Abstract
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 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
