A Distributed Path Query Engine for Temporal Property Graphs

Abstract

Property graphs are a common form of linked data, with path queries used to traverse and explore them for enterprise transactions and mining. Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and properties and structure varying over time. These are seen in social, telecom and transit networks. However, current graph databases and query engines have limited support for temporal relations among graph entities, no support for time-varying entities and/or do not scale on distributed resources. We address this gap by extending a linear path query model over property graphs to include intuitive temporal predicates that operate over temporal graphs. We design a distributed execution model for these temporal path queries using the interval-centric computing model, and develop a novel cost model to select an efficient execution plan from several. We perform detailed experiments of our Granite distributed query engine using temporal property graphs as large as 52M vertices, 218M edges, and 118M properties, and a 800-query workload, derived from the LDBC benchmark. We offer sub-second query latencies in most cases, which is 154×-1786× faster compared to industry-leading Neo4J shared-memory graph database and the JanusGraph/Spark distributed graph query engine. Further, our cost model selects a query plan that is within 10% of the optimal execution time in 90% of the cases. We also scale well, and complete 100% of the query workload for all graphs, as opposed to only 32-90% completion by the baseline systems.