Graph databases play a pivotal role in the FinTech industry. However, existing graph benchmarks fail to capture the unique characteristics of financial datasets and workloads, rendering them inadequate for evaluating graph databases in financial scenarios. This paper presents the LDBC Financial Benchmark (FinBench) Transaction Workload, a novel benchmark that adopts a choke point-driven design methodology, emphasizing performance bottlenecks, and incorporates distinct features such as dataset skewness, edge multiplicity, temporal window filtering, recursive path filtering, read-write query patterns, and truncation on hub vertices. Key contributions include a scalable data generator that synthesizes datasets with financial-specific features, a parameter generator that leverages bucketed data statistics for runtime consistency across queries, and a scalable benchmark driver that biases query execution by time windows. Experimental evaluations on graph databases demonstrate the benchmark's capability to reveal novel choke points and provide insights into system performance in financial scenarios.

We demonstrate the most important new feature of SQL:2023, namely SQL/PGQ, which eases querying graphs using SQL by introducing new syntax for pattern matching and (shortest) path-finding. We show how support for SQL/PGQ can be integrated into an RDBMS, specifically in the DuckDB system, using an extension module called DuckPGQ. As such, we also demonstrate the use of the DuckDB extensibility mechanism, which allows us to add new functions, data types, operators, optimizer rules, storage systems, and even parsers to DuckDB. We also describe the new data structures and algorithms that the DuckPGQ module is based on, and how they are injected into SQL plans. While the demonstrated DuckPGQ extension module is lean and efficient, we sketch a roadmap to (i) improve its performance through new algorithms (factorized and WCOJ) and better parallelism and (ii) extend its functionality to scenarios beyond SQL, e.g., building and analyzing Graph Neural Networks.

The Social Network Benchmark's Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC's "choke point"-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of "parameter curation" in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result - only audited results can use this trademarked term.

Triplestores are data management systems for storing and querying RDF data. Over recent years, various benchmarks have been proposed to assess the performance of triplestores across different performance measures. However, choosing the most suitable benchmark for evaluating triplestores in practical settings is not a trivial task. This is because triplestores experience varying workloads when deployed in real applications. We address the problem of determining an appropriate benchmark for a given real-life workload by providing a fine-grained comparative analysis of existing triplestore benchmarks. In particular, we analyze the data and queries provided with the existing triplestore benchmarks in addition to several real-world datasets. Furthermore, we measure the correlation between the query execution time and various SPARQL query features and rank those features based on their significance levels. Our experiments reveal several interesting insights about the design of such benchmarks. With this fine-grained evaluation, we aim to support the design and implementation of more diverse benchmarks. Application developers can use our result to analyze their data and queries and choose a data management system.

Graph processing challenges are common in modern database systems, with the property graph data model gaining widespread adoption. Due to the novelty of the field, graph databases and frameworks typically provide their own query language, such as Cypher for Neo4j, Gremlin for TinkerPop and GraphScript for SAP HANA. These languages often lack a formal background for their data model and semantics. To address this, the openCypher initiative aims to standardise a subset of the Cypher language, for which it currently provides grammar specification and a set of acceptance tests to allow vendors to implement their openCypher compatible engine.