DAUSK: A Transactional Graph Structure for Skewed Dynamic Graph Storage

Dynamic graphs are widely used in real-world applications and exhibit structural skew, which leads to skewed updates. Existing systems that support transactional updates often fail to fully consider this phenomenon, struggling to balance update and query performance. Inspired by the concepts of hierarchical thinking and uneven rebalancing on PMA, we propose DAUSK, a high-performance in-memory structure for dynamic graph storage that supports efficient graph analytics and rapid transactional updates. DAUSK addresses the skewed characteristics of graphs by employing two types of data structures: an unrolled skip list for frequently updated high-degree vertices, allowing asymptotically faster searches and updates, and a compact array-like structure called UPMA for medium and low-degree vertices, enabling efficient sequential scans. At the same time, DAUSK uses a vertex-centric strategy to partition UPMA and applies uneven gap allocation based on vertex degrees. Futhermore, considering the characteristics of transactions on graphs, DAUSK uses the well-established 2PL concurrency protocol to support millions of transactional updates per second. Experimental results demonstrate that DAUSK is up to 10.17×, 3.29× and 1.52× faster in ingesting graph updates compared to three state-of-the-art transactional graph systems, i.e., LiveGraph, Teseo and Sortledton. As for graph analytics, DAUSK achieves comparable, if not superior, performance compared to the other three graph systems.