souffle VS blog

> In fact, we could have used Datalog to achieve our data goals — but that would mean we have to build our own Datalog implementation, backing data store, etc. We don’t want to do that.

Surprising that creating a whole new language made more sense then a backend. I wonder if they did a proof of concept with an existing logic system like Souffle¹ or Rel² first.

¹ https://github.com/souffle-lang/souffle

² https://relational.ai/blog/rel

Consider using Datalog (the incredible subset of Prolog) for this perfect use case. Compared to Prolog, you get:

1. Free de-duplication. No more debugging why a predicate is returning the same result more than once.

2. Commutativity. Order of predicates does not change the result. Finally, true logic programming!

3. Easy static analysis. There are many papers that describe how to do points-to analysis (and other similar techniques) with Datalog rules that fit on a single page :O

Souffle[0] is a mature Datalog that is highly performant and has many nice features. I highly recommend playing with it!

[0] https://souffle-lang.github.io

Still, there are many useful tools based on these ideas, used by programmers and mathematicians alike. What you describe sounds rather like Datalog (e.g. Soufflé Datalog), where you supply some rules and an initial fact, and the system repeatedly expands out the set of facts until nothing new can be derived. (This has to be finite, if you want to get anywhere.) In Prolog (e.g. SWI Prolog) you also supply a set of rules and facts, but instead of a fact as your starting point, you give a query containing some unknown variables, and the system tries to find an assignment of the variables that proves the query. And finally there is a rich array of theorem provers and proof assistants such as Agda, Coq, Lean, and Twelf, which can all be used to help check your reasoning or explore new ideas.

It's true that this SPARQL-inspired view of Datalog as a triplestore query language is quite a narrow interpretation compared to something closer to the academic Prolog roots like https://souffle-lang.github.io/ - what do you feel are the most important differences?

Very cool! I love the sqlite install everywhere model.

Could you compare use case with Souffle? https://souffle-lang.github.io/

I'd suggest putting the link to the docs more prominently on the github page

Is the "traditional" datalog `path(x,z) :- edge(x,y), path(y,z).` syntax not pleasant to the modern eye? I've grown to rather like it. Or is there something that syntax can't do?

I've been building a Datalog shim layer in python to bridge across a couple different datalog systems https://github.com/philzook58/snakelog (including a datalog built on top of the python sqlite bindings), so I should look into including yours

TerminusDB CTO here.

Echoing what triska said, CLP(ℤ) and friends are some of the most under-appreciated aspects of prolog implementations.

I'm amazed that programmers still don't have access to CLP when trying to do scheduling and planning solutions.

As an example in practice, what if you want to know about a transaction in which a number of entities transitively had holdings in one of the beneficiaries of the transaction at that particular time. The date window is not known, and the date windows are important in the ownership chain as well as the transactions that are being undertaken.

With CLP(FD) you can ask for a window of time, and the solution will zoom in on an appropriate time window which exists for the entire chain and match the time of the transaction.

Now try to do this query in SQL. It's almost impossibly hard.

I can't wait until I have the time to implement constraint variables for TerminusDB, but at the minute we are still working on more prosaic features.

Aside from that there are very interesting program correctness and optimisation systems which are based on prolog (usually a datalog). For instance Soufflé: https://souffle-lang.github.io

No problem :) What do you mean by voice control systems? Prolog has a bit of a learning curve and it's very difficult to write efficient code in. Although it did inspire Erlang, which is used in telecom and has some pretty interesting advantages not offered by other languages (reliance, multithreading, and updating without shutting down) Prolog is also pretty procedural, (the order you declare clauses in really really matters). There are other languages that use a much more pure for of logic Datalog: https://en.wikipedia.org/wiki/Datalog https://souffle-lang.github.io/

This reminds me of a great blog post by Frank McSherry (Materialize, timely dataflow, etc) talking about how using the right tools on a laptop could beat out a bunch of these JVM distributed querying tools because... data locality basically.

https://github.com/frankmcsherry/blog/blob/master/posts/2015...

Oh, cool!

And yeah, licenses can be challenging and frustrating, especially the first time you release a major project.

I am really super excited by the idea of embedded Datalog in Rust. I run into a lot of situations where I need something that fits in that awkward gap between SQL and Prolog. I want more expressiveness, better composability, and better graph support than SQL. But I also want finite-sized results that I can materialize in bounded time.

There has been some very neat work with incrementally-updated Datalog in the Rust community. For example, I think Datafrog is really neat: https://github.com/frankmcsherry/blog/blob/master/posts/2018... But it's great to see more neat projects in this space, so thank you.

They used to but Frank McSherry (author of differential dataflow) wrote them a specialized version without all the dataflow infrastructure [1]. It's part of the rust-lang nursery [2] now but hasn't been updated in a while, so I'm not sure what happened to it.

[1] https://github.com/frankmcsherry/blog/blob/master/posts/2018...

[2] https://github.com/rust-lang/datafrog

Importantly, this doesn't just use memoization (it actually avoids having to spend memory on that), but rather uses operators (nodes in the dataflow graph) that directly work with `(time, data, delta)` tuples. The `time` is a general lattice, so fairly flexible (e.g. for expressing loop nesting/recursive computations, but also for handling multiple input sources with their own timestamps), and the `delta` type is between a (potentially commutative) semigroup (don't be confused, they use addition as the group operation) and an abelian group. E.g. collections that are iteratively refined in loops often need an abelian `delta` type, while monoids (semigroup + explicit zero element) allow for efficient append-only computations [0].

[0]: https://github.com/frankmcsherry/blog/blob/master/posts/2019...