Making RDBMSs Efficient on Graph Workloads Through Predefined Joins

Abstract

Joins in native graph database management systems (GDBMSs) are predefined to the system as edges, which are indexed in adjacency list indices and serve as pointers. This contrasts with and can be more performant than value-based joins in RDBMSs and has lead researchers to investigate ways to integrate predefined joins directly into RDBMSs. Existing approaches adopt a strict separation of graph and relational data and processors, where a graph-specific processor uses left-deep and index nested loop joins for a subset of joins. This may be suboptimal, and may lead to non-sequential scans of data in some queries. We propose a purely relational approach to integrate predefined joins in columnar RDBMSs that uses row IDs (RIDs) of tuples as pointers. Users can predefine equality joins between any two tables, which leads to materializing RIDs in extended tables and optionally in RID indices. Instead of using the RID index to perform the join directly, we use it primarily in hash joins to generate semi-join filters that can be passed to scans using sideways information passing, ensuring sequential scans. In some settings, we also use RID indices to reduce the number of joins in query plans. Our approach does not introduce any graph-specific system components, can execute predefined joins on any join plan, and can improve performance on any workload that contains equality joins that can be predefined. We integrated our approach to DuckDB and call the resulting system {\em GRainDB}. We demonstrate that GRainDB far improves the performance of DuckDB on relational and graph workloads with large many-to-many joins, making it competitive with a state-of-the-art GDBMS, and incurs no major overheads otherwise.