SociaLite: An Efficient Graph Query Language Based on Datalog

Abstract

With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently. Moreover, recursive aggregate functions make it possible to implement more graph algorithms that cannot be implemented in Datalog. We evaluated SociaLite by running nine graph algorithms in total; eight for social network analysis (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) and one for biological network analysis (Eulerian cycles). We use two real-life social graphs, LiveJournal and Last.fm, for the evaluation as well as one synthetic graph. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50 percent for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, with only 16 percent overhead for the largest benchmark, and 25 percent overhead for the worst case benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to perform network analysis easily and efficiently.

Foundations of Modern Query Languages for Graph Databases

“…Recently we have also witnessed proposals that combine user-defined functions into Datalog to obtain a graph query language more tailored for graph analytic tasks (see e.g. [Seo et al 2015]). …”

Efficient Datalog Rewriting for Query Answering in TGD Ontologies

“…For future work, we are working on identifying more concrete classes of Datalog rewritable TGDs, optimising our rewriting algorithm and implementation, and validating the approach in some practical applications. We are also interested in exploring parallel or distributed Datalog engines, such as SociaLite [35] and BigDatalog [36], for efficient query answering with our Datalog rewriting.…”

SchenQL: A Concept of a Domain-Specific Query Language on Bibliographic Metadata

“…More advanced enhancements of functionalities of SchenQL could include the calculation of the influence graph of publications, centrality of authors, the length of a shortest path between two authors and the introduction of aliases for finding co-authors or co-citations as proposed in [15]. Algorithms for social network analysis as PageRank, computation of mutual neighbours, hubs and authorities or connected components could be worthwhile [31]. As user-defined functions [1,31] and support in query formulation were well-received in other works [29], they are a further prospect.…”

“…Algorithms for social network analysis as PageRank, computation of mutual neighbours, hubs and authorities or connected components could be worthwhile [31]. As user-defined functions [1,31] and support in query formulation were well-received in other works [29], they are a further prospect. The incorporation of social relevance in the search and result representation process as shown in [2,14] would also be a possibility for extension.…”