A General-Purpose Query-Centric Framework for Querying Big Graphs

Pioneered by Google’s Pregel, many distributed systems have been developed for large-scale graph analytics. These systems employ a user-friendly “think like a vertex” programming model, and exhibit good scalability for tasks where the majority of graph vertices participate in computation. However, the design of these systems can seriously under-utilize the resources in a cluster for processing light-workload graph queries, where only a small fraction of vertices need to be accessed. In this work, we develop a new opensource system, called Quegel, for querying big graphs. Quegel treats queries as first-class citizens in its design: users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand, using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performanc...

• Type: Journal
• Year: 2016
• Where: PVLDB
• Authors: Da Yan, James Cheng, M. Tamer Özsu, Fan Yang, Yi Lu, John C. S. Lui, Qizhen Zhang, Wilfred Ng
• Keywords: Cryptology, PVLDB 2016