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Comment by j-pb | Hacker News Reader

Hey Martin,

I'm a huge fan of LogicBlox and RelationalAI, and the research done at both had a huge impact on me as an undergrad!

While you're here, could you provide some insight on why you folks went with arbitrary width tuples for your datamodel? Limiting youself to triples seems to drastically ease the implementation of WCOJ algorithms without much loss of expressivity, and provides compatibility with existing RDF research.

E.g. in our system we materialize the 3!(6) indices required, which allows us to pick variable/intersection orderings on the fly without a planner.

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Thanks, nice to hear!

With triples you need to reify hyper edges into nodes (a relation is just a hyper edge). This can lead to pretty awkward models, for example for temporal data. Most graph databases (eg Neo4J) have the same limitation and as a result struggle with temporal data or other higher-dimensional data.

We prefer to support general relations (hyper edges) and face the complications of query planning. We really want a general relational system that decouples the complications in query processing from the way you need to model your data and write your queries

This gets particularly important for what we do besides graphs: we also aim to support machine learning workloads (we have some references to papers on our website).

We still do automatic index selection, but indeed it is important that you do not create too many of these unnecessarily if you relations with arity bigger than triples.

Thanks for the insight! Having the best and brightest of the west coast database departments at your disposal allows for some different tradeoffs I guess ^^'!

I'm actually surprised that RelationalAI and Materialize Inc haven't merged yet, for ultimate world domination.

It's a shame though that both LogicBlox and RelationalAI historically only target large enterprise customers.

It forces us commoners that want to get out of the tarpit too to bolt relational operators to our existing languages. :D

Stay tuned for that :). RelationalAI is developed as a multi-tenant service that has a pay-as-you-go model. This should make it possible for everybody to use the product. I expect we will have some free tier options too, but we have not decided on the exact model for that yet. We are starting to onboard individual preview users already.

We're good friends with Materialize.io and use similar techniques for general incremental view maintenance, but their product is quite a bit different: their purpose is to design for SQL. RAI offers a different programming model to capture complex application and business logic (the language for that is called Rel, and docs are public).

Can't wait!

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