Performance Introspection of Graph Databases

Abstract

The explosion of graph data in social and biological networks, recommendation systems, provenance databases, etc. makes graph storage and processing of paramount importance. We present a performance introspection framework for graph databases, PIG, which provides both a toolset and methodology for understanding graph database performance. PIG consists of a hierarchical collection of benchmarks that compose to produce performance models; the models provide a way to illuminate the strengths and weaknesses of a particular implementation. The suite has three layers of benchmarks: primitive operations, composite access patterns, and graph algorithms. While the framework could be used to compare different graph database systems, its primary goal is to help explain the observed performance of a particular system. Such introspection allows one to evaluate the degree to which systems exploit their knowledge of graph access patterns. We present both the PIG methodology and infrastructure and then demonstrate its efficacy by analyzing the popular Neo4j and DEX graph databases.