GraphCache: A Caching System for Graph Queries

Wang, J., Ntarmos, N. and Triantafillou, P. (2017) GraphCache: A Caching System for Graph Queries. In: International Conference on Extending Database Technology (EDBT) 2017, Venice, Italy, 21-24 March 2017, pp. 13-24. ISBN 9783893180738 (doi:10.5441/002/edbt.2017.03)

Wang, J., Ntarmos, N. and Triantafillou, P. (2017) GraphCache: A Caching System for Graph Queries. In: International Conference on Extending Database Technology (EDBT) 2017, Venice, Italy, 21-24 March 2017, pp. 13-24. ISBN 9783893180738 (doi:10.5441/002/edbt.2017.03)

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Abstract

Graph query processing is essential for graph analytics, but can be very time-consuming as it entails the NP-Complete problem of subgraph isomorphism. Traditionally, caching plays a key role in expediting query processing. We thus put forth GraphCache (GC), the first full-edged caching system for general subgraph/supergraph queries. We contribute the overall system architecture and implementation of GC. We study a number of novel graph cache replacement policies and show that different policies win over different graph datasets and/or queries; we therefore contribute a novel hybrid graph replacement policy that is always the best or near-best performer. Moreover, we discover the related problem of cache pollution and propose a novel cache admission control mechanism to avoid cache pollution. Furthermore, we show that GC can be used as a front end, complementing any graph query processing method as a pluggable component. Currently, GC comes bundled with 3 top-performing filter-then-verify (FTV) subgraph query methods and 3 well-established direct subgraph-isomorphism (SI) algorithms - representing different categories of graph query processing research. Finally, we contribute a comprehensive performance evaluation of GC. We employ more than 6 million queries, generated using different workload generators, and executed against both real-world and synthetic graph datasets of different characteristics, quantifying the benefits and overheads, emphasizing the non-trivial lessons learned.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Triantafillou, Professor Peter and Ntarmos, Dr Nikolaos and Wang, Jing
Authors: Wang, J., Ntarmos, N., and Triantafillou, P.
College/School:College of Science and Engineering > School of Computing Science
ISSN:2367-2005
ISBN:9783893180738
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in International Conference on Extending Database Technology (EDBT) 2017
Publisher Policy:Reproduced under a Creative Commons License
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