souffle VS differential-datalog

> 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/

Hi,

> I wonder if it lives up to the hype.

We do think so! (disclaimer: I'm a co-founder at Feldera)

To give some more background: We are co-designing/trialing feldera with several industry/enterprise partners from different domains. Our core team also built differential datalog (https://github.com/vmware/differential-datalog) in the past. And while ddlog is used quite successfully in products today, we believe the many lessons we learned with ddlog will help us to build an even better continuous analytics platform. FYI our code is open-source at https://github.com/feldera/feldera if you'd like to try it out.

Also feel free to join our community slack channel (https://www.feldera.com/slack/) if you have more questions.

The relational model (and generally working at the level of sets/collections, instead of the level of individual values/objects) actually makes it easier to have this kind of incremental computation in a consistent way, I think.

There's a bunch of work being done on making relational systems work this way. Some interesting reading:

- https://www.scattered-thoughts.net/writing/an-opinionated-ma...

- https://materialize.com/ which is built on https://timelydataflow.github.io/differential-dataflow/, which has a lot of research behind it

- Which also can be a compilation target for Datalog: https://github.com/vmware/differential-datalog

- Some prototype work on building UI systems in exactly the way you describe using a relational approach: https://riffle.systems/essays/prelude/ (and HN discussion: https://news.ycombinator.com/item?id=30530120)

(There's a lot more too -- I have a hobby interest in this space, so I have a small collection of links)

Tutorial which I didn’t see linked in the README: https://github.com/vmware/differential-datalog/blob/master/d...

This is amazing!

Have you looked at differential-datalog? It's rust-based, maintained by VMWare, and has a very rich, well-typed Datalog language. differential-datalog is in-memory only right now, but could be ideal to integrate your graph as a datastore or disk spill cache.

https://github.com/vmware/differential-datalog

Sort of related, in my mind at least, is differential dataflow, e.g. https://github.com/vmware/differential-datalog

It’s fascinating to see so many different parties converging on Datalog for reactive apps & UI.

- There are several such talks at https://www.hytradboi.com/ (happening this Friday)

- Roam Research and its clones Athens, Logseq, use Datascript / ClojureScript https://github.com/tonsky/datascript

- differential-datalog isn’t an end-to-end system, but is highly optimized for quick reactivity https://github.com/vmware/differential-datalog

- Datalog UI is a Typescript port of some of differential-datalog’s ideas https://datalogui.dev/

Rust related https://github.com/vmware/differential-datalog

Actually, that makes me wonder whether or not differential datalog falls under that umbrella, and if it could be applied in the same way Compose is.