agnos-ai / reactive-sparql Edit

A Reactive Sparql Client written in Scala and Akka

Version Matrix


"A Reactive SPARQL Client for Scala and Akka"

This client uses akka-streams to do as much as possible asynchronously, with back pressure support around the HTTP connection towards the triple store. There are no blocking calls crossing process boundaries.

The older Spray HTTP client no longer supported, however it is still available as release v0.1.3.

The akka-streams APIs currently supports 3 flavours of flows:

Flavour #1: Run SPARQL

Use the SparqlQuery(stmt: String) or SparqlUpdate(stmt: String) case class and embed it in a SparqlRequest() to be passed to the flow. On the other end a SparqlResponse() pops out. Support for custom mappings is available, where the resulting values get marshaled to a custom domain object. This is however not mandatory, there is a default result mapper available that will return a standard result set model based on the application/sparql-results+json content type.

It is possible to use a single wrapper flow of Flow[SparqlRequest, SparqlResponse, _] to run both SparqlUpdate() and SparqlQuery() statements. There is an option to use specialised query and update flows as well.

The underlying implementation communicates with the triple store via the HTTP endpoints, as documented here for queries and updates.

Example #1: Run a simple Sparql query

/* Define domain case class and mappings */
object Person extends ResultMapper[Person] {
  override def map(qs: QuerySolution): Person = {
    Person(qs.uri("g").get, qs.string("c").get)
case class Person(id: URI, name: String) extends SparqlResult

/* Create a bespoke SparqlQuery with a mapping to a Person */
val mappingQuery2Get = SparqlQuery( """
  |SELECT ?g ?b ?c
  |FROM NAMED <urn:test:agnos:data>
  |WHERE {
  |  GRAPH ?g {
  |   <urn:test:whatever> ?b ?c
  |  }
  |}""", mapping = Person, reasoningEnabled = true)

/* Create the Flow and Probes */
val sparqlRequestFlowUnderTest = SparqlRequestFlowBuilder.sparqlRequestFlow(testServerEndpoint)
val (source, sink) = TestSource.probe[SparqlRequest]

/* Send the request to the stream and expect the result */
sink.expectNext(receiveTimeout) match {
  case SparqlResponse(_, true, results, None) =>
    val persons: Seq[Person] = results //the  mapped collection is returned
  case r@_ =>

Flavour #2: Construct Models

Working with Sparql query solutions (rows of result bindings as returned by a SELECT statement) is not always suitable. This is because the result is not plain RDF.

Use of SPARQL CONSTRUCTs is suitable in cases where we are only interested in triples (i.e. not quads, where the graph IRI is missing)

At the moment there is no way to write the following statement, so that the resulting RDF is returned in "quads" format (N-QUADS or JSON-LD)

  GRAPH ?g {
    ?s ?p ?o .

This flow has been created to circumvent the problem. It is an extension of the API used in Flavour #1.

Instead of a SparqlQuery() this flow works with a SparqlConstruct() inside the SparqlRequest()

object SparqlConstruct {
  def apply(resourceIRIs: Seq[URI] = Nil,
            propertyIRIs: Seq[URI] = Nil,
            graphIRIs: Seq[URI] = Nil,
            reasoningEnabled: Boolean = false)(
    implicit _paging: PagingParams = NoPaging
  ): SparqlConstruct = {

By specifying a set of matching resource, property and/or graph IRIs, we limit the number of results that are returned. Internally this flow will generate a reified SELECT statement that allows us to capture all 4 properties of the RDF Model, including the graph IRI.

The flow responds with a SparqlModelResult(model: Model) within a SparqlResult() which contains the RDF4J Model (Graph) instance.

case class SparqlModelResult(model: Model) extends SparqlResult

Refer to Flow[SparqlRequest, SparqlResponse, _] for more detail.

Flavour #3: Manipulate Graphs

This flow allows for basic graph manipulation, as defined by the graph-store protocol. Not all aspects of the protocol are supported, however it is possible to:

Retrieve Graphs

Retrieve an entire graph using GetGraph(graphUri: Option[URI]) wrapped in a GraphStoreRequest()

If no graphIri is specified the query returns the contents of the DEFAULT graph.

Drop Graphs

Drop an entire graph using DropGraph(graphUri: Option[URI]) wrapped in a GraphStoreRequest()

If no graphIri is specified the request drops the DEFAULT graph, so be careful with that if you don't use named graphs in your triple store.

Insert Models into Graphs

Insert the contents of an RDF Model into the specified graph. There are 3 variants:

  • InsertGraphFromModel(graphModel: Model, graphUri: Option[URI]): inserts an in-memory RDF Model;
  • InsertGraphFromPath(filePath: Path, graphUri: Option[URI], format: RDFFormat): inserts the contents of the specified file in the given RDF format;
  • InsertGraphFromURL(url: URL, format: RDFFormat, graphUri: Option[URI]): inserts the contents of the file behind the specified HTTP URL in the given RDF format.

All the operations above return a GraphStoreResponse which contains the success status of the operation and a optional model (for GetGraph() queries only)

case class GraphStoreResponse
  request: GraphStoreRequest,
  success: Boolean,
  statusCode: Int,
  statusText: String,
  model: Option[Model] = None

There is a mergeGraphs: Boolean parameter for all insert messages, that allows us to control how the resulting graph will deal with the newly inserted triples.

  • mergeGraphs = true will perform a HTTP PUT operation, which merges the content of the graph being sent with the graph that is already in the triple store;
  • mergeGraphs = false is the DEFAULT option and will perform a HTTP POST operation, which replaces the content of the graph with the one being sent over.

If no graph is specified, the insert will use the DEFAULT graph in the triple store.

Refer to Flow[GraphStoreRequest, GraphStoreResponse, _] for more detail.