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Lookup NU author(s): Hugo Firth,
Professor Paolo Missier
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
As with general graph processing systems, partitioning data over a cluster of machines improves the scalability of graph database management systems. However, these systems will incur additional network cost during the execution of a query workload, due to inter- partition traversals. Workload-agnostic partitioning algorithms typically minimise the likelihood of any edge crossing partition boundaries. However, these partitioners are sub-optimal with re- spect to many workloads, especially queries, which may require more frequent traversal of speci c subsets of inter-partition edges. Furthermore, they largely unsuited to operating incrementally on dynamic, growing graphs. We present a new graph partitioning algorithm, Loom, that op- erates on a stream of graph updates and continuously allocates the new vertices and edges to partitions, taking into account a query workload of graph pattern expressions along with their relative frequencies. First we capture the most common patterns of edge traversals which occur when executing queries. We then compare sub-graphs, which present themselves incrementally in the graph update stream, against these common patterns. Finally we attempt to allocate each match to single partitions, reducing the number of inter-partition edges within frequently traversed sub-graphs and improving average query performance. Loom is extensively evaluated over several large test graphs with realistic query workloads and various orderings of the graph updates. We demonstrate that, given a workload, our prototype produces partitionings of signi cantly better quality than existing streaming graph partitioning algorithms Fennel & LDG.
Author(s): Firth H, Missier P, Aiston J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: EDBT: 21st International Conference on Extending Database Technology 2018 Conference
Year of Conference: 2018
Print publication date: 26/03/2018
Online publication date: 01/03/2018
Acceptance date: 22/11/2017
Date deposited: 22/11/2017