GoFast: Graph-based optimization for efficient and scalable query evaluation

The popularity of the Resource Description Framework (RDF) and SPARQL has thrust the development of high-performance systems to manage data represented with this model. Former approaches adapted the well-established relational model applying its storage, query processing, and optimization strategies. However, the borrowed techniques from the relational model are not universally applicable in the RDF context. First, the schema-free nature of RDF induces intensive joins overheads. Also, optimization strategies trying to find the optimal join order rely on error-prone statistics unable to capture all the correlations among triples. Graph-based approaches keep the graph structure of RDF representing the data directly as a graph. Their execution model leans on graph exploration operators to find subgraph matches to a query. Even if they have shown to outperform relational-based systems in complex queries, they are barely scalable and optimization techniques are completely system dependent. Recently, some systems such as RDF_QDAG have shown that by combining graph exploration and triples clustering one can achieve a good compromise between performance and scalability. In this paper, we propose optimization strategies for this kind of RDF management systems. First, we define novel statistics collected for clusters of triples to better capture the dependencies found in the original graph. Second, we redefine an execution plan based on these logical structures which allows to represent the RDF graph exploration process. Third, we introduce an algorithm for selecting the optimal execution plan based on a customized cost model. Finally, we propose a new approach to refine the chosen plan by pruning invalid clusters that do not participate in the construction of the final query results. All our proposals are validated experimentally using well-known RDF benchmarks.