Wings: Efficient Online Multiple Graph Pattern Matching

Guanxian Jiang, Yunjian Zhao, Yichao Li, Zhi Liu, Tatiana Jin, Wanying Zheng, Boyang Li, James Cheng

Abstract: Finding query patterns in a graph is fundamental for graph data analytics. Existing works mostly focus on either finding a single query pattern or finding patterns in a static graph. However, many applications today need to match multiple query patterns against a dynamically changing graph, i.e., online multiple graph pattern matching (online multi-GPM). Online multi-GPM is challenging as it requires quick responses for timely business decision-making. This paper proposes Wings — a distributed system for online multi-GPM. The key to efficient multi-GPM is a query planner that optimizes query plans by maximizing computation sharing among multiple queries and minimizing intermediate matching results. In addition, we also design an efficient query executor for Wings with memory footprint control and runtime redundant processing elimination. Our experimental results verified that Wings' designs are efficient for online multi-GPM.