An early look at the LDBC social network benchmark's business intelligence workload

Abstract

In this short paper, we provide an early look at the LDBC Social Network Benchmark's Business Intelligence (BI) workload which tests graph data management systems on a graph business analytics workload. Its queries involve complex aggregations and navigations (joins) that touch large data volumes, which is typical in BI workloads, yet they depend heavily on graph functionality such as connectivity tests and path finding. We outline the motivation for this new benchmark, which we derived from many interactions with the graph database industry and its users, and situate it in a scenario of social network analysis. The workload was designed by taking into account technical "chokepoints" identified by database system architects from academia and industry, which we also describe and map to the queries. We present reference implementations in openCypher, PGQL, SPARQL, and SQL, and preliminary results of SNB BI on a number of graph data management systems.

How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks

“…Based on the previous triplestores benchmarks and performance evaluations [2,5,8,10,11,14,16,18,24,27,30,31,33] the performance metrics for such comparisons can be categorized as:…”

“…Accordingly, various benchmarks [2,5,8,11,14,16,18,24,27,30,31,33] have been proposed to evaluate the querying performance of these triplestores. However, due to heterogeneity of RDF datasets and SPARQL queries, real-world applications often require customized deployments and experience different workloads when deployed in real environments [22].…”

“…(1) We identify key design features of SPARQL triplestore benchmarks based on our systematic survey on the state-of-the-art. (2) We provide a detailed comparative analysis of the queries and datasets of 11 representative triplestore querying benchmarks: Train Benchmark [30], FEASIBLE [24], WatDiv [2], DBpedia SPARQL Benchmark (DBPSB) [18], FishMark [5], Bowlogna [11], SP2Bench [11], Berlin SPARQL Benchmark (BSBM) [8], BioBench [33], LDBC Social Network Benchmark Business Intelligence workload (SNB-BI) [31], and LDBC SNB Interactive workload (SNB-INT) [14]. (3) We analyze real data and corresponding user queries of five real-world datasets -DBpedia, 1 Semantic Web Dog Food (SWDF), 2 NCBI Gene, 3 SIDER, 4 DrugBank 5 -and compare them with the selected triplestore benchmarks.…”

“…Bowlogna[11] models a real-world setting derived from the Bologna process and offers mostly analytic queries reflecting data-intensive user needs. The LDBC Social Network Benchmark (SNB) defines two workloads: (1) the Interactive workload (SNB-INT) measures the evaluation of graph patterns in a localized scope (e.g., in the neighborhood of a person), with the graph being continuously updated[14], and (2) the Business Intelligence workload (SNB-BI) focuses on queries that mix complex graph pattern matching with aggregations, touching on a significant portion of the graph[31], without any updates. Note that these two workloads are regarded as two separate triplestore benchmarks based on the same dataset.3.2 Triplestore Benchmarks Using Real DataFEASIBLE[24] is a cluster-based SPARQL benchmark generator, which is able to synthesize customizable benchmarks from the query logs of SPARQL endpoints.…”

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

“…This happens when the graph is being continuously updated [29]. LDBC also defines the Business Intelligence workload (SNB-BI), this workload cover a queries that mix aggregations with complex graph pattern matching with , covering a significant part of the graph [116], without any updates.…”