Cutting-edge Relational Graph Data Management with Edge-k: From One to Multiple Edges in the Same Row

Abstract

Relational Database Management Systems (RDBMSs) are widely employed in several applications, including those that deal with data modeled as graphs. Existing solutions store every edge in a distinct row in the edge table, however, for most cases, such modeling does not provide adequate performance. In this work, we propose Edge-k, a technique to group the vertex neighborhood into a reduced number of rows in a table through additional columns that stores up to k edges per row. The technique provides a better table organization and reduces both table size and query processing time. We evaluate Edge-k table management for insert, update, delete and bulkload operations, and compare the query processing performance both with the conventional edge table — adopted by the existing frameworks — and with the Neo4j graph database. Experiments using Single-Source Shortest Path (SSSP) queries reveal that our new proposal approach always outperforms the conventional edge table as well as it was faster than Neo4j for the first iterations, being slightly slower than Neo4j only for iterations after having loaded the whole graph from disk to memory. It was able to reach a speedup of 66% over a representative real dataset, with an average reduction of up to 58% in our tests. The average speedup over synthetic datasets was up to 54%. Edge-k was also the best one when performing graph degree distribution queries. Moreover, the Edge-k table obtained a processing time reduction of 70% for bulkload operations, despite having an overhead of 50% for individual insert, update and delete operations. Finally, Edge-k advances the state of the art for graph data management within relational database systems.