A General-Purpose Query-Centric Framework for Querying Big Graphs
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AbstractPioneered 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 open-source 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.
All Author(s) ListYan D, Cheng J, Ozsu MT, Yang F, Lu Y, Lui JCS, Zhang QZ, Ng W
Journal nameProceedings of the VLDB Endowment
Volume Number9
Issue Number7
Pages564 - 575
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems

Last updated on 2020-03-07 at 00:47