[smarx007]

I think if someone is just trying out RDF, it is better to start with Apache Jena/Fuseki or Eclipse RDF4J. Maybe https://github.com/oxigraph/oxigraph if you like to live dangerously (i.e. to use pre-1.0 DBMSs).

Use of other systems involves factoring tradeoffs and considerations that are probably not the best for the newcomers. For example, qLever mentioned here is good in query performance and relative disk use but once the import is done, it's essentially a read-only DB and completely unsuitable for a typical OLTP scenario.

Having said that, the Chilean research group that is driving the development of MilleniumDB is very well-regarded in the RDF/semantic web querying space.

[FjordWarden]

If you expect Jena to be more battle-tested because it is older, forget it, if the process is killed by a unexpected shutdown or some other reason it results in data corruption. At least this was my experience a few years ago.

I found graph databases a beguiling idea when I first learned about them, and this is a welcome addition, but I've since temperated my excitement. They are not as flexible and universal a modal as is often promised. Everything is a graph, sure but the result of your SPARQL query not necessarily.

I found classical DBMS based on sets/multisets to be much easier to compose from a querying point of view. A table is a set/multiset and a result of a query is also a set/multiset, SPARQL guarantees no such composability. Maybe, if you want to start mucking around with inference engines, but you'll either run into problems of undecidability.

[PaulHoule]

Jena lets you make little in-memory triple stores that you can use the way people use the list-map-scalar trinity. I've been working on this publication about that (RDF for difficult cases and when ordering counts) for years and it just got published last week

https://www.iso.org/standard/76310.html

I'll call out my collabortor Liju Fan for being the only person I've met who knew how to do anything interesting with OWL. (Well, I can do interesting things now but I owe it all to her.)

(For the research for that paper I used rdflib under PyPi because CPython was not fast enough.)

When I needed big persistent triple stores (that you use the way you might use postgres) I used to use

https://en.wikipedia.org/wiki/Virtuoso_Universal_Server

and had pretty good luck if I loaded a billion triples if I used plenty of 'stabilizers' (create a new AWS instance with ample RAM, use scripts to load a billion triples starting from an empty database, shut it down, make an AMI, start a new instance with the AMI, expect it to warm up for 20 minutes or so before query performance is good)

I don't regularly build systems on SPARQL today because of problems with updating. In particular, SQL has an idea of a "record" which is a row in a table, document oriented databases have an idea of a "record" which is a bit more flexible. Updating a SPARQL database is a little bit dangerous because there is no intrinsic idea of what a record is; i mean, you can define one by starting at a particular URI and traversing to the right across blank nodes and saying it is a 'record' and it works OK. But it's a discipline that I impose on it with my libraries, it ought to be baked into standards, baked into the databases, wrapped up in transactions, etc. For anything OLTP-ish I am still using SQL or document-oriented databases, but I hate the lack of namespaces and similar affordances that make SPARQL scalable in terms of "smash together a bunch of data from different sources" in document-oriented databases wheras SPARQL is missing the affordances you have in document-oriented databases for handling ordered collections. We badly need a SPARQL 2 which makes the kind of work that I talk about in that technical report easy.

[smarx007]

> Updating a SPARQL database is a little bit dangerous because there is no intrinsic idea of what a record is

SPARQL has a notion of a transactional boundary just like SQL has. You can combine multiple SPARQL queries in one transaction, they will all succeed or all fail just like you'd expect.

[PaulHoule]

Sorta kinda.

Your code has to put the right things in a transaction all the time for transactions for transactions to work right. If there is some flow of information like

   application does query -> application thinks -> application does update

you have to wrap the whole sandwich in a transaction, people frequently don't do that. If I'm writing 20 of those for an application I want something that I know is bulletproof.

My experience with SQL is that the average SQL developer doesn't really understand how to do transactions right but their ass gets saved (in a probabilistic sense) by the grouping of updates that is implicit by running an INSERT or an UPDATE against a table.

There's also the fact that a lot of triple stores are seriously half baked research-quality code if that. Many triple stores struggle if you just try to load 100,000 triples sequentially, for an application like my YOShInOn RSS reader which I expect to use every day and not have to patch or maintain anything for 18+ months. (Ok, a 20GB database that needs to be pruned crept up on me gradually, but that's an arangodb problem, I'd expect the average triple to store to have crumbled 17 months ago.)

I'd love to have something that updates like a document-oriented database but lets you run a SPARQL query against the union of all the documents. Database experts though always seem to change the subject when it comes to having a graph algebra that lets you UNION 10 million graphs.

(For that matter, I sure as hell couldn't pitch any kind any kind of "boxes-and-lines" query tool [1] etc. that passed JSON documents/RDF graphs over the lines between the operators to the VCs and private equity people who were buying up query engines circa 2015 because they were hung up on the speed of columnar query engines... Despite the fact that the ones that pass relational rows over the lines require people who really aren't qualified to do so create analysis jobs that look like terrible hairballs because of all the joins they do.)

[1] Alteryx, KNIME

[smarx007]

> you have to wrap the whole sandwich in a transaction

True, SPARQL does not allow "opening" transactions such that you can run one query, do some logic, and run another query while doing commit. Which was a pain for me. RDF4J has a non-standard API to do that, I think they are trying to upstream it to SPARQL 1.2.

> There's also the fact that a lot of triple stores are seriously half baked research-quality code if that.

Also true. Although excellent researchers who wrote one of the best reasoners (Pellet) decided to leave academia and make a production grade system. They succeeded with Stardog but you don't want to know how much a license costs.

> couldn't pitch any kind any kind of "boxes-and-lines" query tool [1] etc. that passed JSON documents/RDF graphs

I really enjoy this talk from one of the creators of OWL [1]. There, he makes a point that OWL is unpopular not because it's too complex but because it's not advanced enough to solve real problems people care about (read: ready to pay money for). I think the case you described involves VCs having clarity on how to make money off one thing but not the other. I do think that the Semantic Web 3.0 (if we count Linked Data as a Semantic Web 2.0 aka Semantic Web Lite attempt) will need a better (appealing to business) case than the one presented in the 2001 SciAm paper.

[1]: https://videolectures.net/videos/eswc2011_hendler_work

[kendallgclark]

OWL ontologies making a big comeback as part of Knowledge Graph groundings for LLM outputs. And several SPARQL and RDF knowledge graph startups are VC-baked and thriving. The world is a big place.

[PaulHoule]

Personally I thought Stardog was trash, but if I'd had different requirements I might be happy with it.

The trouble w/ OWL as I see it (talked about in that TR) is that people don't really want "first order logic", but they want "first order logic + arithmetic" which is a nightmare that Kurt Godel warned you about. (That ISO 20022 which that TR is related to is about the financial domain which is all about arithmetic)

After Doug Lenat's death a lot of stuff came out that revealed the problems w/ Cyc, not least that even if you try to build something that is "knowledge based" it can't practically solve all the problems you want using a SMT-based strategy but you have to build a library of special purpose algorithms for everything you want to do and it turns out to be a godawful mess.

I'm disappointed that the semweb community hasn't made a serious crack at usable and efficient production rules (dealing w/ problems like negation, controlling execution order, RETE execution, retraction) instead we get half-answers like SPIN with fixed-point execution (used an even more half-baked version of that to research that TR, gets you somewhere). Of course, production rules never got standardized in any domain because nobody can agree on the way to address those four issues even though it usually isn't hard to find an answer that's fine for a particular application.

(It's a frequently problem that experts on a technology can get by on half-baked specific answers that would need a general solution if they were going to be useful for a general audience. One reason why parser generators are so bad is that if you understand parser generators enough to write a parser generator you aren't bothered by the terrible developer experience of parser generators.)

[svilen_dobrev]

datomic (and partially xtdb /former crux) are OLTPish, and use only such "tuples" , essentially it's up to the user to define what constitutes an entity if at all ("row", "object", "document", whatever) - maybe some entity-id and everything linked to it, but maybe other less-identity-related stuff. Which might feel freeing to extent, but as you said, also expects great responsibility/discipline to cobble the proper properties together.

[PaulHoule]

Mathematically the boundaries of a record can be defined by production rules

https://en.wikipedia.org/wiki/Business_rules_engine

which could be written as SPARQL queries, I've used these to cut records out of a big graph, I haven't thought seriously if these could be built into a large scale general purpose systems.

The most fun I ever had with Jena was when I used the rules engine for the control plane of a batch processing system which used stream processing primitives [1]

https://jena.apache.org/documentation/inference/

The Jena folks said my use was completely unsupported, I had looked at the source code and got to understand how the rules engine worked and I knew damn well there was nothing wrong with what I was doing.

I've thought a lot about why production rules have had so little impact on the industry, I mean people really hate drools

https://www.drools.org/

That kind of system is particularly strong at handling deep asynchrony, like when a business process at a bank might involve some steps where you might have to wait for a loan office to approve a loan. It's disappointing to me that nobody has tried to use them (so far as I can tell) to deal with the asynchronous comms problems in Javascript though I've yet to get a clear picture in my mind about how to get started on that. (Funny I am getting an idea now so I'm putting a ticket on my personal Kanban board)

[1] I worked later at a place that had a similar engine written in very awkward Scala that allegedly used Either and Optional for error handling but actually dropped errors most of the time; I knew what algebra my engine supported, they argued whether or not something like that had an algebra; my engine got the same answers every time because it tore down the system properly at the end, their engine gave different answers every time but they didn't seem to care

[smarx007]

I said suitable for newcomers aka people touching RDF for the first time. If you want production-ready, you probably want Stardog, Ontotext GraphDB, or AWS Neptune - neither is cheap. https://github.com/the-qa-company/qEndpoint is also an interesting project that's used in production.

[zozbot234]

> SPARQL guarantees no such composability.

SPARQL has a CONSTRUCT clause which gives you RDF as your query output. Isn't that compositional enough?

[FjordWarden]

Ok, that is true, but how do I tell my graph database that the result of the construct query is some other graph in my DB?

[smarx007]

> how do I tell my graph database that the result of the construct query

I am assuming you are asking how to do a CONSTRUCT query that will return you the contents of a given named graph?

https://www.w3.org/TR/sparql11-http-rdf-update/#http-get is a much simpler way to get a graph. As the spec says, it's equivalent to the following query

       CONSTRUCT { ?s ?p ?o } WHERE { GRAPH <graph_uri> { ?s ?p ?o } }

[UltraSane]

Property graphs map to relational databases pretty well. Using Neo4j's terminology

table name -> node label

table row -> node

table column -> node property

The result of a query is a sub-graph and is very composable.

[AtlasBarfed]

I think it maps much better to document databases.

Nodes are just documents.

You just need to slap on a relations document type for the graph edges, and to store edge properties

I was close to finishing at least version 1.0 of a document/graph database on top of Cassandra and dynamodb.

[UltraSane]

With Neo4j properties and list members cannot be complex objects just like DB table rows. I was thinking that my dream DB would be a hybrid of MongoDB documents with Neo4j type relationships between them.

[hobofan]

As someone that has built production systems with Oxigraph (and a bit less with Jena), I'd recommend Oxigraph over Jena any day. Especially if you have you are working with a Rust-based tech stack.

You can save so much time and headache based on less operational complexity and the architectural options it opens up. If you only reinvest part of that into building a framework for versioning/backups, etc. you'll have a much better overall package.

[Karrot_Kream]

Interesting. Do you think RDF is easier to work with in Rust's ecosystem than Java's ecosystem as a whole in your opinion? I've only touched Jena and worked with Java and Go systems with RDF.

[iddan]

Systems like Apache Jena are not production ready for anything serious. It makes total sense to start something different

[spothedog1]

Definitely do not start with Jena/Fuseki, pain in the ass to set up. Start with Oxigraph or rdflib in memory to play around with how to query/interact with the graphs