Paper Info
Title
SPARQLGX: Efficient Distributed Evaluation of SPARQL with Apache Spark.
Abstract
SPARQL is the W3C standard query language for querying data expressed in the Resource Description Framework (RDF). The increasing amounts of rdf data available raise a major need and research interest in building efficient and scalable distributed sparql query evaluators. In this context, we propose SPARQLGX: our implementation of a distributed rdf datastore based on Apache Spark. SPARQLGX is designed to leverage existing Hadoop infrastructures for evaluating sparql queries. SPARQLGX relies on a translation of sparql queries into executable Spark code that adopts evaluation strategies according to (1) the storage method used and (2) statistics on data. We show that sparqlgx makes it possible to evaluate SPARQL queries on billions of triples distributed across multiple nodes, while providing attractive performance figures. We report on experiments which show how SPARQLGX compares to related state- of- the- art implementations and we show that our approach scales better than these systems in terms of supported dataset size. With its simple design, SPARQLGX represents an interesting alternative in several scenarios.
Year
DOI
Venue
2016
10.1007/978-3-319-46547-0_9
Lecture Notes in Computer Science
Keywords
Field
DocType
RDF system,Distributed SPARQL evaluation
Data mining,RDF query language,Spark (mathematics),Information retrieval,Computer science,SPARQL,Implementation,Named graph,RDF Schema,Database,RDF,Executable
Conference
Volume
ISSN
Citations 
9982
0302-9743
8
PageRank 
References 
Authors
0.53
15
4
Name
Order
Citations
PageRank
Damien Graux1104.28
Louis Jachiet2164.06
Pierre Genevès322525.61
Nabil Layaïda450147.84