An experimental comparison of partitioning strategies in distributed graph processing

Shiv Verma, Luke M. Leslie, Yosub Shin, Indranil Gupta

Research output: Contribution to journalConference article


In this paper, we study the problem of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution environments. Through our experiments, we found that no single partitioning strategy is the best fit for all situations, and that the choice of partitioning strategy has a significant effect on resource usage and application run-time. Our experiments demonstrate that the choice of partitioning strategy depends on (1) the degree distribution of input graph, (2) the type and duration of the application, and (3) the cluster size. Based on our results, we present rules of thumb to help users pick the best partitioning strategy for their particular use cases. We present results from each system, as well as from all partitioning strategies implemented in one common system (PowerLyra).

Original languageEnglish (US)
Pages (from-to)493-504
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number5
StatePublished - Jan 1 2016
Event43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany
Duration: Aug 28 2017Sep 1 2017


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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