With the proliferation of large irregular sparse relational datasets, new
storage and analysis platforms have arisen to fill gaps in performance and
capability left by conventional approaches built on traditional database
technologies and query languages. Many of these platforms apply graph
structures and analysis techniques to enable users to ingest, update, query and
compute on the topological structure of these relationships represented as
set(s) of edges between set(s) of vertices. To store and process Facebook-scale
datasets, they must be able to support data sources with billions of edges,
update rates of millions of updates per second, and complex analysis kernels.
These platforms must provide intuitive interfaces that enable graph experts and
novice programmers to write implementations of common graph algorithms. In this
paper, we explore a variety of graph analysis and storage platforms. We compare
their capabil- ities, interfaces, and performance by implementing and computing
a set of real-world graph algorithms on synthetic graphs with up to 256 million
edges. In the spirit of full disclosure, several authors are affiliated with
the development of STINGER.Comment: WSSSPE13, 4 Pages, 18 Pages with Appendix, 25 figure