A Hybrid System for Analyzing Very Large Graphs

Abstract

This paper presents a case study of the design of a hybrid SQL data storage combined with procedural programming language processing (HSPPL) system for the analysis of large graphs. The HSPPL system was evaluated against a system with SQL data storage combined with SQL language processing (SQL), and against a system with internal memory storage combined with procedural programming language processing (PPL). In one experiment, the three systems were used to perform a shortest path analysis on six test graphs which varied in size and density. The HSPPL system was significantly faster than the SQL system and was able to handle graphs larger than those that could be handled by the PPL system, but the HSPPL system was significantly slower than the PPL system. In a second experiment, the three systems were used to perform graph partitioning on four benchmark problems. The results of the partitioning produced by the three systems were not statistically different. The results suggest that an HSPPL system for analyzing large graphs is feasible and may be particularly useful in situations where a graph under analysis is too large to fit into host machine main memory.