Enhancing recursive graph querying on RDBMS with data clustering approaches

Recursive queries are one of the main mechanisms in Relational Database Management Systems to process topology-aware, or graph-like, queries. However, existing works focus only on optimizing the recursive query statements and processing, disregarding the potential physical arrangements that might improve performance. In this work, we propose to use an approach based on adjacent-list storage to physically organize the graph-like data aiming at both reducing the recursive query time and the number of I/O operations. By using Clustered Tables, we tied the adjacency list in chunks for (i) storing both vertex and edge tables together in a Combined Tables approach; and (ii) reordering the edge table with the Edge Clustered Table approach using 20% and 80% of the total adjacency list size. The clustered approaches enabled a faster recursive query processing (up to 22%) and a reduction of up to 61% in the number of page accesses when compared to the Conventional approach. When starting from multiple vertices, the Combined Tables approach achieved a query reduction time of up to 50% in the first join operation, and Edge Clustered Table 20% provided an overall time reduction of up to 20%. The results show that our physical design is effective and allows one to use recursive queries without adaptations.