Bridging the Gap between Relational OLTP and Graph-based OLAP

Recently, many applications have required the ability to perform dynamic graph analytical processing (GAP) tasks on the datasets generated by relational OLTP in real time. To meet the two key requirements of performance and freshness, this paper presents GART, an in-memory system that extends hybrid transactional/analytical processing (HTAP) systems to support GAP, resulting in hybrid transactional and graph analytical processing (HTGAP). GART fulfills two unique goals that are not encountered by HTAP systems. First, to adapt to rich workloads flexibility, GART proposes transparent data model conversion by graph extraction interfaces, which define rules for relational-graph mapping. Second, to ensure GAP performance, GART proposes an efficient dynamic graph storage with good locality that stems from key insights into HTGAP workloads, including (1) an efficient and mutable compressed sparse row (CSR) representation to guarantee the locality of edge scan, (2) a coarse-grained multi-version concurrency control (MVCC) scheme to reduce the temporal and spatial overhead of versioning, and (3) a flexible property storage to efficiently run different GAP workloads. Evaluations show that GART performs several orders of magnitude better than existing solutions in terms of freshness or performance. Meanwhile, for GAP workloads on the LDBC SNB dataset, GART outperforms the state-of-the-art general-purpose dynamic graph storage (i.e., LiveGraph) by up to 4.4×.