Sortledton: a Universal Graph Data Structure

Despite the wide adoption of graph processing across many different application domains, there is no underlying data structure that can serve a variety of graph workloads (analytics, traversals, and pattern matching) on dynamic graphs with single edge updates updates. In this paper, we present Sortledton, a universal graph data structure that addresses the open problem by carefully optimizing for the most relevant data access patterns used by graph computation kernels. It can support millions of updates per second, while providing competitive performance (1.22x on average) for the most common graph workloads to the best-known baseline for static graphs - csr. With this, we improve the ingestion throughput over stateof-the-art dynamic graph data structures, while supporting a wider range of graph computations, with a much simpler design and significantly smaller memory footprint (2.1x that of csr).