really easy

That's just called "being familiar with English" more than any inherent property of SQL or English.

What about SQL syntax makes the hard things possible? I get that the actual language SQL is broken in all sorts of ways. But I don't see any reason to replace it with some opaque from get-go.

I mean, what stops you from defining, say adjectives and using those for rough modularity.

Say

    EXPENSIVE(T) means T.price > 0;
    Select name FROM books WHERE EXPENSIVE(books); 

In particular many people talk a lot about concerns like optimizations across GraphQL plans and how they are expected to behave on underlying tables, but this is something that I think has seen a lot of research in the Datalog realm. And to top it off, even ignoring that, Datalog just feels much more natural to write and read after a bit of practice, I think. (Obviously you need to be in the pure fragment of datalog without recursion, but even then it might be feasible to add those features with termination criteria even if it's just "decrement an internal counter and if it hits zero throw a big error")

What do you think the plans for the Rust implementation will be? That's probably the most likely place I'd use it, as I don't really use Go that much.

The main advantage to any non SQL language is its ability to more efficiently express recursion (graph / hierarchical queries) and dynamic expressions like transposition and pivots.

You can do those in SQL it's just clunky.

The combination of top-down and bottom up logic programming is interesting, especially when one can move work between pre computation and query time.

I like that optimizing queries in datalog can be discussed like optimization of programming language but of course the biggest gains in DB come from join order and making use of indices. There is a tension here between declarative and having some control or hints for execution. I haven't yet figured out how one should go about it, and also how to help programmers combine top-down and bottom-up computation. Work in progress! :-)

Are you aware of any SQL transpilers that spend so much time transpiling that you get irritated? I'm not.

    WITH expensive AS (SELECT * FROM books WHERE price > 100)
    SELECT name FROM expensive

> Are you aware of any SQL transpilers that spend so much time transpiling that you get irritated? I'm not.

Again, when you are running a tool on something that returns results in ~millisecond time, it is important the tool does not spoil that. Even 100-200ms is noticeable when you're typing things out. They could have worded it differently, it's probably just typical "A programmer wrote these docs" stuff, so it's just bad copy. A dedicated technical writer would probably do something different.

If there weren't deficiencies then you wouldn't need to define more syntax, and so many work-arounds wouldn't have already been created. The deficiencies in SQL are why each big SQL database ends up creating some procedural-SQL language for use in stored procedures and triggers.

CTEs are close to what you outline above, but even then (as far as I know) you can't name that CTE and use it across multiple statements.