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@thi.ng/rstream-query

@thi.ng/rstream-query

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This project is part of the @thi.ng/umbrella monorepo.

About

@thi.ng/rstream based triple store & reactive query engine with declarative query specs related to Datalog / SPARQL. Inserted triples / facts are broadcast to multiple indexing streams and any query subscriptions attached to them. This enables push-based, auto-updating query results, which are changing each time upstream transformations & filters have been triggered.

Triples are 3-tuples of [subject, predicate, object]. Unlike with traditional RDF triple stores, any JS data types can be used as subject, predicate or object (though support for such must be explicitly enabled & this feature is currently WIP).

Current features

  • Dynamic & declarative dataflow graph construction via high-level data specs and/or functions
  • Entirely based on stream abstractions provided by @thi.ng/rstream
  • All data transformations done using dynamically composed tranducers
  • Query optimizations
  • Extensive re-use of existing sub-query results (via subscriptions)
  • Interim result de-duplication / dataflow gates
  • Push-based, auto-updating query results

Status

ALPHA - This project is currently still in early development and intended as a continuation of the Clojure based thi.ng/fabric, this time built on the streaming primitives provided by @thi.ng/rstream.

Installation

yarn add @thi.ng/rstream-query

Dependencies

Usage examples

import { TripleStore, asTriples } from "@thi.ng/rstream-query";
import { trace } from "@thi.ng/rstream";

// create store with initial set of triples / facts
const store = new TripleStore([
    ["london", "type", "city"],
    ["london", "part-of", "uk"],
    ["portland", "type", "city"],
    ["portland", "partOf", "oregon"],
    ["portland", "partOf", "usa"],
    ["oregon", "type", "state"],
    ["usa", "type", "country"],
    ["uk", "type", "country"],
]);

// alternatively, convert an object into a sequence of triples
const store = new TripleStore(asTriples({
    london: {
        type: "city",
        partOf: "uk"
    },
    portland: {
        type: "city",
        partOf: ["oregon", "usa"]
    },
    oregon: { type: "state" },
    uk: { type: "country" },
    usa: { type: "country" },
});

// compile the below query spec into a dataflow graph
// pattern items prefixed w/ "?" are query variables

// this query matches the following relationships
// using all currently known triples in the store
// when matching triples are added or removed, the query
// result updates automatically...

// currently only "where" and bounded "path" sub-queries are possible
// in the near future, more query types will be supported
// (e.g. optional relationships, pre/post filters etc.)
store.addQueryFromSpec({
    q: [
        {
            // all "where" subqueries are joined (logical AND)
            where: [
                // match any subject of type "city"
                ["?city", "type", "city"],
                // match each ?city var's "part-of" relationships (if any)
                ["?city", "partOf", "?country"],
                // matched ?country var must have type = "country"
                ["?country", "type", "country"]
            ]
        }
    ],
    // `bind` is an (optional) query post-processor and
    // allows injection of new variables into the result set
    // here we create a new var "answer" whose values are derived from
    // the other two query vars
    bind: {
        answer: (res) => `${res.city} is located in ${res.country}`
    },
    // another post-processing step, only keeps "answer" var in results
    select: ["answer"]
})
.subscribe(trace("results"))
// results Set {
//   { answer: 'london is located in uk' },
//   { answer: 'portland is located in usa' } }

// helper fn to insert new city relationship to the store
const addCity = (name, country) =>
    store.into([
        [name, "type", "city"],
        [name, "partOf", country],
        [country, "type", "country"],
    ]);

addCity("berlin", "germany");
// results Set {
//     { answer: 'london is located in uk' },
//     { answer: 'portland is located in usa' },
//     { answer: 'berlin is located in germany' } }

addCity("paris", "france");
// results Set {
//     { answer: 'london is located in uk' },
//     { answer: 'portland is located in usa' },
//     { answer: 'berlin is located in germany' },
//     { answer: 'paris is located in france' } }

After setting up the above query and its internal transformations, the generated dataflow topology then looks as follows:

graphviz output

  • The blue nodes are TripleStore-internal index stream sources, emitting changes when new triples are added
  • The left set of red nodes are the sub-queries of the above where clause, responsible for joining the individual (S)ubject, (P)redicate and (O)bject sub-queries.
  • The results of these are then further joined (right red node) & transformed to produce the final solution set and post-process it

Btw. The diagram has been generated using @thi.ng/rstream-dot and can be recreated by calling store.toDot() (for the above example)

The source code for the above example is here

(Many) more features forthcoming...

Authors

  • Karsten Schmidt

License

© 2018 Karsten Schmidt // Apache Software License 2.0

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