Recently, graphs are becoming increasingly interesting in the context of large language models and as overlays for commercial databases. Subgraph query processing is an especially challenging workload for graph analysis that is bottlenecked by slow set intersection performance on CPUs. Previous work has shown the viability of utilizing hardware acceleration for related domains like graph and relational join processing. We propose GraphMatch, a hardware-accelerated subgraph query processing system based on worst-case optimal joins (WCOJ). For efficient processing of various data and query graphs, we propose a novel set intersection algorithm, called MaxStep, that leverages hardware parallelism. GraphMatch combines MaxStep operators in a data flow architecture which efficiently solves multi-set intersections in subgraph query processing, superior to CPU-based approaches. GraphMatch achieves an average speedup of over 6.98x and 17.08x, compared to the state-of-the-art WCOJ-based systems GraphFlow and RapidMatch, respectively. On labeled graphs, GraphMatch outperforms the fastest subgraph query processing accelerator FAST by orders of magnitude.

Array database systems offer a declarative language for array-based access on multidimensional data. This study explains the integration of ArrayQL inside a relational database system, either addressable through a separate query interface or integrated into SQL as user-defined functions. With a relational database system as the target, we inherit the benefits such as query optimisation and multi-version concurrency control by design. Apart from SQL, having another query language allows processing the data without extraction or transformation out of its relational form. This is possible as we work on a relational array representation, for which we translate each ArrayQL operator into relational algebra. In our evaluation, ArrayQL within Umbra computes matrix operations faster than state of the art database extensions.