Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/79512

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

Authors Yan, Da
Cheng, James
Özsu, M. Tamer
Yang, Fan
Lu, Yi
Lui, John
Zhang, Qizhen
Ng, Wilfred Siu Hung View this author's profile
Issue Date 2016
Source Proceedings of the VLDB Endowment , v. 9, (7), March 2016, p. 564-575
Summary 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 performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems. © 2016 VLDB Endowment.
Conference 42nd International Conference on Very Large Data Bases, New Delhi, India, 5-9 September 2016
Subjects
ISSN 2150-8097
Language English
Format Conference paper
Access View full-text via Scopus
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