GoDB: From Batch Processing to Distributed Querying over Property Graphs

Abstract

Property Graphs with rich attributes over vertices and edges are becoming common. Querying and mining such linked Big Data is important for knowledge discovery and mining. Distributed graph platforms like Pregel focus on batch execution on commodity clusters. But exploratory analytics requires platforms that are both responsive and scalable. We propose Graph-oriented Database (GoDB), a distributed graph database that supports declarative queries over large property graphs. GoDB builds upon our GoFFish subgraph-centric batch processing platform, leveraging its scalability while using execution heuristics to offer responsiveness. The GoDB declarative query model supports vertex, edge, path and reachability queries, and this is translated to a distributed execution plan on GoFFish. We also propose a novel cost model to choose a query plan that minimizes the execution latency. We evaluate GoDB deployed on the Azure IaaS Cloud, over real-world property graphs and for a diverse workload of 500 queries. These show that the cost model selects the optimal execution plan at least 80% of the time, and helps GoDB weakly scale with the graph size. A comparative study with Titan, a leading open-source graph database, shows that we complete all queries, each in &x2264; 1:6 secs, while Titan cannot complete up to 42% of some query workloads. � 2016 IEEE.