An Experimental Comparison of Pregel-like Graph Processing Systems∗

The introduction of Google’s Pregel generated much inter-est in the field of large-scale graph data processing, inspir-ing the development of Pregel-like systems such as Apache Giraph, GPS, Mizan, and GraphLab, all of which have ap-peared in the past two years. To gain an understanding of how Pregel-like systems perform, we conduct a study to ex-perimentally compare Giraph, GPS, Mizan, and GraphLab on equal ground by considering graph and algorithm agnos-tic optimizations and by using several metrics. The sys-tems are compared with four different algorithms (PageR-ank, single source shortest path, weakly connected compo-nents, and distributed minimum spanning tree) on up to 128 Amazon EC2 machines. We find that the system opti-mizations present in Giraph and GraphLab allow them to perform well. Our evaluation also shows Giraph 1.0.0’s con-siderable improvement since Giraph 0.1 and identifies areas of improvement for all systems. 1