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).
Verma et al. REF evaluates different partitioning strategies provided by distributed graph analytics systems, namely, PowerGraph, GraphX, and PowerLyra, and it suggests the best partitioning strategy for each system among the strategies provided by that system.
An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing
PVLDB
PVLDB
Computer Science