Query notebook

SANSA uses vertical partitioning (VP) approach and is designed to support extensible partitioning of RDF data. Instead of dealing with a single three-column table (s, p, o), data is partitioned into multiple tables based on the used RDF predicates, RDF term types and literal datatypes. The first column of these tables is always a string representing the subject. The second column always represents the literal value as a Scala/Java datatype. Tables for storing literals with language tags have an additional third string column for the language tag.

Sparklify example

import org.apache.jena.riot.Lang
import net.sansa_stack.rdf.spark.io._
import net.sansa_stack.query.spark.query._

val input = "hdfs://namenode:8020/data/rdf.nt"
val lang = Lang.NTRIPLES

val triples = spark.rdf(lang)(input)
val sparqlQuery = """SELECT ?s ?p ?o
                            WHERE {?s ?p ?o }
                     LIMIT 10"""
val result = triples.sparql(sparqlQuery)
z.show(result)

Sparqlify Example with SPARQL Endpoint

import java.net.URI
import net.sansa_stack.rdf.spark.io.NTripleReader
import net.sansa_stack.rdf.spark.partition.core.RdfPartitionUtilsSpark
import net.sansa_stack.query.spark.sparqlify.QueryExecutionFactorySparqlifySpark
import net.sansa_stack.query.spark.sparqlify.SparqlifyUtils3
import org.aksw.jena_sparql_api.server.utils.FactoryBeanSparqlServer
import org.apache.spark.sql.SparkSession
import scala.collection.mutable

val input = "hdfs://namenode:8020/data/rdf.nt"
val graphRdd = NTripleReader.load(spark, URI.create(input))
    
val partitions = RdfPartitionUtilsSpark.partitionGraph(graphRdd)
val rewriter = SparqlifyUtils3.createSparqlSqlRewriter(spark, partitions)
    
val port = 7531  
val qef = new QueryExecutionFactorySparqlifySpark(spark, rewriter)
val server = FactoryBeanSparqlServer.newInstance.setSparqlServiceFactory(qef).setPort(port).create()
server.join()