souffle VS OPA (Open Policy Agent)

> 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/

How can we handle this? Are there any mechanisms to prevent or at least to some extent safeguard this kind of issues without falling back to a manual workflow? There is. One huge advantage of sticking to (de-facto) standards like Terraform is that first we are probably not the first ones to come up with this question and second there is a huge ecosystem around Terraform that might help us with such challenges. And for this specific scenario the solution is the Open Policy Agent. Let us take a closer look how the solution could look like.

A popular Policy-as-Code tool for Terraform is OPA, everyone's favorite versatile open-source policy engine that enforces security and compliance policies across your cloud-native stack, making it easier to manage and maintain consistent policy enforcement in complex, multi-service environments.

Harness Policy As Code uses Open Policy Agent (OPA) as the central service to store and enforce policies for the different entities and processes across the Harness platform. In this section, you will define a policy that will deny a pipeline execution if there is no approval step defined in a deployment stage.

OPA: While OPA is an open-source, general-purpose policy engine capable of enforcing unified and context-aware policies throughout the stack, it can also accept and output data in formats such as JSON, effectively functioning as a tool for generating or modifying configurations. Although it does not provide out-of-the-box schema definition support, it allows the integration of JsonSchema definitions.

In essence, container image signing involves adding a digital stamp to an image, affirming its authenticity. This digital assurance guarantees that the image is unchanged from creation to deployment. In this blog, I'll explain how to sign container images for Kubernetes using Cosign and the Open Policy Agent. I will also share a tutorial that demonstrates these concepts.

In this post we will explores two powerful solutions for addressing this issue: the Open Policy Language (OPA) and Google’s Zanzibar.

Rego is a declarative query language from the makers of the Open Policy Agent (OPA) framework. The Cloud Native Computing Foundation (CNCF) accepted OPA as an incubation-level hosted project in April 2019, and OPA graduated from incubating status in 2021.

Now there are more subdivided practice: * Policy as Code: Sentinel, OPA * Database as Code: bytebase * AppConfiguration as Code: KusionStack, Acorn * ...... (Welcome to add more)